The Role of Obligations in Multiagent Coordination Mihai Barbuceanu
Enterprise Integration Laboratory University of Toronto 4 Taddle Creek Road, Rosebrugh Building, Toronto, Ontario, Canada, M5S 3G9
[email protected] Tom Gray and Serge Mankovski Mitel Corporation 350 Legget Drive, P.O. Box 13089 Kanata, Ontario, Canada K2K 1X3
ftom
gray,serge
[email protected] Abstract
Carrying out distributed business processes over networks is rapidly shifting the nature of application architectures from the simple command and control client-server model to complex peer-to-peer models supporting dynamic patterns of social interaction and behavior among autonomous, proactive, goal oriented agents. Trusting agents to autonomously make decisions and execute actions on behalf of humans, as part of global business processes, requires both understanding and modeling of the social laws that govern collective behavior and a practically useful operationalization of the models into agent programming tools. In this paper we present a solution to these problems based on a representation of obliged and forbidden behavior in an organizational framework, together with an inference method that also decides which obligations to break in con icting situations. These are integrated into an operational, practically useful agent development language that covers the spectrum from the de nition of organizations, roles, agents, obligations, goals, conversations to inferring and executing coordinated agent behaviors in multi-agent applications. The major strength of the approach is the way it supports coordination by exchanging constraints about obliged and forbidden behavior among agents. We illustrate this and the entire system with solution examples to the feature interaction problem in the telecommunications industry and to integrated supply chain management.
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1 Introduction Current application software typically stores and processes `data' under clientserver architectures that allow distributed clients to access the content of centralized servers. While this is appropriate for many applications, new network based forms of business processes, including global manufacturing, concurrent engineering, customized service provisioning in telecommunications, electronic commerce, etc. require capabilities that go beyond the state-of-the-art client-server, object oriented or relational architectures. A common theme that emerges from these new applications is that in a decentralized world of autonomous, proactive, goal oriented entities, it is essential to capture, support and enforce complex patterns of social interaction and behavior that enable participants to coordinate and collaborate in order to achieve their own goals as well as maintain the coherent behavior of the system as a whole. Software agents, in their many incarnations [Nwana 96], are the main technology supporting this shift. To better understand the nature of the di erence between conventional and agent architectures, let's have a comparative look at some basic features of agent oriented and object oriented models and architectures. First, agents focus on behavior while objects focus on structure. Behavior descriptions emphasize the dynamic execution of actions by agents and the propagation of change that actions generate. Structural descriptions (as supported e.g. by object oriented data models), tend to focus on representing the current state of a system, with little concern on why and how it was reached and why and how it may change from there. Agents promote autonomous (self initiated) action and decision making. This enables peer-to-peer interaction where the initiative in making requests or taking action switches freely among participants as required by the dynamics of the application. Being normally reactive (they act only in response to messages), objects are better suited to the more rigid clientserver model. Because of this autonomous and interactionist behavior, agents can only be described and understood with notions like beliefs, goals, plans, obligations, etc. Object models on the other hand are best described and understood in structural terms like attributes, relations, generalizations, etc. The highly interactionist nature of agents has also led to di erent models of communication. Agent communication languages (ACL-s), for example [Finin et al. 92], are based on well-de ned, small sets of general communicative actions that can be described beforehand and are ammendable to declarative semantics. This allows agents to easily join and work together with existing communities provided they adhere to the ACL being used. Objects on the other hand communicate through unrestricted 2
and idiosyncratic messages with ad-hoc semantics. This creates communication barriers and increases communication costs by requiring multiple semantic translations. We will not address communication issues in this paper, but references exist making the case for the importance of uniform, well-de ned ACL-s as opposed to ad-hoc message passing [Genesereth & Ketchpel 94, Barbuceanu & Teigen 98]. Finally, by scaling up notions of interactionist behavior to complex organizations and business processes, agents touch on the issue of social ability and know-how, enabling them to achieve their individual goals in a socially responsible manner. What, at the level of object systems, is described as distributed control becomes, at the agent level, social interaction. To go from structure oriented descriptions and distributed control (stateof-the-art) to goal driven behavior and social interaction (agents) requires a number of new notions and models. This paper is about one way of making this leap, by integrating - in theory and practice - models of obligation, social behavior and conversational interaction.
