Autonomous Systems Enabling Technologies Research Issues Implications
David J. Atkinson, Ph.D Senior!Research!Scientist Tutorial Sponsored by Office of Chief Scientist, AFRL/RH Dayton, OH
17 November 2011
Introduction We are working on two major projects: 1. Give machines ever greater intelligence and autonomy 2. Maintain control of those machines It is far easier to design intelligent machines than it is to prove that they will always operate safely and as we intend What we seek is new kind of relationship with machines that allows for coordination and smooth transfer of control back and forth between humans and machines as needed => Nobody knows yet how to do that really well
Objective • Familiarize non-expert with those aspects of autonomous systems technology which may have implications for: –
Human-Machine interface
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Appropriate reliance
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Potential applications
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Concepts of operations
• Lots of discussion please – let's find the right questions to ask!
Two Parts Part I: (Today) - Introduction and Basic Concepts - Autonomous Agents - Agent Architectures - Knowledge, Reasoning and Decision-making
Part II: (January) - Planning, Task Execution and Control - Learning and Adaptation - Human-Machine Interaction Modes - Robotic Systems - Military Applications
The Basics Today Autonomous Systems
Automation
Agents
Artificial Intelligence
Intelligent Systems
Robotics
Artificial Intelligence Artificial Intelligence is the ability of a system to act appropriately in an uncertain environment, ...where an appropriate action is that which increases the probability of success, … and success is the achievement of behavioral subgoals that support the system’s ultimate goal.
Intelligent System • Intelligent System – An application of AI to a particular problem domain – You interact with them every day! • Credit cards; help centers; traffic control; factory automation; airport security screening … • Usually very specialized -- not “general intelligence”
• State-of-the-art: – Not as broadly competent as people and lack common sense – In some domains machine intelligence equals all but the most skilled humans; in a few areas they excel above all – Taking on tasks once thought only do-able by humans – Accomplishing tasks no human can perform without their help – Their complexity makes it nearly impossible for anyone but an expert to understand them, and that is becoming increasingly difficult as intelligent systems gain the ability to learn
Robotics & Automation (IEEE) Robotics: Robotics focuses on systems incorporating sensors and actuators that operate autonomously or semiautonomously in cooperation with humans. Robotics research emphasizes intelligence and adaptability to cope with unstructured environments. Automation: Automation emphasizes efficiency, productivity, quality, and reliability, focusing on systems that operate without direct control, often in structured environments over extended periods, and on the explicit structuring of such environments.
Agent • • • •
Self-activating, self-sufficient and persistent May be an intelligent system May include significant automation Is capable of modifying the manner in which it achieves objectives (fulfills purpose) • May reside and act entirely in the cyber world, or be embodied in a device such as a robot Agents may or may not be autonomous
Autonomous Systems Autonomy The ability of an intelligent system to independently compose and select among different courses of action to accomplish goals based on its knowledge and understanding of the world, itself, and the situation Autonomous systems are agents
Part of a spectrum of “levels of control” multi-dimensional concept •
Controlled systems: –
•
Supervised systems: –
•
machines do precisely as instructed
Automatic systems: –
•
human has full or partial control
carry out fixed functions without intervention
Autonomous systems: –
make independent decisions on what to do
The Basics Today Autonomous Systems
Automation
Agents
Artificial Intelligence
Intelligent Systems
Robotics
The Basics “Tomorrow” Automation
Autonomous Artificial Robotics Intelligence Systems Intelligent Systems
Future Forecast Autonomous systems are becoming “cognitive entities” - They have goals that we give them and goals of their own - The have beliefs about themselves, the world, and other entities - They sense and interpret the world, reason in many different ways about their beliefs, and act purposefully to achieve goals - They learn and adapt - They interact with humans, perform significant actions in the world, communicate in a variety of ways, and are beginning to act socially
This future is what we are talking about today
2. Autonomous Agents
David J. Atkinson, Ph.D Senior!Research!Scientist
Autonomous Systems are Agents • Agent – from the Latin “agere” (to do) – An agreement to act on one's behalf – Implies an authority to decide when, and if, action is appropriate
• Autonomous Agent – Capable of modifying the manner in which they achieve their objectives (purpose) – Is an intelligent system – May include significant automation – May reside and act entirely in the cyber world, or be embodied in a device such as a robot
Perspectives • Systems theory: – Agents are independent, situated, self contained systems with reflective and reflexive capacities (i.e. “self-awareness”)
• Software system: – Agents are extensions of objects (as in OOP) that can select their methods to be executed in response to inputs in order to achieve some goals
• Behavior-centric: – An agent is a software module that is capable of exhibiting reactive, proactive and social behavior while using communication inputs and outputs
Perspectives (cont.) • Robotics: – Computational entity that perceives its environment through sensors and act upon its environment through effectors – An agent make decisions on what actions to take flexibly and rationally in a variety of environmental circumstances, given the information it has and its perceptual and effectual abilities.
All the perspectives are good, but this one is best for our purposes today
Software Agents An abstraction that provides a convenient and powerful way to describe a complex software entity that is capable of acting with a certain degree of autonomy to accomplish tasks. First described in early '70s by Hewitt (Actor Model) as a mathematical model for concurrent computation - led directly to invention of object-oriented programming
Computation with software agents is not a determinant sequence of global states as in conventional software - better understood as an indeterminate partial ordering of events at the level of individual software agents
Software Agents • Key Characteristics Persistent:
Running continuously, monitoring context
Self-Activating: Recognize when they are needed, not invoked (reactive) Independent:
Do not require user interaction
Authoritative: May invoke tasks, communicate, as well as call upon other agents
Software Agents Examples • Search and Acquire: – Agent searches networked resources, maybe even physically moving from one machine to another, to locate and acquire something (copy, describe, purchase, …)
• Personal Agents: – Perform routine tasks – Assemble and present information of potential interest – Monitor and manage personal information (sync, privacy, …)
• Monitoring Agents – Continuous observation and reporting (quantity, activity, transactional patterns, relationships)
• Analysis Agents – Continuous search fo,r and analysis of, patterns in vast amounts of information (classification, clustering)
Cyber-physical Agents Like software agents, cyber-physical agents represent a move towards distributed control - Tighter coupling between computation and physical resources - Cyber-physical systems have greater independence and authority over physical devices
Cyber-Physical Agents • Same key characteristics as software agents –
Persistent, Self-Activating, Independent, Authoritative
• Key difference from software agents: –
Sense and effect change in the physical world, not cyberspace
• The core software architecture of a cyber-physical agent is nearly identical with a software agent –
Additional of layers for physical sensing and control
Controllers vs. Agents Attributes shared with controllers – Able to make sense of environment (inputs) – Able to decide what to do next – Able to “do stuff” to change environment (outputs)
Key differences from controllers – – – – – –
Degree of independent action Complexity of environmental input and uncertainty Decision process (not a control law) Goal-directedness (teleological, not sequential) Internal state complexity Delayed and/or contingent actions
Cyber-Physical Agents: Types Robots with high degrees of autonomy Important sub-types: – Mobile USV, UGV, UAV …
– Stationary Factory assembly, surgical assistant, sentry
– Bio-mimetic Inspired by, and may replicate the form and function of living things: (humanoid, animal, insectoid)
– Exoskeletons Upper, lower, whole body, individual limbs
Next-gen “embedded systems” Cyber-physical systems conceptually replace notion of embedded systems Emphasis is on networking, cooperation, and coordinated interaction among physical elements of a system, not just stand-alone computation in a device device Fulfill system function to augment or substitute for human control which would otherwise be too complex, too fast, etc. - Aerospace IVHM system
“Smart” Controllers - Manufacturing factory control, Smart Home
Human prosthetic / orthotic - Implanted devices for memory, cognitive function, vision
** Extends our notion of what is a “robot”
Multi-Agent Systems • Multi-agent systems involve large scale cooperation between agents to achieve their collective objectives • Cooperation may be explicit or emergent – Explicit:
Planned and executed at the agent level Planned at higher system level => Requires inter-agent communication
Example: Any kind of work team!
– Emergent:
Cooperation results as a consequence of local acts of individual agents; specialized behaviors triggered by changes in the environment => Inter-agent communications are not required but may be present.
Example: Ant colony
Weak vs. Strong Weak Notion of Agents
Strong Notion of Agents
Function w/o intervention Proactive: goal-directed behavior Reactive: perceive and respond to changing environment “Social”: Communicate with other agents to get work done
In addition to “Weak” factors:
Knowledge Beliefs Reasoning Intention
Multi-Agent Systems • Multi-agent systems may be localized or distributed – Localized:
Within reasonably close physical proximity. More likely to see emergent behavior.
Example: Swarm of robots for construction tasks, crowd simulations
– Distributed:
Arbitrary physical distance. Communications is a greater concern. Less likely to see emergent behavior
Example: Wide-area UAV surveillance or mapping
• Agents in cooperative multi-agent systems may be identical, similar, or vastly different in form and function – Possible to have mixtures of software / cyber-physical, and variations in number of types and their distribution
Multiple Agents In most cases, a single agent is insufficient Multiple agent systems are the norm As a system, multiple agents support: Natural decentralization (“there is no top”) Multiple loci of control (“Say go / Say no”) Multiple perspectives (task and context sensitive) Competing interests (resolved in context sensitive fashion)
Agent Interaction Interaction between agents is inevitable - to achieve individual objectives, to manage inter- dependencies Conceptualized as taking place at knowledge-level - which goals, at what time, by whom, what for
Flexible run-time initiation and responses - cf. design-time, hard-wired nature of extant approaches
paradigm shift from previous perceptions of computational interaction
Types of Agents in Multi-Agent Systems
Most intelligent agents are multi-agent systems and include (and/or share) many types of sub-agents:
Decision agents (implement specific decision process) Input agents (process and make sense of sensor data) Special processing agents (specialized algorithms) Believable agents (provide artificial personality for HCI) Physical agents (handle execution of actions in world) Temporal agents (scheduling and time constraints) … many more types
Agent System Paradigm Many types of agent-agent relationships are possible Agent Agen t
AGENT Agent Agen Agen t t Agen t
Agent Interaction
Monolithic vs. Agent Systems Complex System
Agent-based System
Subsystems
Agent-based organizations
Components
Agents
Interfaces between subsystems and components
Cooperating to achieve common objectives Coordinating action Negotiating to resolve conflicts
Relations between subsystems and components - Treated as single unit
Explicit mechanisms for managing relationships Structures for modeling collectives
Implications for Systems Software agents - are supplanting object-oriented systems as a software engineering paradigm - will accelerate the trend towards decentralization of control - new properties of complex systems will emerge from the aggregate behavior of many thousands of agents, each acting to achieve their purpose - will help systems be more robust - will make harder to understand and predict at low level - proving “correctness” will become even more challenging; traditional methods of software system test, evaluation, verification and validation are already struggling to keep up
Implications (cont.) Cyber-physical agents - change notion of control from “in the loop” to “on the loop” or even “outside the loop” - command is by goals and objectives, not by individual actions - are capable of independent action to achieve goals despite obstacles, and thus harder to predict - require higher level of “intelligent” interaction to coordinate with other agents, including humans - with tighter link between computational and physical elements of a system, are expected to greatly increase • • • • • •
Adaptability Efficiency Functionality Reliability Safety Usability
Take Away Thoughts Agents are a methodology for implementing very complex systems as much as an approach to creating autonomous systems Agents may have high degree of autonomy There are many different types, and they vary on many dimensions: difficult to make comparisons Agents will change the nature of work at least as much as automation.
