Jennings, N. R.

AI Communications

English

Southampton (e-Prints Soton)

1Controlling Cooperative Problem Solving using Joint IntentionsNick JenningsDepartment of Electronic EngineeringQueen Mary & Westﬁeld CollegeUniversity of LondonN.R.Jennings@qmw.ac.ukIn Distributed AI (DAI) systems, problem solving agents cooperate to achieve the goals of theindividuals and of the system as a whole. Such joint work is needed because of the dependenciesbetween agents’ actions, the necessity to meet global constraints and because often no oneindividual has sufficient competence to solve the entire problem. Initially I concentrated ondeveloping a general-purpose cooperation framework (called GRATE) and applying it to the real-world industrial applications of electricity transportation management and diagnosis of faults in aparticle accelerator controller. These experiments were successful in that it was possible toinstantiate useful cooperation schemes, however when anything unexpected happened (eg newinformation invalidated existing goals, synchronisation between actions was disrupted or agentshad misinterpreted the situation) the cooperating community became uncoordinated. Thisincoherence occurred because GRATE agents did not embody sufﬁcient knowledge about theprocess of cooperative problem solving. To rectify this problem, it was decided that agents shouldhave a well-grounded and explicit model of joint problem solving on which their behaviour couldbe based.Existing models of joint intentions provide only a partial description of the process ofcollaboration. Most importantly, from the perspective of industrial applications, they fail todescribe how joint actions may falter and how individuals and the group should behave in suchcircumstances. Also as the existing models were predominantly theoretical, little considerationhad been given to computational tractability. To rectify these shortcomings, the model of JointResponsibility was developed. This model speciﬁes that each individual within a team shouldremain committed to achieving the common objective by the commonly agreed solution until oneof the following becomes true: the objective has been met, the objective will never be met, themotivation for the action is no longer present, the desired outcome of a plan step is alreadyavailable, following the agreed action sequence does not achieve the desired outcome, one of thespeciﬁed actions cannot be carried out or one of the agreed actions has not been carried out.Whilst in this state the agent will honour its commitments to its agreed actions. However if anagent is no longer committed to the joint action or the common solution, it cannot simply abandonthe action because its accomplices may not have been able to detect that there is a problem. Forthis reason, the responsibility model stipulates that when a team member is no longer jointlycommitted to the joint action it must ensure that all its acquaintances are informed of this changeof state. This enables the whole team to reassess the viability of the joint action and in particularthe actions involving the agent which is no longer committed.A rule-based interpretation of joint responsibility was then used to build agents which had anexplicit and principled model of collaboration to guide their individual and social actions. A seriesof comparative experiments were undertaken to assess the performance characteristics of theseagents. Three types of problem solving organisation were compared - a responsible community;an implicit group model in which agents had individual intentions, but did not form explicitcollaborating groups and groups of selﬁsh problem solvers who set up joint intentions, but whenthe joint action became unsustainable, behaved selﬁshly and simply abandoned their localprocessing without informing their fellow team members. These experiments showed that2responsible communities performed more coherently than the other two; this difference beingespecially noticeable as the domain became more dynamic and unpredictable (i.e. the chance ofjoint action unsustainability increased). This gain in performance was achieved with negligibleextra processing requirements for the coordination mechanisms.This work shows, through empirical evaluation on a real-world problem, that a suitablyformulated model of joint intentions is a powerful mechanism for coordinating the behaviour ofcollaborating agents. This is especially true in situations where agents have to make decisionsusing partial and imprecise data and when the environment itself is evolving and unpredictable. Italso indicates how theoretical models of collaboration can be used as a basis for implementationlevel systems. Finally as a consequence of the insights gained in this work, a proposal for the nextgeneration of multi-agent systems is made. In such “cooperation knowledge level systems”individuals maintain and reason about explicit and deep representations of social interactions,rather than having an implicit and shallow understanding of these processes.

Controlling Cooperative Problem Solving Using Joint Intentions

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