Adding temporary memory to ZCS
Adaptive Behavior
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Lookahead And Latent Learning In ZCS
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Biasing Exploration in an Anticipatory Learning Classifier System
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
A New Architecture for Learning Classifier Systems to Solve POMDP Problems
Fundamenta Informaticae
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
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Previously we introduced Parallel Specialized XCS (PSXCS), a distributed-architecture classifier system that detects aliased environmental states and assigns their handling to created subordinate XCS classifier systems. PSXCS uses a history-window approach, but with novel efficiency since the subordinateXCSs, which employ the windows, are only spawned for parts of the state space that are actually aliased. However, because the window lengths are finite and set manually, PSXCS may fail to be optimal in difficult test mazes. This paper introduces Recursive PSXCS (RPSXCS) that automatically spawns windows wherever more history is required. Experimental results show that RPSXCS is both more powerful and learns faster than PSXCS. The present research suggests new potential for history approaches to partially observable environments.