3. Agent Architecture
David J. Atkinson, Ph.D Senior!Research!Scientist
Overview What is an agent architecture and why is it important Classical “Sense-Plan-Act” Challenges for agent architectures Examples – – – –
Behavioral & Reactive Control Hierarchical, Layered Cognitive Belief-Desire-Intention (BDI)
Dimensions of agent architectures On-Going Challenges
Architecture • The architecture of a system specifies how the elements which comprise the system are connected, related, and act together to perform the functions of the system as a whole. – – – – – –
A conceptual abstraction Policies, principles and guidelines Structural element descriptions Components, protocols and interface specifications Functional and behavioral specification Operational concepts
• Architectures can be for individual systems, or a system of systems –
An agent can be a collection of agents
Why is this interesting?
Understanding the variety of agent architectures helps to clarify concepts that may often be confusing
Different architectures support different sets of capabilities, states, processes, actions The objectives driving the creation of architectures, whether for research or application, can be very different
There are clusters of related architectures which share attributes, related capabilities and shortcomings There is no widely accepted over-arching theory which explains the range of possible architectures for intelligent agents
Why Agent Architecture Is Hard
As we expand the degree of independent, local control in a software- or cyber-physical agent, we are also asking it to cope with a much more complicated operating environment: - Dynamic (changing) - Sensor (input) noise and uncertainty - Unexpected events - Dynamic constraints (especially for cyber-physical) - High-level complex goals
“Designing out” these issues devolves an agent into traditional software, with resulting loss of advantages We just have to deal with them!
Classic Agent: Sense-Plan-Act Sense:
How continuous data from the world is segmented into meaningful units
Plan:
Deciding what to do next based on inputs and the internal model of the world
Act:
Translating a decision into an executable action that makes a change in the “world” (either cyber or physical)
Sense–Plan–Act
Sensing flows into world model, used by planner, and plan is executed without directly using the sensors that created the model Worked fine for simple applications in the lab but broke immediately in the real world: –
–
–
As complexity of world increases, the time for deliberative reasoning (planning) can go up exponentially (“state space explosion”) While the agent is planning, the world continues to change; what was true at the beginning of planning may not be true at the end Result: Broken plans that cannot be executed
==> Conclusion: Simple “top-down” approach is not sufficient
Some Architectural Challenges
Continuous perception during deliberative reasoning Integration of low-level control loops with high-level reasoning Loss of fidelity of information about world as it is transformed from raw inputs into symbols that can be used in reasoning “Anytime” plans that are “good enough”, because sometimes the agent must act Managing uncertainty because perceptions are not exact and actions may not have the desired effects on the world
What to Look For Most important ingredients of an agent architecture: 1. How it represents or models “the world” - What it can observe - What it can infer - What is knows about its own internal state - Its own experience and capabilities - “Relevant” relationships, interactions and implications
2. How it reasons about the world - Methods of inference - Goals, and how to achieve them - Management of uncertainty and change
Example Architectures
Behavioral and reactive architectures Hierarchical, layered architecture “Cognitive” architectures Belief-Desire-Intention (BDI) Architectures Hybrids
Behavioral & Reactive Architectures Behavioral architectures are based on the idea of reflexive action – – –
Lots of simple senors continuously and concurrently monitoring the world Specific values trigger actions More direct route from sensing to action
Very fast: In most extreme case, no deliberation! – –
“Stateless control”: There is no model of the world Great for simple reflex action
Problems: –
Messy; hard to create organized behaviors
Simple Reflex Agent
Subsumption Architecture (Brooks, 1986)
Subsumption architecture is today the dominant approach to reactive control in agents (primarily robotics) The intelligent agent is a collection of finite state machines organized into a hierarchy of layers of control - Some modules in higher levels can “subsume” (override) the inputs and outputs of lower level modules via arbitration mechanism
Can achieve surprisingly complex behaviors that emerge as a result of simple actions: - Strongly resembles insect behavior (e.g., “foraging”)
Reactive control is a very common feature of the layers of an autonomous agent that must act very quickly in response to environmental conditions
Subsumption Architecture No internal representation of world! Created by analysis of tasks Behaviors “subsumed” by higher level behaviors to exert control Tight coupling: sensing and action Behaviors (layers of FSA) organized into modules that implement major functions, modules in more layers Best known application: iRobot's Roomba Problems of behavior-based control: Avoiding simultaneous actions that conflict Sequencing actions for complex tasks Long range goals Optimization
Simple Model-Based Reflex Agent
Hierarchical Layered Architectures Hierarchical, Layered Architectures Addresses the challenge of integrating low-level continuously reactive perception-action loops with high-level symbolic reasoning – –
Typically two, three or more layers Researchers often mean different things by “layer”!
Most popular variant is a three-layered architecture:
Controller or Reactive layer Sequencer or Executive Layer Planner or Deliberative
Cognitive Architectures
A multi-layered agent architecture Explicit goal is to create computational processes that can be compared in detail to natural cognitive systems (human, animal, insect) Acts intelligently (under some definition) Unlike cognitive models, they attempt to implement cognition as a whole rather than specific competencies
Three basic types: Symbolic, Connectionist, Hybrid
Well known: Soar, ACT-R (both symbolic)
Soar (Laird,Newell & Rosenbloom, 1987) Continuous development since 1980's Procedural knowledge takes the form of production rules (condition/action) Episodic and Semantic knowledge Entirely goal-driven Attempts to transform a problem state into one which matches the goal Dynamically creates goal hierarchies Multiple learning mechanisms Many applications: TAC-Air-Soar (Tambe et. al., 1995) modeled fighter pilots in air combat training Recently used very successfully in a urban recon robotics challenge involving multiple cooperating autonomous robots (MAGIC 2010) **Simplified Classic Soar Architecture
http://sitemaker.umich.edu/soar/home
Belief-Desire-Intention (BDI)
BDI architecture focuses on the rapid selection and execution of procedures to apply to a situation from an existing library.
There is no automated planning Selection process is a type of means-ends analysis Not a complete architecture for autonomous agents
Procedure selection and procedure execution are separate and occur concurrently Deliberation involves deciding how much time to spend looking for the right procedures and when to start/stop execution of procedures based on the situation. Key advantage is that BDI agents are amenable to formal logic analysis of their behavior, singly or in multiplicity. Example: Procedural Reasoning System (PRS)
Procedural Reasoning System
(Georgeff & Lansky, 1987)
PRS/dMars Developed for real-time embedded systems Procedural knowledge is pre-compiled into sequences of low-level actions to achieve a goal Main deliberative cycle is about rapidly choosing which “knowledge area” to apply Reasoning process is based on first order predicate calculus; not suitable for learning systems Applied by NASA to monitoring and diagnosis of the Shuttle reaction control systems More recently, applied for real-time telecom network management Also: air-traffic control, tech support callcenters, air combat modeling
PRS/dMars PRS architecture motivations In general, an agent cannot achieve all its desires. Must therefore fix upon a subset. Commit resources to achieving them. Chosen desires are intentions. Agents continue to try to achieve intentions until either believe intention is satisfied, or believe intention is no longer achievable.
PRS Plans BDI model is operationalized in PRS agents by plans. Plans are recipes for courses of action. Each plan contains: - invocation condition: circumstances for plan consideration; - context: circumstances for successful plan execution; - maintenance condition: must be true while plan is executing, in order for it to succeed; and - body: course of action, consisting of both goals and actions.
Many different kinds! There are many dimensions upon which agent architectures can vary and be compared. Only a few have been explored in depth. Fewer have been implemented and matured enough for applications.
A Few Architectural Dimensions
Pipelined vs. concurrently active layers Dominance vs. functional differentiation Direct control vs. trainability Processing mechanisms vs. processing functions Varieties of representation Varieties of learning Motives for action and arbitration mechanisms Focused vs. wide perception Pathways for action Specialized capability components vs. emergence Programmed vs. self-bootstrapped ontologies
On-going Challenges Appropriate representations Managing uncertainty and loss of fidelity over levels Reasoning about uncertainty Real-time performance Detecting and recovering from failures Interaction with other agents, including humans
Very Different Architectures! Conventional - Centralized - Top-down Control - Sequential operations
Autonomous Agent - Distributed - Autonomous - Concurrent
Take Away Thoughts The architecture of an agent defines the overall strategy it follows for sensing the world, deciding what to do, and acting Even with the best knowledge, “perfect” perception and completely reliable execution of actions, an agent may fail if it cannot correctly (and quickly) make good choices An architecture is necessary for an intelligent agent, but it is not sufficient - that's for the next segment!
Back-up Examples of other agent architectures (following charts)
Model/Goal-based Agent
Utility-based Agent
Simple Learning Agent
4. Knowledge, Reasoning & Decision-Making David J. Atkinson, Ph.D Senior!Research!Scientist
Overview Knowledge Types Representations Formalisms Examples - Formal Logic - Production Rules - Semantic Network - Schemas / Frames / Scripts - Bayes Networks Organization Decision-Making Dimensions
Types of Knowledge Declarative:
Statements of fact (beliefs)
Procedural:
How to perform tasks (skills)
Semantic:
Relations of objects, situations (conceptual)
Episodic:
Entities and events encountered (cases)
Meta-Knowledge: The agent's own capabilities (self)
Knowledge Representation
Question: How best to encode various types of knowledge in a form that facilitates inferencing as part of a computational decision-making process? Key parameters of choice:
Expressivity: Easy and compact to state a fact Complete and Consistent: The whole truth and nothing else Complexity: Are the semantics easy to work with?
Roles:
Ontological: It defines what is important to think about Support Reasoning: Types of inferencing possible Pragmatism: Computational efficiency means tradeoffs Understandability: A way for humans to see what agents know
Representation of Knowledge
Representations are formalisms that encode knowledge in a form useful for computation Specific knowledge is not part of an agent architecture, but how an architecture represents what it knows is very important The different types of knowledge can be represented using multiple formalisms, with various tradeoffs Most formalisms with well-specified semantics have been shown to be “Turing Equivalent”
Theoretically, can be transformed from one into another In practice, very very difficult to do so
Choice of Formalisms
Fundamental choice:
Single:
Single formalism or multiple formalisms? Simplicity, elegance, more amenable to learning Can force an architecture into awkward and inefficient approaches to problems
Multiple:
Optimal formalism for each type of knowledge Greatly increased computational complexity Integrating multiple formalisms in the reasoning process can be very difficult
Example Formalisms Formal Logic Production Rules Semantic Networks Probabilistic Networks Schemas / Frames / Scripts
Formal Logic
Formal mathematical logics: representation and deductive techniques that are provably correct and complete Each type has expressive strengths and weaknesses, but the advantage is that these are well-defined Automated theorem proving enables precise derivations
“First Order Logic” is very frequently used but is not appropriate for domains where facts may change Non-Monotonic Logic is a Second Order Logic
Exhaustive derivation is computationally infeasible due to search space size (exponential growth)
for abductive reasoning for belief revision for default reasoning
Temporal Logic (time), Deontic Logic (permitted, obligatory, optional)
Production Rules
Formal logics may be implemented as production rules Production Rules are Condition–Action pairs, where a condition and action each is a logical “pattern”:
A condition may be a single fact or multiple facts (assertions) conjoined by logical operators (AND/OR) Conditions may contain variables (themselves patterns) Actions may be assertions of new facts, goals to achieve, or operations to execute Actions are contingent until the decision cycle stops
Using Production Rules
Using this knowledge representation requires an interpreter Match–Resolve–Act Cycle
Since many rules may match, deciding which one to use is a significant factor in performance: heuristics required
(remember Sense-Plan-Act?)
e.g., First, Last, Priority, Frequency of Use Sometimes in parallel, with or without bounds on iteration
The conflict resolution / priority heuristics in the interpreter are the mechanism for implementing different kinds of search algorithms
Decision-Making with Production Rules Backward Chaining
Forward Chaining
To determine if a candidate decision should be made, search for justification
Given a set of facts, enumerate conclusions that can be derived from the data
Means-Ends Analysis combines both
Production Rule Issues
Major challenges for applications (many tens of thousands of rules): Rules interact with each other in unexpected ways Maintenance: Adding/Deleting/Editing rules can result in unintended side effects There is no procedural knowledge in the rules themselves; inference is unguided
When correct (and that's provable) production rule representations are compact and very powerful
Schemas / Frames / Scripts
A representation based on structured attribute-value pairs Pairs (or other more complex structures with facets) are organized together to form a useful patterns The structure may be as simple as an unordered list or as complex as a directed graph Very useful for representing complete concepts that “go together” in a meaningful way for problem solving Generally frames are organized into hierarchies, possibly at multiple levels of abstraction.
