Computational Logic for Social Abilities in Multiagent Systems

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In this research, we study computational logic for multiagent systems. We focus on social abilities of agents such as coordination, coalition, consensus, and negotiation. We then formulate those abilities in terms of computational logic. To this end, we first consider an agent who has a knowledge base as a logic program with the answer set semantics. Answer sets represent belief sets which are built by a rational reasoner on the basis of a program. Different agents have different answer sets in general. To coordinate different belief sets of agents, we introduce program transformations which construct new programs having intermediate meanings between different agents. We also develop several program transformations: one realizes coalition by combining different answer sets, and another one realizes consensus by extracting common beliefs from different answer sets. Those program transformations produce serial knowledge bases which reflect results of coordination, coalition, and consensus in an agent community. Moreover, we provide methods for evaluating proposals and constructing new counterproposals in the process of negotiation. Tb build new proposals, we use abduction and induction in artificial intelligence, and relaxation techniques in cooperative query answering for databases. The proposed methods integrate commonsense reasoning and social reasoning of multiple agents in the context of logic programming, and are realized on top of the existing answer set solvers. The results of this study contribute to a step on understanding social behavior of multiple agents by means of computational logic, and also serve as an important application of artificial intelligence and logic programming.