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)
The paper presents an expert system based on Logic of Plausible Reasoning (LPR). This formalism reflects human ways of knowledge representation and reasoning. The knowledge is modeled using several kinds of formulas representing statements, hierarchies, similarities, dependencies and implications. Several types of inference patterns are defined. Knowledge(More)