Using Schemas / Frames / Scripts
The process of reasoning is based on:
Frame matching (e.g., situation recognition) Inheritance (e.g., filling in default knowledge) Spreading activation (e.g., what else applies?)
Especially useful for case-based reasoning and episodic memory storage and retrieval Examples:
“Baseball Team” => (position, player name) “Restaurant” => A sequence of actions (read, menu) (order, entree) (eat, food) (pay, waitress)
Bayes (Probability) Networks
Emphasizes subjective nature of information Formalizes a method for using Bayes' theorem Clearly distinguishes causal and evidential reasoning A Bayesian network represents a set variables and their conditional dependencies as a directed acyclic graph
Nodes represent
Observable quantities Latent variables Unknowns Hypotheses
Edges represent conditional dependencies
A node has a probability function that computes the truth of the node given all the parent variables and probabilities Inference is a result of performing various computations on the graph (many different types)
Using Bayes Networks
Probabilistic inference is used to infer unobservables – Many types – most important are importance samples, belief propagation, and related functions – All inferences on Bayes Networks are exponentially complex based on the structure of the graph
Various methods to learn probability distributions Frequently, the structure of a Bayes Network is not known or too complex to be specified in advance – Learning Bayes Networks is very useful for classification
Dempster-Shafer Theory is a generalization of Bayes' subjective probability which enables formal mathematical reasoning about degrees of belief – whether a given proposition is “provable” (not the same as “true”)
Organization of Knowledge Flat vs. Hierarchical Structure Fine vs. Coarse Granularity Types of Memory: Working, Long-Term, Buffers, etc. While organization is deeply tied to the knowledge representation techniques and reasoning methods, there are strong implications for computational tractability, understandability, maintenance, and effectiveness ** No single theory of knowledge organization is accepted widely enough to confidently provide guidance for applications: It is an engineering art
Dimensions of Decision-Making
Does problem-solving rely on heuristic search through problem spaces, or retrieval of solutions/plans? Parallel / Sequential => more important question is where are the sequential bottlenecks in the architecture? Search: An issue of managing the computational resources to find solutions. Is deliberation committed to run to completion, or is a result available at any time (and improves with time)?
Bounded Rational Agent
Intelligent agents follow the theory of “bounded rationality” – Decision making is a function of, and limited by, knowledge, methods of reasoning, and the time to make decisions – Not a process of finding an optimal choice!, instead, finding a satisfactory solution is acceptable since computing resources are finite (called satisficing)
The fundamental notion is to reduce the number of possible decisions (choices) by exploiting limitations, structure, and other constraints presented by the environment - “find short cuts” This can be done algorithmically with heuristics The intelligence or rationality of an agent is a function of the scope of its knowledge, the quality of its heuristics, and its logic and efficiency in applying these to problems
Take Away Thoughts • Choice of knowledge representation, formalisms and reasoning methods is dictated by: – – –
The problem domain The tasks to be achieved, problems solved The desired level of performance
• Engineering knowledge has proven to be extremely timeconsuming, error-prone, and difficult to maintain – Wide agreement that machine learning is required
• Reasoning and decision-making, while correct, may be extremely difficult to explain to a human depending on the architectural dimensions of the agent. • Achieving optimal problem-solving is computationally intractable – the best we can do is “good enough”
Autonomous Systems Tutorial: Part II
5. Planning, Execution & Control David J. Atkinson, Ph.D Senior!Research!Scientist
Overview Planning Classical planning and example systems Why it is hard: computational complexity Types of planning systems Task Execution & Control Planning meets action Monitoring – for when things go badly Getting back on track Multi-Agent Systems Coordinated planning and execution
Acknowledgment: Some material adapted from Simmons & Veloso course at CMU
About Planning
An area of research in artificial intelligence for >30 years, initially motivated by mobile robots Planning is a type of problem-solving Requires structured representations of the world, actors, and actions: how will things change? Planning is about hypothesizing the possible actions and their consequences which are necessary and sufficient to accomplish goals
Applications of Planning
Robot mobility and behavior –
Simulated environments –
Oil-spill, humanitarian relief, forest fires, urban rescue
Factory automation –
Composing queries or services Workflows on computational grids (e.g., finance)
Managing crisis situations –
Goal-directed agents for training or games
Web and grid environments – –
Underwater, sea, land, air and space applications
Nuclear power, pharmaceutical, manufacturing
Autonomous spacecraft –
Deep space, Earth observation, mission control
Plan Generation
Given all the states of the world, search for paths between states –
Many different kinds of search algorithms Many ways to decide which paths are “best”
Actions create the paths between world states
e.g., fewest steps, safest, resource efficient, etc.
When are plans generated?
Well in advance of execution (known and constrained environments)
Just-in time (Dynamic and/or uncertain environments) Seldom or Never (reactive behaviors)
Planning - States
The world is described in terms of a “state”:
A conjunction of a set of state variables, each representing some facet of the world. State space = all such sets
Conjunctive, enumerative, observable Complete, correct, deterministic Probabilistic, approximate, incremental, on-demand
“state space explosion” = near-∞ number of variables and values of possible interest
Planning - Goals Goal State –
Goal Statement –
Completely specified
Partially specified state
Preference Model – –
Objective function Defines “good” or “optimal” plan
Increasing Generality
Preference Models
Planning - Actions
Actions change state variables PickUp(block): location(block,table) => location(block, hand)
Action representation not usually so simple:
Conditional effects (derived effects) Quantified effects (changes a class of facts) Disjunctive and negated preconditions Functional effects (e.g., depleting a resource) Disjunctive effects Probabilistic effects ...external, concurrent, conditional, duration …
The richer the set of possible actions, the more difficult planning becomes.
Key Questions
While planning, deliberation only and no execution?
Deliberation:
Sometimes need to think while acting
How long/efficiently to plan? How often? How precise?
World modeling:
How accurately? How detailed? How precise?
What is a plan? A set of actions that, if executed, transforms the initial state of the world to a goal state (Newell and Simon) A plan is a partially-ordered sequence of states Once execution begins, there is a history of states Unrealized future states are predictions of what will happen
Plans may include: One or more agents Disposition of resources Constraints, policies Contingencies Varying degrees of granularity: abstract to fine detail
A Plan
Set of instantiated actions Tree of instantiated actions Policy mapping states to actions
Increasing Generality
Sequence of instantiated actions
Classical Planning Problem Unique, unambiguous known initial state Actions are deterministic and have no duration Actions must be executed one at a time Only one agent
A plan is an fully-ordered sequence of actions No need for monitoring since you can accurately predict the state of the world after any action.
Very few (interesting) planning problems have these properties!
Situation Calculus (McCarthy 63)
Key idea: represent a snapshot of the world, called a “situation” explicitly. world state “Fluents” are logical statements that are true or false in any given situation,e.g., “I am at home” Actions map situations to situations S1 = result(go(store), S0)
go(store)
S1 S0
holds (at(home), S0) holds (color(door,red), S0)
mow_lawn()
S2
¬holds (at(home), S1) holds (at(store), S1)
Frame Problem
I go from home to the store, creating a new situation S'. In S': • • • • •
My friend is still at home The store still sells chips My age is still the same Los Angeles is still the largest city in California ....
There are an infinite number of fluents! How can we efficiently represent everything that hasn't changed?
Successor state axioms
Normally, things stay true from one state to the next, unless an action changes them: holds(at(X),result(A,S)) iff A = go(X) or [holds(at(X,S) and A != go(Y)]
We need one or more of these for every fluent Now we can use automated theorem proving to deduce a plan!
Not so fast... – Writing “frame axioms” is tedious, error prone and inefficient – Length of axioms proportional to number of actions – Not computationally tractable
Strips planner (Fikes and Nilsson 71)
Strips Assumption: – –
No logical inference, just adding and deleting facts –
World state is a database of ground literals If a literal is not in the database, it is assumed to be false
Strips separated theorem-proving within a world state from searching the space of possible states
Revealed new challenges (“ramification problem”; “Sussman anomaly” …) How do we even decide what facts to represent? After 40 more years of research most challenges have been knocked down, however, one realization has become very clear …
Complexity of Planning Domain-independent planning is undecidable in some cases, and usually exponentially complex • Typically exponential in the number of actions • Typically polynomial in the number of states • Typically number of states is exponential in the number of actions needed • Decomposing state space is the key to planning efficiency • Many ways to find solutions to planning problems –
they differ in complexity and how well they scale with respect to size of search space
Planning is a Decision Problem Decision Problem: a problem with a yes/no answer –
Decidable if there is a program that takes any instance and correctly halts with answer “yes” or “no”
–
Semi-decidable if program halts with correct answer in one case (either “yes” or “no”) not not in the other case (goes on forever)
–
Undecidable if there is no algorithm to solve the problem, e.g., “halting problem”
Planning Decision Plan Existence: Is there a plan that achieves goals given the initial state and what I know how to do? Decidable if actions, constants, and initial state is finite, otherwise Semi-decidable
If decidable, how many resources are required to compute the answer (in the worst case) - We measure in terms of time or space (memory) as a function of the size of the input
If a problem is known to be in some complexity class, then we know there is a program that solves it using resources bounded by that class
Complexity Classes
Deterministic Exponential Space Non-Deterministic Exponential Time Exponential Time Polynomial Space Non-Deterministic in Polynomial Time Deterministic in Polynomial Time
Complexity of Planning
Most planning algorithms for domains of practical interest
Deterministic Exponential Space Non-Deterministic Exponential Time Exponential Time Polynomial Space Non-Deterministic in Polynomial Time Deterministic in Polynomial Time
Tractable Planning To make automated planning computationally tractable: • Knowledge about the structure of a problem domain is used to remove irrelevant calculations – –
Factored state representation Means-ends analysis
• Known solutions are adapted and reused • Heuristics, partial solutions and a variety of other aids are learned • Anytime algorithms: When time is short, carefully manage what you work on and give the best result Perfect plans are rare
Types of Plans Certainty
Strong Solutions: – –
Plans that are guaranteed to achieve the goal Limited or highly constrained domains: factory automation, traffic control, railroad switching
•
Strong Cyclic Solutions: –
–
• Uncertainty
Iterative trial-and-error strategies whose executions always have a possibility of terminating and, if/when they do, they are guaranteed to achieve the goal. Typical of domains with initially unknown but discoverable features: local navigation and mapping
Weak Solutions: – –
Plans that may achieve the goal When the effects of actions are probabilistic due to uncertainty about the world and or capabilities
Planning is Problem Solving Planning is reasoning about the effects of actions using a representation the world to search for ways to achieve goals Methods used depend highly on properties of the problem: • Actions: Certain or probabilistic outcomes? • State variables: Discrete or continuous? Finite? • Knowledge: Foreknowledge and the ability to observe state • Temporal considerations: Do actions take time? • Concurrency: Ability to do actions in parallel. • Agents: One or many? Cooperative or Selfish? • Control: Centralized or Distributed? • Resources: Many different types!
