Collaborative Logic Programming via Deductive-inductive Resolution

This thesis presents a powerful deductive-inductive resolution technique, by combining deductive theorem proving with inductive logic programming, for solving a new class of multi-agent problems—namely the collaborative logic programming (CollabLP) problems. In essence, the CollabLP formulation captures a wide range of problems in multi-agent settings where knowledge is typically distributed, private and possibly incomplete. Meanwhile, communication is allowed among the agents but restricted only to be in the form of simple logic programming queries. CollabLP captures not only problems requiring induction in multi-agent environments, but also deductive problems requiring collaboration in general. Under the deductive-inductive resolution (DIR) approach to the CollabLP problem, induction is viewed as an integral component and natural extension of an agent’s deductive process. The DIR approach tightly integrates processes of deduction and induction among agents, where communication is limited to inductive hypotheses and deductive consequences. Based on a modal treatment, the DIR approach is proven to be both sound (in general) and complete (under a separably inducible assump-