2 The Need for Obligations in Coordination Coordination has been de ned as the process of managing dependencies between activities [Malone & Crowston 91]. An agent that operates in an environment holds some beliefs about the environment and can use a number of actions to a ect the environment. Coordination problems arise when (i) there are alternative actions the agent can choose from, each choice a ecting the environment and the agent and resulting in di erent states of a airs and/or (ii) the order and time of executing actions a ects the environment and the agent, resulting in di erent states of a airs. The coordination problem is made more dicult as an agent has incomplete knowledge of the environment and of the consequences of its actions and the environment changes dynamically making it more dicult for the agent to evaluate the current situation and the possible outcomes of its actions. In a multi-agent system, the environment is populated by other agents, each pursuing their own goals and each endowed with their own capabilities for action. In this case, the actions performed by one agent constrain and are constrained by the actions of other agents. To achieve their goals, agents will have to manage these constraints by coordination. A popular model of agenthood that has been used to guide the production of coordinated behavior is the BDI (Beliefs, Desires and Intentions) model. In this approach, coordinated behavior is accounted for by introducing social intentional constructs like joint intentions [Levesque, Cohen & Nunes 90]. The idea is that a group of agents will knowingly adopt a common goal and knowingly plan to achieve it, thus producing coordinated and cooperative behavior. Usually some escape clauses are also added, stipulating when agents can quit the joint e ort - for example when they believe the common goal has been achieved, 3
can not be achieved, or when the motivation for the common goal has dissapeared. When any of these conditions occur, the agent detecting it is required to inform the others about it, to save the group from doing unnecessary or even damaging work. This model has been implemented in a number of systems, including [Jennings 95] for diagnosing electricity transportation problems and [Tambe 97] for coordination of military operations. While this approach has many strong points and provides an elegant solution in many simple cases, it fails to scale up to complex business situations involving coordinated work intra and inter organizations where participants do not share clear common goals or their goals are opposed. Consider for example the case of a customer issuing a Request For Quotation (RFQ) to a company, for some goods. The goal of the customer is to buy goods within a certain price range. The goal of the company is to have the customer buy goods in a di erent price range. None of them knows the other's price range, nor do they know if any transaction will eventually take place. None of them is committed at this stage to do business with the other. Clearly, there's no clear mutually known common goal. Yet the company responds and their coordinated interaction may be very complex and long lasting. The reason why they interact is that they both follow social laws that prescribe social behaviors in this situation. The buyer is normally obliged to issue several RFQ-s to several vendors, and then choose from among them. The company is obliged to respond to an RFQ either because it represents potential business that is needed or, if it is already swamped with orders, to clearly inform the customer that it can not proceed with it at the current time, but is willing to do that whenever possible (few companies that intend to stay in business can a ord to ignore an RFQ). As another example, consider the situations of an assembly line worker and the CEO of the company. They are clearly part of the same team, but what is their common goal? One could argue that although they may not have concrete common goals, they still have abstract common goals like "helping the company do well". The problem with this argument is not only that this goal is too vague and thus needs to be re ned into concrete subgoals that directly generate behavior, but that it is simply not necessary for explaining the coordinated behavior of the two agents. The worker does his job because he has an obligation to do his duties, or else he may lose the job or su er other penalties. The CEO has his own obligations toward shareholders, and he may equally lose his job, or su er other penalties, if he doesn't satisfy them. What these examples tell us is that agents' behavior within organizations is governed by social rules which impose obligations and interdictions over the agent's actions. Not ful lling these obligations and interdictions incurs a cost to pay, hence agents decide what course of action to follow by considering the relative strengths of these penalties (e.g. one may still do something that will have him red, if the situation is such that the cost of being red is not important enough - e.g. if one has just won the lottery!). For the same reason, an agent may do actions that are contrary to some of his goals, like for example a salesperson that has to provide service to a customer that he personally dislikes. The conclusion that we draw from this analysis provides the motivation for 4
