| Logic-based Learning in Conflict Simulation Domains (2007) | |||||||||||||||
Abstract | |||||||||||||||
| It is infeasible to specify complex Multi-Agent Systems in advance. Machine Learning techniques enable agents to learn from and adapt to the dynamic environment. So far, Reinforcement Learning methods have been extensively used in Multi-Agent Learning. However successful this approach has been, it relies on an assumption that establishes strict limits in the system's scalability: Agents are not provided with background knowledge. They learn from scratch. It is well-known, on the other hand, that domain-knowledge reduces considerably the complexity of planning problem. We propose to directly incorporate domain knowledge in the reasoning and learning processes of logic-based agents. To test this idea, we plan to apply Explanation-Based Learning and Inductive Logic Programming techniques in Conflict Simulation domains. 1 | |||||||||||||||
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