Memoryless policies: theoretical limitations and practical results
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
An Algorithmic Description of XCS
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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 comparison between ATNoSFERES and Learning Classifier Systems on non-Markov problems
Information Sciences: an International Journal
A learning classifier system for mazes with aliasing clones
Natural Computing: an international journal
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Optimising of support plans for new graduate employment market using reinforcement learning
International Journal of Computer Applications in Technology
Particle swarm optimisation of a discontinuous control for a wheeled mobile robot with two trailers
International Journal of Computer Applications in Technology
Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification
International Journal of Computer Applications in Technology
Development of an adaptive and intelligent tutoring system by expert system
International Journal of Computer Applications in Technology
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Within the paradigm of learning classifier systems, extended classifier system XCS is outstanding. However, the original XCS has no memory mechanism and can only learn optimal policy in Markovian environments, where the optimal action is determined solely by the state of current sensory input. But in practice, most environments are partially observable environments with respect to agent's sensation, and they form the most general class of environments: non-Markov environments. In these environments, XCS either fails completely, or only develops a suboptimal policy, since it is memoryless. In this paper, we develop a new learning classifier system based on XCS, named 'XCSMM', which adds an internal message to XCS as an internal memory, and then extends the classifier with a memory condition that is used to sense the internal memory. XCSMM holds a simple and clear memory mechanism, which is easy to understand and implement. Besides, four sets of different complex maze problems have been employed to test XCSMM. Experimental results show that XCSMM is able to evolve optimal or suboptimal solutions in most non-Markovian environments.