this work: that coordination in organizations and societies can not be accounted for without considering the social laws of the organizations and the way they constrain the behaviors of individual agents. To make this view practically usable in applications we have to solve a number of problems related to (1) the representation of agent behavior coupled with a representation of obligation and interdiction relative to behaviors - that is a representation of obliged and forbidden behaviors within agents (2) the inference mechanisms allowing an agent to deduce the logical consequences of given obligations or interdictions for example if a choice between a and b is forbidden, does it follow that doing a or b in part is forbidden as well? (the answer is yes, as we'll see later on) (3) the resolution of con icts amongst obligations - how does an agent proceed if it is both obliged and forbidden to do an action? (4) the integration of the above solutions into an operational agent architecture. In this paper we present solutions to the above problems, based on a dynamic representation of behavior and obligation. The major bene t of the approach is a more general and exible approach to coordination as exchange of obliged and forbidden behaviors amongst agents. In a nushell, we assume that in general agents interact by requesting various things from each other and satisfy to the extent possible - each other's requests. A request of agent A to agent B can be generally described as a set of behaviors A wants B to perform and a set of behaviors A wants B to refrain from doing. In our representation, a request is thus a set of obliged and forbidden behaviors A wants B to satisfy. As B is endowed with mechanisms for representing and reasoning about obliged and forbidden behavior, it can infer the consequences of the requested obligations and interdictions, solve any con icts between these and its own obligations and interdictions (or obligations and interdictions imposed by others) and construct a behavior (set of actions) that it can execute to satisfy the request. If the agent is able to satisfy all requested obligations and interdictions, then it can plan or schedule the actions and execute them. If some constraints can not be satis ed (e.g. obliged actions can not be executed or forbidden ones will be executed), then the agent will start to negotiate with the other agent. There are many ways in which such negotiations can take place, that can be described in our system (we illustrate some of them in section 5). The general idea is that B can reveal to A which of its constraints it can not satisfy and then A can either modify its request (drop, change, add constraints) or, if it has enough authority, modify B's cost of violating some of the given constraints to determine B to satisfy them. This cycle would end successfully when a set of constraints that A desires and B is willing to satisfy is produced, or in failure when some terminating condition becomes true. The overall control of this negotiation process is not predetermined in the architecture. We provide a notion of conversation plan which can be used to specify the sequencing of exchanges, the conditions in which exchanges take place or terminate, the messages exchanged (in any communicative action based agent communication language), etc. The generality of this model becomes clear when we compare it with previous models of coordinated behavior. The contract net model for example [Smith 80] assumes a predetermined architecture of behavior where a task man5
ager announces tasks to potential bidders and then collects bids and selects from amongst them. Agents do not reason about behavior in general, they can only bid or not bid in the provided framework. In our system, contract nets can be speci ed as a particular case of conversational interactions and agents can send to each other descriptions of complex behavior that can be reasoned about in a general sense, as opposed to instantiations of the same standard task speci cation. As a consequence, if we have to implement contract nets in our system, we have leverage to modify the structure of the overall interaction as well as the form and content of the exchages between any of the agents involved. The same is true if we compare our system with the joint intentions framework. Moreover, as previously discussed, joint intentions only operate in the presence of joint goals, while our model works with di erent and even con icting ones. This generality allows us to approach application domains which other models can not handle. Two of these are feature interaction problems in the telecommunications industry and integrated supply chain management. Brie y, features are packages of functionality that provide services to subscribers or the telephone administration. The Plain Old Telephone Service (POTS) features for example o er basic means of setting up conversations and billing for sevices. Others, like Call Forwarding or Call Waiting extend the services o ered to subscribers. Feature interactions are interferences that a ect the desired operation of the feature, creating major diculties in the process of service deployment. Recent changes in the industry, like deregulation, the promotion of customized features in intelligent networks, the increasingly heterogenous nature of the telecommunications network, the distribution of the service logic among network components, and nally the convergence of computer and telecommunications industries, complicate the problem further. As we show in section 5, the problem can be addressed in our architecture by representing features as behaviors and having agents negotiate about them using the constraint exchange mechanism. The supply chain is naturally modelled as a world-wide network of `agents', such as suppliers, factories, warehouses, distribution centres and retailers through which raw materials are acquired, transformed into products, delivered to customers, serviced and enhanced. In order to operate eciently, supply chain functions must work in a tightly coordinated manner. But the dynamics of the enterprise and of the world market make this dicult: customers change or cancel orders, materials do not arrive on time, production facilities fail, workers are ill, etc. causing deviations from plan. In many cases, these events require several agents to negotiate in order to revise plans, schedules or decisions. As also illustrated in section 5, our coordination model enables us to capture and execute the multitude of social interaction patterns that help supply chain agents successfully coordinate their behaviors to achieve their individual goals as well as coherence of the supply chain system as a whole. To conclude, we assume the following model of social interaction and behavior as a basis for our coordination formalisms and technologies. 1. At the social level, societies and organizations regulate the social behavior of agents by imposing social constraints or laws, representable as networks 6
