Collaborative inductive logic programming for path planning

Authors:
Jian Huang;Adrian R. Pearce
Affiliations:
NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia;NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

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Abstract

In distributed systems, learning does not necessarily involve the participation of agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately. In this paper, we develop and evaluate a new approach for learning in distributed systems that tightly integrates processes of induction between agents, based on inductive logic programming techniques. The paper's main contribution is the integration of an epistemic approach to reasoning about knowledge with inverse entailment during induction. The new approach facilitates a systematic approach to the sharing of knowledge and invention of predicates only when required. We illustrate the approach using the well-known path planning problem and compare results empirically to (multiple instances of) single agent-based induction over varying distributions of data. Given a chosen path planning algorithm, our algorithm enables agents to combine their local knowledge in an effective way to avoid central control while significantly reducing communication costs.