Properties of Planning Algorithms
Soundness A planning algorithm is sound if all solutions found are legal plans
All preconditions and goals are satisfied No constraints are violated
Completeness A planning algorithm is complete if a solution can be found whenever one actually exists A planning algorithm is strictly complete if all solutions are included in the search space
Optimality A planning algorithm is optimal if the order in which solutions are found is consistent with some measure of plan quality
Search Algorithms Many different kinds of search algorithms in planning
Progression: forward from state
Regression: backward from goal
Focused on achieving goals More inference required; sometimes incomplete
Linear: Solve one goal at a time from a stack
Simple algorithm (“forward simulation”) Often large branching factor
Simple, efficient under some conditions Incomplete and may produce suboptimal plans
Non-linear: Interleave work on subgoals from a set
Complete, can produce shorter plans Larger search space
All Approaches Have Same Issues Tractability • Plans can have many possible outcomes • How can we control reasoning and search to remain tractable? Plan Utility • Is probability of success enough? • What measures of cost and benefit can be used? • Can we compute the cost of executing a plan? • What differences are made by time-based utilities (deadlines)? Observability and conditional planning • Classical planning is “open-loop” with no sensing • What policies are needed to assume unimportant changes? • How to reason about when to add sensing? • Can we model limited observability, noisy sensors, bias …?
Break 10 minutes
Example Planning Approaches Reactive Planning Hierarchical Task Networks Constraint Satisfaction Model Checking Temporal Reasoning Heuristic Search
Reactive Planning Reactive Action Packages (RAPS) • Networks of “conditions” and “tasks” Task Control Language • Network arranged according to capabilities Procedural Reasoning System (PRS) • Integrates planning, BDI and reactive techniques
Hierarchical Planning Hierarchical Task Network Planners Captures the hierarchical structure of a planning domain Non-primitive (high-level) actions and schemes focus search for solutions: •
Expert knowledge (preferred ways to achieve a goal)
•
Reduction schemas (task abstraction via network)
Task Reduction: A way to refine plans Task Hierarchy is similar to context-free grammar •
Prune plans that do not conform to the grammar (partial order planner)
Task Reduction
HTN Planning (nutshell) Problem reduction: • Decompose tasks into subtasks • Handle constraints • Resolve interactions • If necessary, backtrack and try other decompositions Formalization: »
Tasks: Primitive and non-Primitive (goals, compound tasks)
»
Actions: Operators with pre- and post-conditions
»
Method: A non-primitive task in a given task network
»
Plan: A sequence of ground primitive tasks (operators)
»
Task Network: a directed graph whose nodes represent tasks with constraints
»
Bindings on conditions, ordering constraints, other constraints on state
Basic HTN Procedure (Recursive) • •
• • • • •
Input a planning problem P If P contains only primitive tasks, then resolve the conflicts and return the result. If the conflicts cannot be resolved, return failure Choose a non-primitive task t in P Choose an expansion for t Replace t with the expansion Find interactions among tasks in P and suggest ways to handle them. Choose 1. Go to 2
Basic HTN Procedure (Regression) • •
• • • • •
What kind of representation? Input a planning problem P Ordering, resources, etc If P contains only primitive tasks, then resolve the conflicts and return the result. If the conflicts cannot be resolved, return failure Choose a non-primitive task t in PWhich one? Many different ways to Choose an expansion for t expand (“decompose”) Replace t with the expansion Find interactions among tasks in P and suggest ways to handle them. Choose 1. Mutual exclusions: inconsistent effects, competing needs, Go to 2 interference, ...
Answering such questions is a problem-solving task that can be automated (planner languages, learning, self-optimization)
HTN Bottom Line Powerful technique, successful and widely used Explicit knowledge about how to solve problems Abstractions encapsulate useful patterns of interaction Can generate a larger class of plans + May be easier to specify domain + Easier to understand what is going on - Have to specify all possible goals (and how to achieve them). Knowledge engineering is hard - May not terminate (hard to detect infinite loop) - May have to completely expand before discovering a plan won't work
SIPE-2 (Wilkins 85-) Generates and monitors execution of plans Multi-level plan/task abstractions Main focus on computational efficiency using heuristics Applications
http://www.ai.sri.com/~sipe/
Air campaign planning (DARPA, AFRL/RI) Oil Spill Response Planning (Coast Guard) Planning and Decision Aid for Small Unit Operations (DARPA) Flight Manager Assistant for RT Flight Ops (AF Air Mobility Command)
Task Execution & Control Task execution is the bridge between planning and action in an autonomous system The Sequencer/Executive:
Decomposes unplanned tasks and dispatches subtasks Monitors execution for contingencies (and opportunities) Schedules or reschedules tasks (if failed earlier)
Primary differences between Executive approaches:
Methods for distributing functionality in agent architecture Domain and control knowledge representation Reactive vs. deliberative vs. hybrid
Why Monitor Execution? “Everybody has a plan until they get hit in the face” - Muhammad Ali (World Champion Boxer)
What could possibly go wrong? Capability changes (something in the agent breaks) Incomplete knowledge (e.g., unknown preconditions) Incorrect knowledge (state e.g., spare tire is flat; missing post-conditions) Exogeneous events (most are irrelevant to a plan) External processes (e.g., weather) Goal changes Policy changes Effects of other agents (cooperative, neutral, adversarial) Non-deterministic effects of actions (disjunctive effects) Qualification Problem: You can never finish listing all of the required preconditions and possible conditional outcomes of actions
Solutions
Conditional planning Plan to obtain information (observation actions) Make a sub-plan for each contingency Expensive because there are many unlikely cases
Monitoring / Replanning Assume normal states and outcomes Check progress during execution, replan if necessary Unanticipated outcomes may lead to failure
Combination
Plan for likely/serious eventualities, deal with others as they arise
Compromise: planning efficiency, chances of success
Planning Under Uncertainty Deal with contingencies at planning time, before they occur • • •
Can’t plan for every contingency, so need to prioritize How do we know what the most likely contingencies are? Can we distinguish bad outcomes (not holding the cup) from really bad outcomes (broke the cup and spilled sulfuric acid)?
Key Approaches: Probabilistic planning: Bayesian Networks • How do we figure out the probability of a plan succeeding? – Often, the time to compute probability of success dominates the total planning time
• Can counter plan threats by decreasing probability they will happen: create conditions that negate the threat trigger Decision theory: Markov Decision Processes • Need to control state-space explosion • Possible to find optimal solutions
Probabilistic (Bayesian) Planning
Enables us to explore the middle ground between planning for contingencies and replanning Assumes partial knowledge about uncertainties: different contingencies have different probabilities of occurring (e.g., 70% chance of rain) Approach:
Plan ahead for likely contingencies that may need steps taken before they occur (e.g., bring umbrella)
Use probability theory to judge plans that address selected contingencies:
Find plan that is above some min probability of success See Tutorial Part 1, Sec. 4 on Bayesian Networks
Decision Theoretic Approach for Uncertainty (Non-Deterministic Actions)
Given: Nondeterministic (probabilistic) actions without a duration, a single agent and a goal to maximize a reward function: If fully observable, use: Discrete-time Markov Decision Processes (MDP)
…otherwise use Partially Observable Markov Decision Process (POMDP)
If multiple agents => Use decision processes related to Game Theory
Simple Conditional Plan
Execution Monitoring Need to detect changes that matter to plan progress “Failure” = preconditions of remaining plan are not met Preconditions: All preconditions of remaining steps not achieved by remaining steps All causal links crossing current time point
Getting back on track Recovery: Repair a plan or replan from scratch? Activate appropriate preplanned contingent actions • Expensive because many unlikely plans are made Re-planning and Repair: • Make a plan assuming nothing bad will happen • Build a new plan if a problem is found or repair plan • In some cases this is too late Dominant Approach: • Combination: Plan for likely or serious eventualities, deal with other problems when they arise (and we know they will)
Multi-Agent Systems Major Challenges: • Allocating tasks to individual agents • Dynamic re-tasking • Managing shared, limited resources • Sequencing activities • Cooperation between agents • Dynamic, flexible and adaptive response to change (mission, environment, capabilities)
A Spectrum of Approaches Centralized model (Hen and chicks) Decentralized model (Ants or Bees) Key challenges for an multi-agent architecture: • Who is in charge? • How are agents distributed? • How much and what kind of autonomy does an individual agent have? • When, what and how do agents communicate? • How and how much to coordinate? • How do agents resolve conflicts?
Multi-Agent Coordination Task Sharing (Homogeneous or Heterogeneous agents) – Contract Nets • Agents (contractors) bid for tasks • Managers (control layer) bids for contractors
– Market Mechanisms
Results Sharing – Blackboard architectures – Distributed constraint satisfaction – Resource sharing through auctions
Multi-Agent Coordination Distributed Planning – Planning Approaches • • • •
Cooperative plan construction Centralized planning for distributed plans Distributed planning for centralized plans Distributed planning for distributed plans
– Execution Issues • Pre-planning • Post-planning
State and Collaboration – Joint intentions – Shared knowledge (including plans or plan fragments) – Coordination without communication (reacting to the effects of other agents’ actions)
The Human Role Generation of a solution: plan generation
User specifies goals and tasks for the solution
User indicates preferences or constraints to be used during planning
Assessment of a solution: plan evaluation
User indicates criteria to analyze plan features
Tradeoff analysis in solution quality: plan comparison
User navigates solution space and indicates preferences
Resource assignment: scheduling
User indicates resource allocations, temporal constraints
Problem formulation: design of the planning task
User adds and retracts constraints on the planning problem
User establishes policies for system’s responsibilities
Questions?
Autonomous Systems Tutorial: Part II
6. Learning and Adaptation
David J. Atkinson, Ph.D Senior!Research!Scientist
Outline • • • • •
Review: Types of Knowledge Why Learn and Adapt? Desired Capabilities Bootstrapping a Mind Key Theoretical Questions
Types of Knowledge Declarative:
Statements of fact (beliefs)
Procedural:
How to perform tasks (skills)
Semantic:
Relations of objects, situations (conceptual)
Episodic:
Entities and events encountered (cases)
Meta-Knowledge: The agent's own capabilities (self)
Why Learn and Adapt?
Why should an autonomous system learn and adapt? – – – –
Even if that was not the case: – –
It is highly unlikely it knows everything it needs Some of what it knows may be irrelevant What it knows likely contains errors It's problem-solving skills are sub-optimal
It will be tasked and used in unanticipated ways We require that agent performance improves
Uncertainty dominates – – – –
The world is constantly changing Predicting the actions of other agents is difficult A great deal of relevant information may be hidden or not readily observable Realistically, confidence in information is never absolute
Practical Matters
Knowledge engineering has proven to be difficult, error-prone and incomplete for domains of any significant complexity
Procedural knowledge is especially difficult to encode Problem-solving skills can benefit immensely by optimization as a result of learning
Knowledge and skills can rapidly become obsolete unless continually assessed and improved Autonomous systems interacting with human users must be able to adapt to new contexts and at extended time-scales, in a variety of environments that cannot be foreseen at during design and development.