of mutual obligations and interdictions amongst agents. Not ful ling an obligation or interdiction is sanctioned by paying a cost or by a loss of utility, which allows an agent to apply rational decision making when choosing what to do. These social laws are objective forces motivating social behavior and to a large extent determine the `mental states' at the individual agent level. Agents `desire' and `intend' the things that are requested by their current obligations, knowing that otherwise there will be a cost to pay. 2. At the level of individual agent decision making, each agent in part determines what behavior to adopt in order to satisfy the applicable social laws as well as its own goals and priorities. In particular, at this level agents determine how to solve con icting obligations and interdictions, which normally occur in any real organization. 3. Next, having decided on the general behavior (in terms of what to do or not), agents need to plan/schedule the activities that compose the selected behavior. This determines the precise sequencing of actions to be executed, consistent with time, resource and possibly other constraints on action execution. 4. Finally, actions have to be executed as planned, with provisions for handling exceptions and violations. These may be dealt with at any of the above levels, for example by retrying, replanning, deciding on di erent actions or even (in an extreme case that we do not deal with here) trying to modify the social laws. In this paper we present operational, implemented solutions that address all above levels. We start with providing a representation of behavior and of obligations, permissions and interdictions and a constraint propagation reasoning method allowing agents to infer the applicable obligations and to decide among con icting ones. These form the basis of our solution to dealing with most of the issues above and are presented in section 3. The solutions are fully implemented and operational, being integrated in an agent programming language that supports agent development along the entire spectrum from organization and role speci cation, de nition of social obligations and interdictions, agent construction, proactive and interactionist agent behavior. This language is presented in section 4. The major consequence of the approach is the way it supports coordination as negotiation about obliged and forbidden behaviors. We illustrate this, and the entire system, with examples addressing feature interaction and integrated supply chain management. This is the subject of section 5. We end with conclusions, a review of related work and future work hints.
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3 Representing and Reasoning about Behavior and Obligation Intuitively, an agent a1 has an obligation towards an agent a2 for achieving a goal G i the non-performance by a1 of the required actions allows a2 to apply a sanction to a1. The sanction is expressed as a cost or loss of utility. Agent a2 (who has authority) is not necessarily the bene ciary of executing G by the obliged agent (you may be obliged to your manager for helping a colleague), and one may be obliged to oneself (e.g. for the education of one's children). Semantics. We model obligations, permissions and interdictions (OPI-s) using the reduction of deontic logic to dynamic logic due to [Meyer 88] in a multiagent framework. Brie y, we de ne obligation, interdiction and permission as follows, where V ij denotes a violation by i of a constraint imposed by j wrt action or goal (associated with a cost to be paid): F ij [ ]iV ij : i is forbidden by j to execute . An agent is forbidden to do i in any state resulting after executing the violation predicate holds. P ij :F ij : i is permitted by j to execute . Permission is the same as non-interdiction. Oij F ij (- ): i is obliged by j to execute . Obligation is an interdiction for the negation of the action (forbidden not to do ). As shown by [Meyer 88], this reduction eliminates the paradoxes that have plagued deontic logic for years and moreover, leads to a number of theorems which, as will be shown immediately, allow us to apply an ecient constraint propagation method to reason about OPI-s in action networks. Both of these are essential for applying this model to real applications. The main theorems that we use are as follows (indices dropped for clarity), where ; denotes sequential composition, [ nondeterministic choice and & parallel composition of actions. Also, [...] and < ::: > are the dynamic logic necessity and possiblity operators. j= F( ; ) [ ]F (1) j= F( [ ) F ^ F (2) j= (F _ F ) F( & ) (3) j= O( ; ) (O ^ [ ]O ) (4) j= (O _ O ) O( [ ) (5) j= O( & ) (O ^ O ) (6) j= P( ; ) < > P (7) j= P( [ ) (P _ P ) (8) j= P( & ) (P ^ P ) (9) j= O( [ ) ^ F ^ P O (10). In words, these theorems tell us that: (1) A sequence is forbidden i after executing the rst action the remaining subsequence is forbidden. (2) A choice is forbidden i all components are also forbidden. (3) If at least one component of a parallel composition is forbidden, the parallel composition is forbidden as well. (4) A sequence is obliged i the rst action is obliged and after executing it the remaining subsequence is obliged as well. (5) If at least one component of a 8
