Formalising abilities and opportunities of agents
Fundamenta Informaticae
Extracting Context-Sensitive Models in Inductive Logic Programming
Machine Learning
Machine learning and inductive logic programming for multi-agent systems
Mutli-agents systems and applications
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Distributed Computing
Toward Inductive Logic Programming for Collaborative Problem Solving
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Collaborative inductive logic programming for path planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Prototype selection algorithms for distributed learning
Pattern Recognition
A multiagent framework for coordinated parallel problem solving
Applied Intelligence
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Both explanation-based and inductive learning techniques have proven successful in a variety of distributed domains. However, learning in multi-agent systems does not necessarily involve the participation of other agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately, or single-agent learning. In this paper we present a new framework, named the Multi-Agent Inductive Learning System (MAILS), that tightly integrates processes of induction between agents. The MAILS framework combines inverse entailment with an epistemic approach to reasoning about knowledge in a multi-agent setting, facilitating a systematic approach to the sharing of knowledge and invention of predicates when required. The benefits of the new approach are demonstrated for inducing declarative program fragments in a multi-agent distributed programming system.