MULTI-AGENT MODELLING FOR AUTONOMOUS BUT COOPERATIVE ROBOTS Norbert Glaser, Vincent Chevrier & Jean-Paul Haton CRIN-CNRS/INRIA Lorraine BP 239, 54506 Vandœuvre, France Norbert.Glaser, Vincent.Chevrier,
[email protected]
Keywords: Autonomy, Cooperation, Multi-Level Interaction Abstract
Modelling a society of cooperative and autonomous mobile robots can be viewed as a multi-agent issue, i.e., the task to determine a multi-agent architecture for a robot society according to this two requirements. In this paper, we motivate a multi-layered approach to fulfill cooperation and autonomy. We propose an agent model together with an interaction model based on this approach. Features of this model are the clear distinction between social and individual capabilities and knowledge, and the possibility to provide interactions at several levels of abstraction to guarantee cooperation between agents.
1 INTRODUCTION Modelling a society of cooperative and autonomous mobile robots is a multi-agent issue, i.e., the task to determine a multi-agent architecture according to specific requirements of such a problem definition. In this article 1 , we propose an agent model and an interaction model for autonomous and cooperative robots. We denote cooperation as the capacity of an agent to change its own goals to achieve global ones. Autonomy concerns the capacity of an agent to decide for its goals and to achieve them by itself. Autonomy does not imply the existence of other agents and communication is found to be reduced in an autonomous agent society. The achievement of a goal which might be also treated simultaneously by several agents - is tied to the individual skills of an agent. Cooperation is necessary when global goals cannot be achieved 1
This paper does only present the motivation and a short outline of our approach without having the possibility to go into details.
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individually, or individual goals can be achieved with lower costs collectively [Adler et al., 1992]. Costs may be evaluated through the total number of actions and interactions between agents. In a multi-robot framework, an equilibrium has to be reached between autonomy and cooperation.
2 MOTIVATION Agents are intelligent and autonomous entities which act on their own in using their individual knowledge. Moreover, they build social organisations with other agents to enhance collective efficiency. Thus, agents need both individual and social capacities and knowledge. Figure 1 illustrates some individual and social characteristics of agents which may be found in a mobile robot society. Social Capacities cooperation communication interaction
Cooperative Robots
Multi-Agent Systems
Individual Knowledge
Social Distributed Knowledge control knowledge Problem-Solving role solution plans commitments, beliefs world maps protocols, primitives behaviors Distributed planning ressources Systems navigation & obstacle avoidance task solving secure mecanisms, perception
Autonomous Robots
Individual Capacities
Figure 1: Knowledge and Abilities of Agents. The abilities of an agent are drawn on the vertical axis and the required individual and social knowledge on the x-axis. Individual abilities describe the autonomic functionality of an agent and social abilities describe its capacity for cooperation and its social behaviour.
Individual capacities allow an agent to plan, to navigate, to solve tasks and provide it with certain secure mechanisms. An agent has individual knowledge to perform these activities. As part of a multi-agent society, an agent needs social abilities and social knowledge to cooperate, communicate and interact with its counterparts. Social knowledge and abilities are required by agents to determine their organisation and their own roles for later cooperation. A multi-robot society has to fulfill two criterias : first, maximal autonomy for an individual robot to guarantee that it has the capacities and resources to perform most 2
of its tasks by itself, second, robots need to cooperate because of incomplete capacities, incomplete knowledge or limited resources. They require social knowledge and cooperation abilities. This implies minimal agreement on, for example, a common knowledge representation and a common communication language [Bobrow, 1991]. We propose a multi-layered agent model and present multi-level interaction for cooperative robots.
3 A MULTI-LAYERED MODEL We propose a multi-layered agent model responding to the previously mentionned social and individual needs of a multi-agents society. Our model is composed of six layers (see Figure 2). Social knowledge and capacities allow an agent to reason about the structure of the society and to determine the needs for the underlying cooperation layer. Agents interact on the cooperation layer according to the static structure of their society, they may not modify their roles and may not choose other cooperation mecanisms.