choice is obliged, the choice is also obliged. (6) A parallel composition is obliged i all components are obliged. (7) A sequence is permitted i there is a way to execute the rst action after which the remaining subsequence is permitted. (8) A choice is permitted i at least one component of it is permitted. (9) If a parallel composition is permitted, then all components must be permitted. (10) If a choice is obliged and one component is forbidden while the other is permitted, then the permitted component is obliged. While providing an understanding of what OPI-s are in a dynamic framework where agents' behavior can be described with sequential, parallel and choice compositions, this model does not allow to compare obligations in con icting situations. Next, we show (1) how the model can be given a constraint propagation formulation and (2) how in this format it can be extended to handle con ict resolution among contradictory obligations and interdictions by comparing violation costs. Deontic Constraint Propagation. To infer the consequences of given obligations and interdictions and to solve con icts amongst them we use constraint propagation over acyclic networks in which goals are connected to their subgoals. Figure 1 shows a somewhat arbitrary such network in which g1 is a choice between g2 and g3, g2 is a sequence containing g8 and g9, g3 has g4 and g5 executing in parallel, etc. If the arc connecting a goal to one of its subgoals is labelled with a `-', it means that the subgoal is negated in the goal. In gure 1, g4 is negated in g3, meaning that g3 consists of not doing g4 in parallel with doing g5. Figure 1 illustrates this process using a goal network where we have initially asserted that (forbidden g4) and (obliged g5). For each of these assertions the propagation process traverses the network along supergoal and subgoal links and applies the deontic theorems listed previously. For example, since g4 is a choice, forbidding it also forbidds all its alternatives, cf. theorem (2). This makes both g8 and g6 forbidden. Since g8 is forbidden, g2 is also forbidden as a sequence with one subaction forbidden, cf. (1). Propagating along supergoals, since g4 is negated in g3, it follows that g3 becomes obliged, having both subgoals obliged, cf. (6) (g5 asserted as such initially, and -g4 obliged because g4 is forbidden). Next, since g3 is obliged, g1 becomes obliged as well, cf. (5). As g5 has been asserted as obliged and since g6 is forbidden, it follows that g7 must be obliged, cf. (10). We note that g9 has no label, as nothing could be inferred about it. Integrating violation costs. With violation costs, the propagation process can no longer be described by Mayer's theorems in their given form. The purpose of violation costs is to allow us to compare obligations and, in con icting cases, select the course of action that incurs a smaller cost to pay. Consider gure 2 and assume g is a choice. Asserting all its subgoals as forbidden with the given qualitative costs results in propagating g as forbidden (cf. theorem 2) with a cost equal to the cost of the smallest cost alternative (this is just one possibility). If later the choice goal is asserted as obliged with a greater cost, then we propagate this upon the smallest cost subgoal. Now we have contradictory labelings for g1 and g2, but by comparing the violation costs the agent is justi ed to accept g1 9
Asserted:
g1: choice g2: sequence
(forbidden g4) (obliged g5)
g3: parallel -
Inferred:
g4: choice
g9
g8: atomic
g5: choice
g6: atomic
(obliged g3)
(forbidden g6) (forbidden g8) (forbidden g2) g7: atomic (obliged g1) (obliged g7)
Figure 1: Deontic propagation in a goal network. Asserted: (forbidden g2 :cost low) (forbidden g3 :cost med) (forbidden g4 :cost high) (obliged g1 :cost high)
g1: choice
g2:atomic g3:atomic g4: atomic
Inferred: (forbidden g1 :cost low) (obliged g2 :cost high)
Figure 2: Deontic propagation with costs and con icts. and g2 as obligatory because thus it will incur a smaller penalty. This scheme works with both quantitative and qualitative violation costs by means of a cost abstract data type allowing each agent to de ne the nature of violation costs it uses. Deontic Propagation Algorithm. The propagation algorithm puts the theorems in section 3 in rule form inside a recursive invocation mechanism. In gure 3 we show one example of such a rule. The rule is activated when (1) a subgoal gi of a choice type goal g has been propagated as forbidden, (2) g is obliged, (3) all its subgoals are forbidden, (4) sO is the sum of all obligation costs on g (derived from all independent obligations placed on g) and (5)g-min and c-min are the subgoal with smallest interdiction cost and that cost respectively. In this case, the rule checks whether c-min is less than sO. If so, g-min becomes obliged (or forbidden if it occurs negated in g). Otherwise, the choice g becomes forbidden with c-min as violation cost. Labelings consist of multiple, independently justi ed propositions of type (obliged ) or (forbidden ). These propositions are stored in a LTMS [McAllester 80] and are justi ed by the other propositions that make the rules applicable. This allows us to implement non-monotonic reasoning and to provide explanations of every labeling in the system. The propagation process propagates one input deontic assertion at a time. For each assertion, all goals reachable from the goal of the input assertion along both supergoal and subgoal links are visited until no new assertion can be prop10
g: choice Propagation-rule: choice-conflict-3 :when-asserted F(gi) :such-that O(g) and sum-O-costs(g, sO) and all-subgoals-forbidden(g)
and
min-subgoal-F-cost(g, g-min, c-min) :propagate if c-min