Desired Capabilities To learn, a system must make effective judgments about: – – – – –
Similarity Representativeness Randomness Coincidences as clues to hidden causes Causal strength and evidential support
Essential for: – – –
Diagnostic and conditional reasoning (causal knowledge) Predictions about events (episodic knowledge) Correctly identifying new instances of objects, actors, and situations (semantic knowledge)
Bootstrapping a Mind • To “go beyond the data” requires other sources of data and processes that make up the difference • Something more abstract must generate and delimit potential hypotheses or meaningful generalization would be impossible (computationally intractable) – Psychologists and Linguists: “Constraints” – Artificial Intelligence: “Inductive Bias” – Statisticians: “Priors” The key question is what data, information, or process is needed to bootstrap learning knowledge
Three Key Theoretical Questions 1) How does abstract knowledge guide learning and inference from sparse data? –
Question of Constraints and Inductive Bias
2) What forms does abstract knowledge take, across different domains and tasks? –
Question of Representation
3) How is abstract knowledge itself acquired? –
Question of Cognitive Development
From (Tenenbaum, et. al., 2011)
Schools of Thought • Associative Learning (connectionism) – Simple, unstructured forms of knowledge – Statistical learning; correlations – Assumes knowledge is induced with trivial mechanisms – Learning is about adjust weights, strengths, parameters – Example: Artificial Neural Networks (ANNs)
• Conceptual Learning (semanticism) – Symbolic, richly structured knowledge – Logical, heuristic, other non-statistical methods – Assumes (some degree) of abstract knowledge is innate – Learning is about discovery of rich symbolic structures – Example: Explanationbased learning
Acquisition of Abstract Knowledge • Discovering a structure (form) for the properties and data about “objects” enables new inferences –
Clusters; nameable categories, tree-like hierarchies
• Associative learning algorithms assume a single fixed structure (e.g., clusters) –
Cannot learn other forms
• Conceptual learning algorithms start with some knowledge of multiple structures, then adapt data to the one(s) that fit the best –
Capable of learning new forms
Importance of Representation Representations – The type of structured symbolic form(s) used has a strong influence on ease of encoding of concepts and inference – Imposes constraints on induction (generalization) – “Compact” representations reflect real-world granularity and make reliable induction easier and computationally efficient
Neural Network
vs.
Belief Networks
Distributed representation
Localized representation
Network variables have only one degree of activation
Network nodes may have many active dimensions (properties, range of values, probabilities)
Once trained, inference can be executed in linear time
General inference is NP-Hard (computationally complex)
Associative
Conceptual
Graph Representation Every Everyform formofofabstract abstractknowledge knowledge can canbe berepresented representedas asaagraph. graph. The Theprinciples principlesofofthe theform formare areequivalent equivalent totoaagrammar grammarfor forgrowing growinggraphs graphsofofthat thatform form learning learninggrammars grammars== ==learning learningnew newforms forms • Very useful! – – –
We have rigorous mathematical tools for analyzing graphs and grammars to make formal proofs Different machine learning algorithms work may work better with graphs or with grammars Now we know we can (theoretically) transform one into the other
Cognitive Models Cognitive model = structured symbolic forms and the processes that operate on them
Important model properties enable machine learning: • Generative: – Supports hypotheses about hidden variables
• Abstract: – Represents not only specific situations but classes over which generalization is possible
Inference in Learning Deduction – – –
Knowledge-intensive Explain and analyze an example instance Apply generalized concepts to infer facts about new instances
Induction – –
Data-intensive; requires many examples Generate a general description of a concept
Abduction – – – –
The “art of good guessing” Mmaking reasonable hypotheses Identify an explanation of the sufficient conditions for describing a concept (there may be many explanations) Motivates simple, efficient explanations (e.g., Occam's razor)
Putting it all together
Associative and Conceptual learning algorithms have each proven to be useful for different classes of problems Historically, these have been separate developments with different communities of interest
Difficult conceptually to unite them in theory or practice
Recently, Bayesian learning methods have shown a bridge between the two schools of thought:
Hierarchical Bayesian Models combine richly structured, expressive knowledge representations with powerful statistical (probabilistic) inference engines The best of both conceptual and associative learning!
Hierarchical Bayesian Models • Key insight: multiple levels of hypothesis networks arranged hierarchically can be used to address the origins of the hypothesis spaces and priors (probabilities) • Hypothesis spaces of hypothesis spaces! • Each layer generates a probability distribution on variables at the level below; higher levels pool variables from below • Advantage: Hypotheses and priors can be learned at longer time scales while still constraining lower level learning (thus avoiding computational intractability)
Hierarchical Bayes Models (HBM) Can discover the basis of “similarity” in a problem domain:
Can infer the correct (and best) forms of structure (grammars) for many domains Can learn abstract causal knowledge and specific causal relations at a level below Fast (polynomial), from relatively little data HMBs have been effectively applied to a wide range of analysis and learning problems in multiple domains
Other Dimensions of Machine Learning • Conceptual vs. Associative –
Blends such as Hierarchical Bayes Models
• Orthogonal dimensions – –
Supervised vs. Unsupervised “Off-line” vs. “On-line” (learning while doing; active)
Many possible hybrid techniques are possible... This is very much the frontier of research!
Supervised Learning
The learner is provided with labeled training data, examples such as (instance, class) An instance is a vector of features
A learning system may be given many sets of training data The learning algorithm infers a function from the training data: called a Classifier (assumes discrete data) A Classifier is valid if it produces the correct out given a new instance of an unknown class Inductive reasoning Key challenges:
What are the important features of an instance? Bias vs. variance (flexibility vs. consistency) Amount of training data vs. complexity of classifier Dimensionality of features (supervisor should reduce #)
Unsupervised Learning • Conventional algorithms for unsupervised learning assume a single fixed form of structure is to be discovered – Hierarchical clustering, principal components analysis, multidimensional scaling, clique detection
• Cannot learn multiple forms of structure, or discover new forms in novel data • Examples: – Genetic / Evolutionary Algorithms – Neural networks (Self-organizing map; Adaptive resonance theory) – Statistical methods (clustering; density estimation)
Off-line vs. On-line • An “off-line” (passive) learner simply watches the world going by, and tries to learn the utility of being in various states
• An “on-line” (active) learner must also act using the learned information, and can use its problem generator to suggest explorations of unknown portions of the environment
May be more typical of autonomous systems, although both are useful
Learning by Doing • Includes learning while planning and learning while executing a plan (active, “on-line” learning) • Motivation: The knowledge in domain theory is not usually effective/efficient ab initio – An agent must learn how to use knowledge
• Learning in Planning: Opportunities – – –
Search Efficiency: Learn control knowledge to guide a planner though the search space Domain Specification: Learn the preconditions and effects of the planning actions Quality: Learning control knowledge to create higher quality plans
Widely used methods Explanation-based learning Reinforcement learning
Explanation-based Learning • A deductive learning method • Purpose is not to “learn” more about target concept – To “re-express” target concept in a more operational manner – “Control” learning leads to greater efficiency
• Domain theory contains the information – Not usually effective; rarely complete
• Examples focus on the relevant operational knowledge: – Characterize only examples that actually occur
• Very useful in learning how to plan
Explanation-based Learning Inputs: • Target concept definition • Training example • Domain theory • Operationality criterion Output: Generalization of the training example that is: • Sufficient to describe the target concept, and • Satisfies the operationality criterion (adapted from Veloso and Simmons, 2010)
SAFE-TO-STACK Example
SAFE-TO-STACK Example
SAFE-TO-STACK Example
Generating Operational Knowledge
Reinforcement Learning
Concerned with maximizing reward by appropriate action The agent receives some evaluation of its action (such as a hefty bill for rear-ending the car in front) but is not told the correct action
A general problem studied by many disciplines Typically formulated as a Markov Decision Process (MDP) or Partially Observable Markov Decision Process (POMDP) Focus is “on-line” performance, a balance of exploratory learning with using that knowledge to accomplish tasks Agent chooses actions, gets reward, then adapts selection function Especially useful when the only way to get information is by interacting with the environment (no training data) Many uses in robot control Adaptation
Ubiquitous Learning (Forbus, 2009) • People learn continually in all sorts of situations • Computers (typically) learn only when directed – Consumes most or all resources – Incompatible with highly interactive systems
• Ubiquitous learning aims to learn constantly: – Compute-intensive learning tasks are off-loaded to background processing on dedicated notes – Learning is focused via explicit learning goals • constructed on the fly, prioritized, scheduled, reasoned about
– Not just learning about domain knowledge • Learning how knowledge is communicated • Learning about agent's own expertise and understanding
This will be essential for long-lived autonomous agents
Learning vs. Adaptation Learning: • Adds possibilities • Conceptual, abstract • Relations • Generalization
Adaptation: • Constrains possibilities • Concrete • Parameters • Refinement, tuning
Both are important to … intelligently react to change and … to improve performance
Ultimate Challenge of Learning • Formalizing the content of all intuitive theories requires Turing-complete compositional representations; not yet invented – Probabilistic first-order logic – Probabilistic programming languages
But we can usually do “good enough” Consequence: => formally proving correctness of (most) learning systems is still a major stretch
Challenges for Humans Introspection, Learning and Bootstrapped Ontologies
• Autonomous learning systems will develop their own ways of clustering phenomena – –
What they've been exposed to Their successes and failures
• They will use this information to optimize themselves – –
Internal problem-solving capabilities States and Processes
• No one else will be able to understand this intuitively –
No one else has the identical history of experience!
Subsequent effects of using those ‘personal’ concepts may exacerbate the complexity and idiosyncratic character of the autonomous agent's internal processing
Challenges for Humans
For many if not most machine learning algorithms, it is hard to see where human input can make an impact...possibly:
The products of associative learning are hard to explain because they are distributed and have little structure
Statistical, reinforcement, fuzzy, genetic algorithms
“Undoing” what has been learned is very hard
Selection of training examples Ordering presentation Providing criticism, reward
2nd Order logics let us retract beliefs … theoretically
Long-lived learning
No systems have learned over extended periods
Autonomous Agents that Learn
“Dexter” performs cooperative assembly task with human and learns how to: - generate effective expressive behavior - build robust, scalable knowledge model of humans - recognize human behavior and infer human intention http://nishabox.com/research/thesis
Shichao Ou, Univ. Mass
“ARMAR-III” learns about objects and what actions can be applied to them by touching and manipulating, with human guidance, hints and demos. Embodied cognition Tamim Asfour, Karlsruhe Inst. Tech
“Simon” learns concepts from a human teacher through demonstration, asking questions and active learning M. Cakmak, Georgia Tech
Questions? Thank you!
Autonomous Systems Tutorial: Part II
7. Autonomy and Human-Agent Interaction David J. Atkinson, Ph.D
Topics • • • • • • •
Agents are different What we want to know Semi-Autonomy Joint Activity Model Interdependence Mixed Initiative Interaction Modeling of Self and Others Guidelines for Interactive Agents
Agents are Different
When a significant fraction of what a machine knows is automatically learned, manual inspection will be hopeless Agents operate independently in complex situation, without human supervision, on a scale and with speed that is impractical or impossible for humans to be “in-theloop” Autonomous agents are becoming increasingly different from the software we use today We will find some roles for agents where the agent is working alone, but a great many roles will involve interacting with humans.
