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Collaborative inductive logic programming for path planning.
| Content Provider | CiteSeerX |
|---|---|
| Author | Pearce, Adrian R. Huang, Jian |
| 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. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Distributed System New Approach Communication Cost Paper Main Contribution Central Control Well-known Path Planning Problem Effective Way Many System Inductive Process Local Knowledge Systematic Approach Chosen Path Planning Algorithm Compare Result Collaborative Inductive Logic Programming Path Planning Single Agent-based Induction Multiple Instance Inductive Logic Programming Technique Epistemic Approach Inverse Entailment |
| Content Type | Text |
