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• W204613853 abstract "The work presented here deals with ways to improve problem solving control skills of cooperative agents. We propose situation-specific learning as a way to learn cooperative control knowledge in complex multi-agent systems. We demonstrate the power of situation-specific learning in the context of dynamic choice of a coordination strategy based on the problem solving situation. We present a leaming system, called COLLAGE, that uses meta-level information in the form of abstract characterization of the coordination problem instance to leam to choose the appropriate coordination strategy from among a class of strategies. The work presented here deals with ways to improve problem solving control skills of cooperative agents. Problem solving control is the process by which an agent organizes its computation. An agent in a MAS faces control uncertainties due to its partial local view of the problem solving states of the other agents in the system. Uncertainties about progress of problem solving in other agents, characteristics of non-local interacting sub-problems, expectations of non-local problemsolving activity, and generated partial results can lead to global incoherence and degradation in system performance if they are not managed effectively through cooperation. Effective cooperation in such systems requires that the global problemsolving state influence the local control decisions made by an agent. We call such an influence cooperative control(Lesser 1991). Cooperation among agents can be explicit or implicit. In explicitly cooperative systems, the agents interact and exchange information or perform actions so as to benefit other agents. On the other hand, implicitly cooperative agents perform actions that are a part of their own goal-seeking process but these actions affect the other agents in beneficial ways(Mataric 1993). Agents exchange information through communication. An agent in a MAS needs to communicate with other agents to acquire a view of the non-local problem solving so as to make local decisions that are influenced by more global considerations. The agents perform direct communication or indirect communication as a part of their process of interaction with other agents. Direct communication involves intentionality entailing a sender and one or more receivers. The communication is purposeful and is aimed at particular members of *This material is based upon work supported by the National Science Foundation under Grant Nos. IRI-9523419. The content of this paper does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. the agent set. Much of the explicitly cooperative activities involves direct communication. In contrast, indirect communication is achieved through observation of the behavior of the other agents and their effects on the environment(Mataric 1993). Indirect communication involves cues that are simply by-products of behaviors performed for reasons other than communication(Seeley 1989). In this paper, we deal with an explicitly cooperative directly communicating system. In such systems, the local problem solving control of an agent can interact in complex and intricate ways with the problem solving control of other agents. In these systems, an agent cannot make effective control decisions based on purely local problem solving state. A given local problem solving state can map to a multitude of global problem solving states. An agent with a purely local view of the problem solving situation cannot learn effective control de" @default.
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• W204613853 title "Learning Problem Solving Control in Cooperative Multi-Agent Systems*" @default.
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