Control Layer
Conflict resolution methods Communication protocols and primitives Pattern of behaviors
Tasks Strategies
Behaviors
Methods
Reactive Planning Knowledge
Social Layer
Cooperation Layer
Cognitive Layer
Reactive Layer
Meta-level reflection about cooperation and organisation Robot-robot and robot-control station communication
Planning for navigation and task solving Learning
Basic navigation
Resources, Sensors, World Model
Perception Layer
Sensor data filtering, Perception
Security and recovery mechanisms, Error protocols
Integrity Layer
Reinitialisation and Recovery
Agent Knowledge
Interaction Layer
Role, Commitments, Beliefs Model of other agents and their roles
Agent Capacities and Abilities
Figure 2: The Multi-layered Agent Model.
We are able to distinguish between social and cooperative abilities on one side, and individual ones on the other side. Integrity is an aditional individual ability which is required by a real-world application to guarantee functional requirements, for example, stability and fidelity. The social layer describes social aspects about an agent, (e.g., its role in the agent society, its commitments to the other agents, etc). Social knowledge al3
lows agents to decide collectively their roles and their organisation. The cooperation layer describes the possible cooperation modes of an agent with its environment and contains three knowledge sources : communication and cooperation primitives, communication and cooperation protocols and coordination algorithms. A cooperation protocol by the agents using their social knowledge or capacities (dynamic choice) or by the designer of the system (static choice). The cognitive and reactive layer represent the cognitive and reactive knowledge of an agent. Cognitive knowledge represents an agent’s problem-solving capacity and its experience or task knowledge [Glaser and Haton, 1995]. Reactive knowledge is represented in form of simple behaviours (simple precondition-action pairs), complex behaviours and patterns of behaviours. The perception layer describes the required resources, the perception system and filter and preprocessing mecanisms. The integrity layer describes mecanisms of an agent to recover from error situations in sending low level signals for support from other robots or from the human operator. Agents may exchange configurations on this level to support the setup of another robot after its breakdown. The two vertical layers contain knowledge and functionalities which are common to or used by serveral layers. The control layer contains methods to control the problem-solving process and of the execution of behaviours. The control knowledge for problem resolution is represented by task-oriented control methods. The interaction layer allows to interact with other agents on several horizontal layers of the agent model.
4 MULTI-LEVEL INTERACTION FOR COOPERATION Agents can cooperate on various levels to achieve common goals : joint-planning, sharing processing capabilities, exchange of knowledge sources [Werner, 1992]. Figure 3 presents the various levels of cooperation between two autonomous agents. The interactions have been identified with respect to the types of knowledge and of capacities at the various layers of the agent model. The lowest level of interaction deals with robot integrity (very low battery tension, hardware failure, etc). We assume that the robot cannot communicate at this level and interaction is based on hardware procedures. At the perception level, robots can just transmit sensor inputs on a request from another robot. At the reactive level, robots perform basic actions depending on its perception but also on a request which it has received from another robot. At the cognitive level, a robot can perform individual 4
INTERACTION LAYERS 5
Social Level
4
Cooperation Level
3
Cognitive Level
2
Reactive Level
1
Perception Level
0
Integrity Level
Roles Joint Plans Plans, Goals
Social Level Cooperation Level Cognitive Level
Actions Data
Reactive Level Perception Level
Signals Integrity Level
Figure 3: Possible Interactions between Agents. This Figure illustrates the possible exchanged information between two agents on the various interaction levels.
action plans to achieve some non-trivial goals. It can exhibit expert behaviour during the execution of its plans. However, it cannot handle conflicts which may arise with other robots. The robot can be qualified as a specialist agent [Erceau and Ferber, 1991]. At the cooperation level, a robot knows its role in the society and knows its acquaintances and the cooperation protocol that it should use. It can solve conflicts and manage the allocation of tasks. However, it does not possess the ability to change its roles, nor the organisation, nor the protocols it uses. Its knowledge about the society is static. Interaction at the social level allows full social behaviour. Agents can negotiate the assignment of roles, can dynamically change their organisation and can agree on common interaction protocols.
