Introduction to Multiagent Systems
Introduction to Multiagent Systems
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Agent-Oriented Model of Simulated Evolution
SOFSEM '02 Proceedings of the 29th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
Multimodal Optimization with Artificial Immune Systems
Proceedings of the International Symposium on "Intelligent Information Systems X"
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The basic idea of the approach proposed in this paper is to apply multi-agent paradigm in order to enable the integration and co-operation of different knowledge acquisition and representation techniques. The effective operation of learning process is achieved by evolutionary optimization running at the level of agents’ population. In the discussed variant of the model, each agent uses reinforcement learning, and the obtained knowledge is represented as the set of simple decision rules. The approach is illustrated by a particular realization of the system dedicated to the evasive maneuvers problem, together with preliminary experimental results.