Toward optimal classifier system performance in non-Markov environments.

P L Lanzi; S W Wilson

doi:10.1162/106365600568239

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Toward optimal classifier system performance in non-Markov environments.

P L Lanzi·S W Wilson

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Abstract

Wilson's (1994) bit-register memory scheme was incorporated into the XCS classifier system and investigated in a series of non-Markov environments. Two extensions to the scheme were important in obtaining near-optimal performance in the harder environments. The first was an exploration strategy in which exploration of external actions was probabilistic as in Markov environments, but internal "actions" (register settings) were selected deterministically. The second was use of a register having more bit-positions than were strictly necessary to resolve environmental aliasing. The origins and effects of the two extensions are discussed.

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Algorithms Artificial Intelligence Learning Markov Chains Maze Learning Memory Neural Networks, Computer Probability Robotics Journal Article Research Support, Non-U.S. Gov't

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P L Lanzi, S W Wilson. 2000. Toward optimal classifier system performance in non-Markov environments.. https://doi.org/10.1162/106365600568239

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