Bartlomiej Sniezynski

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Reinforcement learning suffers from inefficiency when the number of potential solutions to be searched is large. This paper describes a method of improving reinforcement learning by applying rule induction in multi-agent systems. Knowledge captured by learned rules is used to reduce search space in reinforcement learning, allowing it to shorten learning(More)
HANDLING CONSTRAINED OPTIMIZATION PROBLEMS AND USING CONSTRUCTIVE INDUCTION TO IMPROVE REPRESENTATION SPACES IN LEARNABLE EVOLUTION MODEL Janusz Wojtusiak, Ph.D. George Mason University, 2007 Dissertation co-Director: Dr. Ryszard S. Michalski Dissertation co-Director: Dr. James E. Gentle This dissertation investigates two closely related problems in the(More)
In this paper we propose an agent-based system for Service-Oriented Architecture selfadaptation. Services are supervised by autonomous agents which are responsible for deciding which service should be chosen for interoperation. Agents learn the choice strategy autonomously using supervised learning. In experiments we show that supervised learning (Näıve(More)