5 DISTRIBUTION FOR AUTONOMY The requirement for autonomy is important for the design of an architecture for an agent society. We distinguish between autonomy of action and autonomy of resources. The first one aims to guarantee the independence of the operationability of an individual agent with respect to its capacities and knowledge. The second is a technical constraint which aims to guarantee the necessary resources for an agent to accomplish its tasks according to user or system requirements. We focus in this part only on the second criterion for the design of a mobile robot society. The operationability of a single robot is restricted by the limited on-board computation power, memory space and battery power. It seems not reasonable to implement energy-costly computation techniques on-board [Gat et al., 1994]. Planning 5
and reasoning are often expressed in languages (such as Lisp or Prolog) that are CPU and memory consuming. Reactive behaviors, perception and integrity are more tied to the functionalities of a robot and its physical characteristics. Physical distribution reinforces the autonomy of a mobile robot in a way that the computational resources of the robot are used for integrity, perception and reactive behaviour. The multi-layered agent model which describes all the low and high level knowledge and activities of a robot can be physically distributed between the mobile platform and a remote control station. We do not propose absolute criteria to determine which layers have to be on-board and which have to be on a remote control station. This decision depends mainly on the computational capacities of the mobile platform towards to the energy consummation of each layer.
6 AN EXAMPLE To illustrate the usage of our proposal for a multi-robot society, we will briefly describe three problems for such a society (see Figure 4). This Figure shows potential cooperation and communication links between two robots. We describe how are handled three kinds of failures. REMOTE CONTROL STATION Social Layer
Social Layer
Cooperative Layer
Cooperative Layer
Cognitive Layer
Cognitive Layer
Interactions
Problem A: Disturbed radio link
Radio links ROBOT 1
Problem B : Loosing the higher layers
Reactive Layer
Reactive Layer
Perception Layer
Perception Layer
Integrity Layer
Integrity Layer
ROBOT 2
Problem C: Low battery tension
Figure 4: Cooperative Robots. Examples of failures in the distributed architecture.
We consider a first problem concerning the disturbance of the communication channel between a robot and its higher layers (Problem A). Since second robot detects the impossibility to communicate with its higher layers, it first tries to establish a new connection by using the radio link of the other robot. If it succeeds, it will use this temporary connection until it can enable its own one. Otherwise, the impossibility to reconnect is equivalent to the lost of its higher levels (Problem B). Thus, it will 6
behave according to the higher on-board layer, that is in our case, the reactive one. The robot will adapt a cooperation habitude through interaction. It will react to basic action requests by executing them and indicating their results. The last problem (Problem C) is a low battery tension. Hardware procedures are activated and consist in broadcasting a signal on a specific frequency. When another robot detects this emergency broadcast, it will transmit this to the global control station which will undertake appropriate procedures for repair.
7 RELATED WORK Recent work on agent models and multi-agent architectures concentrates in general on agent cooperation and communication [Chevrier, 1994; Demazeau et al., 1994; Montgomery and Durfee, 1990; Wittig, 1992]; other approaches focus on the internal state of agents [Shoham, 1993], or propose architectures characterised by the integration of reactive and plan-based components [Gasser et al., 1987; M.Pischel and M¨uller, 1994]. The model is thereby designed for less or more autonomous agents.
8 CONCLUSION This paper investigates the requirements for the modelling of autonomous but cooperative robots. We use a multi-agent approach and propose a layered agent model with multi-level interactions between agents. We study the role and importance of social knowledge for agents to determine their organisation and their cooperation protocols. We recognise the fact, that our model is a more conceptual one that abstracts from implementation details. Features of our agent model are the possibility to provide interactions at all layers to guarantee the cooperation between agents. We propose a physical distribution of the layers between a mobile platform and a control station for autonomy purposes. This is reasonable since a powerful control station is better suited for higher level computation and energy expensive techniques. Moreover, high level techniques remain often too experimental to be implemented on-board. High level languages are used for simulation and test of complex experiments. The lower levels can be clearer defined. Experiments at these levels may be less expensive or can be realized by simpler routines which guarantee a suboptimal but sufficient behaviour of a robot. Another advantage of our model, because of physical distribution, is the transparence from technical characteristics of each particular robot. The lower layers upto the reactive one depend more on the technical equipment of a robot while the same higher levels may be used by several agents. 7
9 ACKNOWLEDGEMENTS We are grateful to the European Union (E.U.) for partial funding of this work through the Human Capital and Mobility program as doctoral fellowship.
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