What we want to know What does the system know? What is it doing? What is it planning to do? Is it under control? Is it doing the right thing? How can I use it? How can I teach it?
Levels of Control Earlier in the tutorial, we said that autonomous systems are part of a spectrum of “levels of control”
Controlled systems:
Supervised systems:
carry out fixed functions without intervention
Autonomous systems:
machines do precisely as instructed
Automatic systems:
human has full or partial control
make independent decisions on what to do
That last category is very complex when it comes to interacting with humans
Notion of Semi-Autonomy Must Change
The success in achieving greater autonomy has raised the bar on the nature of useful human interaction “Adjusting autonomy” is not a simple matter of transferring control – the nature of the task changes Traditional view of function allocation is not sufficient – not a case of who does what better, but how do human and machine agents complement each other? Many functions in complex systems may be performed by humans as well as machines
Teaming
No longer a case of “what can I do with this tool” It is now “what can this agent and I do together” Machines and humans are, and will remain, vastly different, with different competencies
Humans are far better at representation and reasoning Autonomous agent's ability to sense and infer information about the human environment is (so far) very limited
“What can we do together” is a function of how well we enable our mutual competencies to become synergistic
Competencies Enhanced
From R. Hoffman (“un-fitts chart”)
Joint Activity Joint activity theory captures the central new idea:
Interdependence –
Joint activity is a process involving two or more agents; a flow of actions beginning and ending
(a generalization of Herbert Clark’s work in linguistics)
Joint activity structure: embedded actions Some may be jointly accomplished, others individually The interdependence of these activities is the framework in which to consider human-agent interaction
Joint activities can be analyzed in terms of the types of interdependencies required among agents J. M. Bradshaw, P. Feltovich, and M. Johnson, Human-Agent Interaction. (Forthcoming) Institute for Human and Machine Cognition, 2010.
Types of Interdependence Co-allocation
Interdependence among resources. Parties have independent goals No functional coupling of methods
Example: Two groups of agents who want to use a meeting room
Co-operation
Example: Interdependence of activities Players on opposing Agents do not necessarily share teams in a football game motivations or goals Often includes interdependence of resources
Collaboration
Shared objectives; agents try to achieve the same goals Example: Players on same team Interdependence of activities, in a football game roles and resources
Coordination is Hard
Interdependencies exist between software agents as well as between software agent and human There must be sufficient basis for shared situational awareness and feedback for human-agent interaction
Coordination is achieved through various mechanisms –
Interpredictability (teams with shared experiences) Common Ground (pertinent mutual knowledge, beliefs and assumptions) Directability (deliberate assessment and modification of each other's actions)
(natural) social interaction, rules, regulations, norms and policies
Requires exchange of rich content, frequently and with attention to agent's (human) state, signals, and task at hand
Communication Establishes Grounding
Participants in joint activity need common understanding:
About the situation The activity they are engaged in The goals The nature and timing of individual contributions
A major challenge in grounding is the continuous resolution of uncertainty
The activity and knowledge of the participants The nature of the problem The abilities of each participant to contribute
Mixed Initiative Interaction
Mixed initiative interaction allows multiple agents (human and machine) to work together effectively as a team Individual agents sometimes lead and sometimes support, and sometimes work independently Contributions by each agent are interleaved in joint activity and aimed at converging on solutions to problems There can be many different types of mixed initiative interaction, and the type of interaction may change based on many factors during joint activities
Types of Mixed Initiative
Unsolicited reporting
Sub-dialogue initiation
An agent might initiate a dialog, e.g., to ask for clarification
Fixed-subtask mixed-initiative
The agent continually verifies the progress of problem solving and notifies the user when problems crop up The user retains control of the interaction
An agent has responsibility for certain operations and the human user for others The agent might ask the human to make choices or to decide when the agent needs help
Negotiated mixed-initiative
No fixed assignment of responsibilities or initiative between the human and agent (or any two or more agents of either type) Requires a high degree of self-knowledge as well as the ability to model other agents
Self-Knowledge
Taking the initiative requires an agent to have several abilities relating to self-knowledge:
The agent must have the ability to recognize problemsolving opportunities
The capability to coordinate the interaction and achieve the subtask The resources available given other demands for its attention Recognize if there another agent who is more capable of coordinating the interaction
Its automated capabilities may assist another agent in solving the problem in a useful manner
Given inescapable uncertainty
Make a cost versus benefit analysis to decide whether to take the initiative or not
Knowledge about others
When the agent is not leading the interaction, it must interpret the responses of the other agents (human or agent)
In more complex, open-ended tasks the agent needs to understand:
The competency and goals of the other participants
What actions they are trying to accomplish
What they are concerned with (e.g., the interaction about the task or performance of the task itself) The purpose of their communication (e.g., request, agreement, etc.)
The agent needs to understand how humans understand information
In simple tasks → the allowable responses may be quite limited
perceptual mechanisms, cognition, and affect
Humans, in decision-making under uncertainty, see information vital to the decision process
When uncertainty is “high”, it may be better for an agent to let the human take the initiative until useful information can be identified
Challenges of Mixed Initiative Interaction Problem-solving style
Formal problem solving methods are not easily interleaved with human decision makers' common practice of moving recursively between information seeking and making comparative judgments about alternatives Need flexible problem solving frameworks that can support collaboration
Managing classes of solutions, tracking constraints and previously explored solutions
Understandable problem decomposition and solution
The agent must have the ability to decompose problems into appropriate subproblems that a human may have to understand The agent must weave solutions to subproblems into larger solutions that are understandable to a human Ground the discussion with a specific plan
Human-agent mixed-initiative interaction requires signaling
E.g., gestures help maintain fluid exchange of initiative Mapping human input into possible operations/responses, disambiguating requests is a challange
Example: Interactive Planning Tasks Generation of a solution: plan generation
User specifies goals and tasks for the solution
User indicates preferences or constraints to be used during planning
Assessment of a solution: plan evaluation
User indicates criteria to analyze plan features
Tradeoff analysis in solution quality: plan comparison
User navigates solution space and indicates preferences
Resource assignment: scheduling
User indicates resource allocations, temporal constraints
Problem formulation: design of the planning task
User adds and retracts constraints on the planning problem
User establishes policies for system’s responsibilities
PASSAT: Mixed-Initiative Plan Authoring for HTN Planning [Myers 97]
User specifies tasks (prim or non-prim) that should be part of the solution: a plan sketch System completes plan sketch
Extended to handling incorrect sketches [Myers 03]
Hypothesizes top-level goals Refines goals to include sketch tasks (anchors)
“Orphaned” tasks that do not map to any top-level goals Inconsistencies with template constraints
Really good at incorporating user guidance into automated plan generation algorithm Adapted from Y. Gil, USC
Guidelines for Agents (1) • A good agent is observable It makes its pertinent state and intentions obvious. • A good agent enables frequent progress appraisal It helps others to stay informed about the status of its tasks and identifies any potential trouble spots ahead. • A good agent is informative and polite It knows enough about others and their situations to tailor its messages to be helpful, opportune, appropriately presented. • A good agent knows its limits It knows when to take the initiative, and when it needs to wait for direction. It respects policy-based constraints on its behavior, but will consider exceptions and workarounds when appropriate. J. M. Bradshaw, P. Feltovich, and M. Johnson, Human-Agent Interaction. (Forthcoming) Institute for Human and Machine Cognition, 2010.
Guidelines for Agents (2) • A good agent is predictable and dependable It can be counted on to do its part. • A good agent is directable at all levels It can be re-tasked in a timely way by a recognized authority whenever circumstances require. • A good agent is selective It helps others focus attention on what is most important in the current context. • A good agent is coordinated It helps communicate, manage, and deconflict dependencies among activities, knowledge, and resources that are prerequisites to effective task performance and the maintenance of “common ground.” J. M. Bradshaw, P. Feltovich, and M. Johnson, Human-Agent Interaction. (Forthcoming) Institute for Human and Machine Cognition, 2010.
Observations
Human-centered computing is being transformed
Rethink human-agent interaction and systems autonomy
Joint activities, not function allocation Not “transfer of control” → “mutual control”
Communication between human and agent becomes far richer in content and possibly more frequent
Increasing capability of intelligent systems to model and understand humans, and change their behavior accordingly
Fundamental changes required in human-machine interface
Claim: the only way to avoid adding human workload is to make human-agent interaction “more natural”
Natural language (subsets; mini- or task-language) Multi-modal (speech, gestures, body language …) Mixed-initiative interaction Social
Questions? Thank you!
Autonomous Systems Tutorial: Part II
8. Robotic Systems
David J. Atkinson, Ph.D Senior!Research!Scientist
Overview • • • • •
Why talk about robots? Situated cognition and robots Androids Human Augmentation Technical Challenges
Why talk about robots? • An autonomous agent is a system situated within and part of an environment – – – –
It senses that environment It acts on it This changes what it will sense in the future This changes how it will behave
• An environment can be entirely informational, physical, or a blend of the two – Informational (Intelligence analysis agent) – Physical (Warehouse robot) – Blend (Autonomous reconn UAV)
• A robot is simply an agent that can (directly) sense and affect the physical world – Stationary or mobile – Unitary or distributed multiplicity
Why (cont.) • A great many autonomous agents that interact with humans will be robots in this sense – More generally, we refer to them as cyber-physical systems – Embedded in common objects, we won't recognize them as robots
• Examples of cyber-physical systems – – – – –
Smart cars that drive themselves Embedded medical devices Sensor networks iRobot's “Roomba” ...many more
Robotics transforms the connection of computers to the real world – Today, the “World Wide Web” is mostly about human-provided information; a communications and storage medium – Robotic sensing and perception, with multiple sensor types, will be a new source of information and enable many applications
Cyber-Physical Agents: Types Robots with high degrees of autonomy: Same characteristics as software agents ●
Persistent, Self-activating, Independent, Authoritative
Important sub-types: – Mobile USV, UGV, UAV …
– Stationary Factory assembly, surgical assistant, sentry
– Bio-mimetic Inspired by, and may replicate the form and function of living things: (humanoid, animal, insectoid)
– Exoskeletons Upper, lower, whole body, individual limbs
This is Not Science Fiction
Situated Cognition and Robots • Cognitive Robotics studies cognition and autonomy in the context of the informational and physical environment, including the computational and physical limitations of an embodied intelligent agent • Goal: Endow robots with high-level cognitive capabilities: – Perception, attention, anticipation, planning, reasoning about other agents, reasoning about their own mental and physical states
• Based on theories of “embodiment”, “bootstrapped learning” – Cognition requires real-time interaction with the real world – Requires ability to (internally) improve without actually being present “decoupled abstract thought” – The ability to think abstractly depends on experience coping with the real world, models of the world and models of itself
Example Challenge Associating semantics with sensed data. – Can see an “obstacle” but not recognize what it is (DARPA Challenge: It was a bush and the autonomous vehicle could easily have driven over it!)
Sensing limitations often result in misclassification because a bootstrapped cognitive robot doesn't yet have knowledge to constrain hypotheses about what it perceives – This is where supervised learning and interaction can help – ...just as it does with human infants!
