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By Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson

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Extra resources for Advances in learning classifier systems: third international workshop, IWLCS 2000, Paris, France, September 15-16, 2000 : revised papers

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5)}]. This classifier now predicts correctly the deterministic changes due to the execution of N and further predicts that the last attribute will change to 1 with a 50% chance. Since this classifier always anticipates correctly, its quality q will increase over 90% and will consequently become part of the internal environmental representation. The left-hand side of Fig. 3 shows the resulting performance in Woods1. As a comparison, also the learning curve without any non-determinism in the environment is shown.

As a comparison, also the learning curve without any non-determinism in the environment is shown. The correct anticipations measure is evaluated by considering in each situation in the environment each possible movement and checking if there is a reliable classifier that matches in the conditions and predicts the correct effects of the movement. The population size is the number of distinct classifiers in the population. When adding randomly changing attributes, the evolution of the whole internal environmental representation takes longer, but the ACS is able to build it completely.

PEEs. We have shown that the GA is able to substantially decrease the size of the population while the formation of the environmental representation stays very close to perfect. 5 Discussion Although a detailed observation of the evolving classifier lists (not shown herein) revealed that the probability distribution in the PEEs accurately reflect the current environmental settings, the usefulness of this distribution was not further investigated as yet. Enhancements in the reinforcement learning mechanisms as well as in the cognitive capabilities of the ACS are imaginable.

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