Androids • A humanoid form facilitates the use of robots in environments designed for people, and facilitates interaction with people • People respond to androids socially at an unconscious level – Eye gaze – Reaction to mimicry, facial gestures, smiling, nodding – Treat as “third person” as bystander in human-human interactions – Animacy (appearance of sentience) helps communicate “emotional” state
• Acceptance, likeability, perceived competence … all influenced by the shape of the robot
Android Examples Robonaut
CB2
Geminoid DK
Would you trust these robots?
RI-Man
ReplieeQ2 Aldebaran
How realistic is good?
- Masahiro Mori (1970)
Human Augmentation Exoskeletons • “Wearable” robot • Augment muscles in limbs, torso • Human moves normally, system anticipates and follows • Several companies building them for different roles – Logistics – Mobility – Combat
Sarcos XOS 2
• Limited autonomy, but teleoperation and autonomous “take me home” capabilities are being developed • Legs-only, arms-only versions • Prosthetics
A Few Technical Challenges • Perception: – We don't yet have general-purpose algorithms that can recognize an object never seen before as an instance of a known class.
• Manipulation – Robots are not very dextrerous and fine manipulation is still very difficult
• New Sensors – High price and poor availability of useful sensors (dense touch, 3D range, RF and capacitance sensors)
• Awareness of people – Robots do not yet have an intrinsic awareness of people in their immediate proximity
• Interaction – Not yet able to interact with them in cognitively easy ways
Observations • A robot may be a manifestation of an autonomous agent • The physical form of a robot is largely dictated by task, environment and required function • The physical form of a robot as part of the interface to an autonomous system is less well understood • Androids have some unique advantages and disadvantages which are only recently being articulated • Androids may be especially useful where elicitation and management of empathic response in human “users” is required
Questions? • Thank you!
Tetsuwan Atomu (“Astro Boy”) , 1952, Japan
Autonomous Systems Tutorial: Part II
9. Military Applications
David J. Atkinson, Ph.D Senior!Research!Scientist
Topics Example Requirements Military Application Domains - Airbase Operations - Intelligence - Logistics - Flight Operations - Training How will life change? Technical and other challanges
Observations • 65 countries now use military robots or are in the process of acquiring them • Control schemes vary from tele-operation to semiautonomous depending on task and mission – All are candidates for greater autonomy
Example Requirements • Long-term independent operation – Transit long distances, detect, assess and avoid threats, report back
• Adaptive Functionality – Recognize threats, respond, replan to complete mission – Flexible to changing mission requirements, dynamic adversaries
• Minimize real-time telecommunications – Push signal processing and decision-making to lowest level where it can be successfully accomplished
• Weaponize within constraints of law, precedent, and procedure • Common control and interoperability • Cooperative / Collaborative coordination among multiple heterogeneous systems (autonomous and man-machine) • Minimize frequency and complexity of operator interaction • Allow human operators to interact with the system on multiple levels, in a variety of roles • Operator “on-the-loop” cooperative planning
Military Application Domains •
Airbase Operations – Emergency First Responder – UXO Response – Security
•
Command & Control – Assess, plan, act
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Logistics – Supply Chain – Warehousing and Distribution – Convoys
– Weapons Handling – Aircraft Support – Airfield Maintenance – Terminal Airspace Operations •
•
– Refueling – Delivery of Munitions
Intelligence – Tasking
– Search and Rescue – Ground Forces Support
– Reconnaissance – Surveillance – Analysis – Modeling
Flight Operations and Combat – Collision avoidance
•
Training
Airbase Operations
Aircraft Support
Weapons Handling
Emergency First Responder
UXO Response
Security
Terminal Airspace Operations
Aircraft Support • Fueling • Maintenance • Battle Damage Assessment
AFRL/RX Robotics Roadmap (2009)
Weapons Handling • Weapons Build-up • Transportation • Loading
XOS 2 Exoskeleton, Sarcos – Raytheon - to be deployed by 2016
First Response • First Responder Robotic Support (AFRL/RX) – – – –
Hazardous area search & rescue Medical evacuation Close-in firefighting CBN agent neutralization
AFRL/RX Robotics Roadmap (2009)
UXO Response • Automated UXO Response (AFRL/RX) – Investigate and eliminate explosive threats including UXOs, IEDs on runways, at entry control points, and clear ordnance from ranges. – Multiple cooperating UGVs to detect and dispose of UXOs
Security • Integrated Base Defense (AFRL/RX) – Integrated air, sea and ground robots – Conduct stand-off adversary challenge, identification, delay/denial and neutralization – High degree of autonomy to perform tasks independently
Security • Protector (Singapore) – USV for protection against suicide boats – On-board munitions (explosives, guns) – Designed to investigate a suspicious boat, provide warning, and attack if necessary (currently tele-operated; autonomy planned)
• SGR-A1 (S. Korea) – – – –
Semi-autonomous gun tower for guard duty on defensive lines Optical, laser and thermal sensing, voice recognition LMG, grenade launcher, gas canisters Autonomously detect human targets to 4km, track at 2km, fire on target autonomously or with human in-loop.
Terminal Airspace Operations
Maybury (2011) “Remotely Piloted Aircraft” Unmanned Vehicle Systems Conference
Intelligence • • • • •
Tasking Reconnaissance Surveillance Analysis Modeling
Autonomous Tasking Earth Observing-1 (NASA) One node in space/ground sensor web
Autonomous capabilities:
Recognize features of interest in Land, ice, snow, water, thermally hot Recognize change relative to previous observations Flooding, volcano, ground deformation On-board wide-area “search” for interesting features On-board decision-making to re-task sensors to specific targets Downlink only data of interest
R. L. Sherwood et al., “Intelligent systems in space: the EO-1 Autonomous Sciencecraft,” 2005.
Autonomous Reconnaissance MAGIC 2010 (ARL, TARDEC, …) Challenge: cooperating autonomous robot teams that can execute an intelligenced, surveillance and reconn mission in a dynamic urban environment
**Very difficult challenge! Accelerated UVS technologies for: - Task allocation, multi-UVS control machine intelligence, tactical behavior dynamic planning, data/sensor fusion HMI, multi-aspect SA, and more
Autonomous Surveillance Typical requirement is “constant stare”: the ability to surveil a target area (near) continuously
SWARM II (Australia DSTO)
Brian D. O. Anderson, ANU/NICTA
Autonomous capabilities: Autonomous multi-vehicle formation and control
Cooperative passive radar/emitter localization
Sensor network self-localization (partial GPS denial)
Autonomous Control Capability •
Understands the commander’s intent with respect to missions / objectives
•
Understands the battlespace (including events, activities, entities, and networks of entities) based on data that it has collected or to which it has access through other sources
•
Assesses this knowledge in order to determine what the shortfalls and threats are in the knowledge of the battlespace and threats therein relative to the commander’s intent
•
Optimally (with regard to resources, time, and significance) determines / evaluates options for courses of actions and self-tasks specific components of the sensor(s) network to address these shortfalls and threats
•
Executes the taskings while adapting to changing conditions and being self-aware and team-aware
•
Alerts appropriate forces or commands to engage critical threats
ONR
Logistics • • • • •
Convoys Convoy Escort Airlift / Mobility Supply Chain Warehousing and Distribution
KMAX cargo helicopter
Autonomous Convoy Convoy Active Safety Technology (CAST) Lockheed Martin Corporation
Builds on best results from DARPA Challenges “Hen and Chicks” model: - one driver everyone else follows!
Airlift / Mobility Flight Manager Assistant (SRI and CMU) For Air Mobility Command and AFRL (Integrated Flight Management Program
Capabilities:
Mixed initiative real-time flight management Autonomous monitoring of progress vs. schedule Autonomous responses to anomalies (when permitted) Dynamic rescheduling for globally coherent recovery and minimal disruption to other missions
Multi-agent architecture Wilkins, D.E., et al., Airlift mission monitoring and dynamic rescheduling, Engineering Applications of Artificial Intelligence (2007), doi:10.1016/j.engappai.2007.04.001
Flight Operations & Combat Refueling Collision avoidance Delivery of Munitions Search and Rescue Ground Forces Support
Aerial Refueling
Flight testing since 2006
Maybury (2011) “Remotely Piloted Aircraft” Unmanned Vehicle Systems Conference
Munitions • Wide area loitering attack munitions – Many in development for >5 years – Low Cost Autonomous Attack System (LOCAAS) • • • •
Autonomous navigation to destination Area loitering Autonomous ID of high/low priority targets Autonomous target selection and attack
• Miniaturized autonomous munitions – Target identified in “rifle” scope – Data transferred to munition when fired – Autonomous target recognition, final trajectory adjustment – concept stage?
Training Transitional Online Post-Deployment Soldier Support in Virtual Worlds (TOPSS-VW) (RDECOM) Designed to assist soldiers who are post-deployment and reintegrating to civilian life Uses virtual world technology and “virtual humans” (humanoid agents) who serve as informed guides and help each person determine what might be of most benefit to them; tutoring and mentoring Virtual Human capabilities: Natural language Natural gestures User modeling ...
Jacquelyn Ford Morie, “Re-Entry: Online worlds as a healing space for veterans“, presented at the Engineering Reality of Virtual Reality 21st Annual IS&T/SPIE Symposium, San Jose, CA. January 2009.
How Will RPAs Change?
Maybury (2011) “Remotely Piloted Aircraft” Unmanned Vehicle Systems Conference
Technical Challenges
Not just sensing → Perception in real-time for effective decision-making and action Testing → Existing V&V processes are insufficient Trust → We can't prove it won't do something bad Interoperability
An unmanned system built for the Army by one contractor cannot today seamlessly interact with another robotic system built for the Navy by another contractor.
Collaboration assumptions
All the unmanned systems have the same level of autonomy and s/w architecture; need the ability to introduce an unknown, autonomous system to a “team” without having to reconfigure all the robots
Other Challenges • • • •
• •
• •
Clash of cultures Force structure issues Inefficiencies created by duplicative activities for similar functions Coordination across current activities and domains is not robust – stakeholders unaware of other's efforts – parochialism Pockets of advocacy but no broad spectrum acceptance – no consistent top level advocacy (at Service HQ level) Trust of unmanned systems is still in infancy in ground and maritime domains. Stronger in air domain but still difficult to fly in US airspace Lack of stable and robust industrial base Shortage of qualified engineers
Conclusions The needs for future military systems drives well beyond today’s familiar deterministic sequencedriven, centralized software systems towards highly distributed and intelligent systems that are capable of functioning independently as an element of a coordinated team and in close partnership with one or more humans. Such systems are likely to manifest autonomy (self control) and inevitably will become “robotic” in the sense that they are given the capability to directly interpret – and control – sensors, and to take action in the world.
Questions?
Autonomous Systems Tutorial: Part II
10. Challenges & Predictions for Autonomous Systems David J. Atkinson, Ph.D Senior Research Scientist FL Institute for Human and Machine Cognition
Copyright © David J. Atkinson 2012
Topics Review of major challenges for autonomous systems Predictions, especially with respect to the human element Conclusion and Discussion
Autonomous System Challenges Agents Knowledge Reasoning Planning Task Execution (Behavior) Learning Human-Agent Interaction Robots / Cyber-physical Systems Applications
Review: Agents An autonomous system is an agent ● ●
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an intelligent system capable of modifying the manner in which it achieves objectives May include significant automation May reside and act entirely in the cyber world, or be embodied in a device such as a robot May consist of many sub-agents, local or distributed
Key Characteristics Persistent :
Running continuously, monitoring context
Self-Activating : Recognize when they are needed, may be invoked Independent :
Do not require user interaction
Authoritative : May invoke tasks, communicate, invoke other agents
Challenges: Agents Key architectural challenges include: ● ● ● ● ●
Specialized agents vs. general collective behavior Explicit cooperation vs. emergent behavior Controlled vs. trained behavior Programmed vs. bootstrapped knowledge “Best” knowledge representations ●
… getting multiple representations to play nicely together
Challenges: Agents ●
Scaling: Current agent-based intelligent systems may be composed of thousands of sub-agents ●
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Will these architectures scale to tens, hundreds of thousands of agents? Millions? No one really knows how a large “community” of distributed, empowered intelligent agents will work in practice Emergent behavior is likely, although it is difficult to characterize (good, bad, a mix)
Key issues related to inter-agent communication and interoperability remain to be solved ● ●
Some standards are appearing Especially important since a wide variety of architectures have been developed and more will follow
Review: Knowledge The intelligence or rationality of an agent is a function of the scope of its knowledge, the quality of its heuristics, and its logic and efficiency in applying these to problems The choice of internal representation of knowledge, and how it is organized, makes certain types of reasoning easier, but may make others harder Types of knowledge important to autonomous systems: Declarative :
Statements of fact (beliefs)
Procedural :
How to perform tasks (skills)
Semantic :
Relations of objects, situations (conceptual)
Episodic :
Entities and events encountered (cases)
Meta-Knowledge :
The agent's own capabilities (self)
Challenges: Knowledge ●
Gaining, encoding and appropriately using knowledge is a bottleneck for development of intelligent autonomous systems ●
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Knowledge engineering is tedious, prone to error, and insufficient to provide the knowledge needed for applications of significant complexity Machine learning is absolutely required
Three questions dominate: 1) What knowledge is important? Facts and features are potentially infinite even for small domains 2) What is the “best” symbolic representation? The choice(s) made can make reasoning easy – or not 3) How can we transform knowledge representations? (functionality Copyright follows form) © David J. Atkinson 2012
Review: Reasoning A process that applies knowledge to solve problems Many different types: Logical, heuristic, probabilistic, decision-theoretic, case-based, … Each type of reasoning process has strengths and weaknesses and may be better suited to some types of problem-solving than others An autonomous agent may use multiple types of reasoning processes, and may adapt how it reasons by learning from experience Copyright © David J. Atkinson 2012
Challenges: Reasoning ●
Computational complexity! ●
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Constraints must either be imposed by design, by a human, or learned from experience
Although formal logic is preferred because of the guarantees it offers, all application systems contain work-arounds, heuristics etc. that dilute confidence ● ●
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Reasoning must be constrained since the algorithms are combinatorially complex
When is “good enough” good enough? Self-correction, via learning, is required.
The process of reasoning algorithms are difficult to understand, even for experts ●
Sometimes quite a bit of analysis before we understand why a conclusion is reached Copyright © David J. Atkinson 2012
Challenges: Reasoning ●
Logic: The ability to retract beliefs and conclusions when something thought to be true is no longer true requires very complex belief management algorithms ●
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Good in theory, limited experience in scaled up applications
Probabilistic Reasoning: Powerful, but a work in progress ● ●
Inability to know prior probability distributions hard to explain reasoning over distributions of distributions!
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Reasoning under uncertainty remains #1 research topic
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Very difficult to explain a non-linear decision-making process ●
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Acceptance will require explanations
Intelligent systems have no common sense Copyright © David J. Atkinson 2012
Review: Planning ● ●
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Planning is a type of problem-solving Requires structured representations of the world , actors, and actions : how will things change? Planning is about hypothesizing the possible actions and their consequences which are necessary and sufficient to accomplish goals Planning relies upon search algorithms, of which there are many types Domain-independent planning is undecidable in some cases, and usually exponentially complex
Copyright © David J. Atkinson 2012
Challenges: Planning ●
Methods for goal conflict resolution widely vary, are sometimes hard to explain, and may not match user expectations ●
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Hard to explain the reasoning process when concurrently working towards solution of multiple goals ●
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Humans solve problems very differently
Constant battle of amount of data vs real-time requirements ●
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Priorities, must-haves, nice-to-haves can be difficult to express clearly and to represent operationally
Computing power has risen enough that planning can now be applied to problems of realistic size, and can be executed “in the loop,” Fast enough that re-planning can occur in real time as new sensor data arrives. Challenge: Balance between pre-planning and re-planning
Procedural reasoning systems work well, but ... ● ●
Bias is to “force fit” procedures to a given situation Tendency towards local maxima because of means-ends analysis – can't see the best solution, or that there is no solution Copyright © David J. Atkinson 2012
Challenges: Behavior ●
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Intelligent systems, especially cyber-physical systems such as robots, do not necessarily behave in a manner that matches human expectations. They can be “quirky” Sequences of activities, although “logical”, are not obvious Behavioral / Reactive Architectures are powerful, but may give rise to unexpected emergent group behavior ● ●
Sometimes startling, especially when learning is involved Example: “Cooperative” agents sharing a resource learned how to deceive other agents to maximize their access to resources (Wagner 2010)
Copyright © David J. Atkinson 2012
Challenges: Behavior Divergence from expectations produces unease in people interacting with an unintuitive system.
Copyright © David J. Atkinson 2012
Challenges: Learning ●
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Computer agents must be proficient at assimilating and synthesizing sensory and other sources of data into useful conceptual information Measures of “interestingness” are needed ●
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A data mining process may generate thousands of patterns from data. The challenge is discriminating the most useful or "interesting" patterns from those that are trivial or well known.
Many, if not most, powerful learning algorithms are not amenable to human “guidance” ●
Hard to see where human input can make a useful impact
Copyright © David J. Atkinson 2012
Challenges: Learning ●
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Associative and statistical machine learning algorithms and results are hard to explain Ontologies that are automatically constructed can be very obscure – Results are widely distributed; not a compact representation – The conceptual categories, relations, etc. are a product of the agent's experience, not ours
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Long-lived learning – There are no learning systems that continue to learn over extended periods of time – we have no idea what will happen
Copyright © David J. Atkinson 2012
Challenges: Interaction ●
So much of human interaction “works” because our thinking processes are fundamentally similar. ● ● ●
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Is mimicking human social behavior required for acceptance? Autonomous systems must not be annoying!
Difficulty (or impossibility) of explaining what it is doing ● ● ●
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Machines are “alien”, with different reasoning processes and experience
Knowing what is important to say and when Level of detail, abstraction, complexity Lack of common semantic references, e.g., probabilistic models of geometry of world have little in common with how people think of the environment Not known how to explain non-linear decision-making processes
These mismatches make it difficult for untrained humans to make the “mental leap” necessary to understand the “state” of an autonomous system and how to command it for a wide Copyright © David J. Atkinson 2012 range of tasks
Challenges: Interaction ●
Statistical models of resolving ambiguity in natural language break down ● ● ●
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Some tasks simply cannot be interactive ● ● ●
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when the number of users increases Task complexity increases User requirements are more than minimally expressed
Time and complexity considerations “In-the-loop” is obsolete “On-the-loop” may be dreaming since knowledge and reasoning processes are difficult to explain Can we delegate and simply accept the results?
The main issue will be about delegation of authority Copyright © David J. Atkinson 2012
Challenges: Androids Although “substitution” may be a myth, androids are very popular and may be very useful as an “familiar” interface to an autonomous system in human environments How realistic is good? – – – – –
Expression Movement Gaze Manipulation Speech
Copyright © David J. Atkinson 2012
“HRP-4C”, from NAIST (JP) 2009
http://www.youtube.com/watch?v=xcZJqiUrbnI
Challenges: Military Applications The major challenge is not capability, it is the decision of whether or not to use it Autonomous systems can not be tested, verified, validated using traditional methods – other ways of assuring “trustworthiness” are required Culture, force structure and a host of related ops issues Many ethical and legal issues with weaponizing autonomous robotic systems are unresolved Copyright © David J. Atkinson 2012
Predictions
“Prediction is very difficult, especially about the future.” Niels Bohr Danish physicist (1885 - 1962) Copyright © David J. Atkinson 2012
Autonomous Agents ●
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Agents represent a paradigm shift from previous notions of computational interaction This will accelerate the trend towards decentralization of control New properties of complex systems will emerge from the aggregate behavior of many hundreds of thousands of agents, each acting to achieve their purpose Agents will help systems become more robust due to their distributed nature, ability to invoke or “persuade” other agents to achieve goals Agents will make systems harder to understand and predict at low level than today, which is already very difficult Proving “correctness” will become even more challenging; maybe impossible due to emergent behavior
Copyright © David J. Atkinson 2012
Personalization Companion, Major Domo, Executive Assistant, Wingman, Counselor ... The most capable intelligent autonomous systems will be effectively personalized to one person or at most a very small group of people – – – – – – –
Training and learning Shared experiences Disambiguation of language Conversational and social “style” Problem-solving style Mutual mental models Personality Copyright © David J. Atkinson 2012
Autonomic Functions An autonomous system be able to: ●
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Understand itself in terms of access to resources, its capabilities and limitations, and the method and purpose for its connection to other systems Automatically reconfigure itself as a function of a dynamic computing environment Optimize its own performance Identify and work around any faults or problems by repairing itself or otherwise reconfiguring its functions Maintain its own integrity and security when faced with attack Adapt to its environment, interact with other systems and establish communication protocols.
Copyright © David J. Atkinson 2012
Learning ●
It will not be necessary to pre-program autonomous systems with every skill they will need ●
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Any information available electronically (i.e., Internet) will be available for learning ●
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Autonomous systems will interact with people and each other to learn about their environment, their roles, to develop their skills and continually adapt
To the extent required to achieve goals, autonomous systems will use this source of knowledge
Autonomous systems will learn by observation, by experience, by being taught, by asking, by reading, and by sharing their knowledge with one another If one learns, they all learn Copyright © David J. Atkinson 2012
Cyber-Physical Systems Robotics transforms the connection of computers to the real world – Today, the “World Wide Web” is mostly about human-provided information; a communications and storage medium – Robotic sensing and perception, with multiple sensor types, will be a new source of information and enable many applications
Cyber-physical systems conceptually replace notion of embedded systems Emphasis is on networking, cooperation, and coordinated interaction among physical elements of a system, not just standalone computation in a device device
Affect and Emotion ●
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Affect plays an important role in human interpersonal relations Machines have no emotions; they “feel” no affect, whether positive or negative They will, however, understand the function of affect and be able to portray emotions when interacting with humans People will attribute emotional states to autonomous systems because it is our nature to do so
Appropriate emotional behaviors by autonomous systems will ease acceptance Copyright © David J. Atkinson 2012
Cognitive Entities Autonomous systems will become “cognitive entities” ●
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They will have goals that we give them and goals of their own The will have beliefs about themselves, the world, and other entities They will sense and interpret the world, reason in many different ways about their beliefs, and act purposefully to achieve goals They will learn constant and quickly adapt They will interact with humans and with each other to perform significant actions in the world They will almost never be “turned off”.
Social Challenges
Copyright © David J. Atkinson 2012
Social Challenges
Copyright © David J. Atkinson 2012
Bottom Line Autonomous Systems will change the nature of work at least as much as automation has already Almost any job, role or activity will be (increasingly) subject to automation by intelligent autonomous systems Answer: Teamwork
Copyright © David J. Atkinson 2012
THANK YOU!
David J. Atkinson
[email protected] Florida Institute for Human and Machine Cognition
Copyright © David J. Atkinson 2012
