Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
A study of rule set development in learning classifier system
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A comparison of Q-learning and classifier systems
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Adding temporary memory to ZCS
Adaptive Behavior
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Classifier Systems and the Animat Problem
Machine Learning
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
On ZCS in Multi-agent Environments
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
A multiagent model of the UK market in electricity generation
IEEE Transactions on Evolutionary Computation
TCS Learning Classifier System Controller on a Real Robot
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Rule Fitness and Pathology in Learning Classifier Systems
Evolutionary Computation
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS
Learning Classifier Systems
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems
A learning classifier system for mazes with aliasing clones
Natural Computing: an international journal
Imitation guided learning in learning classifier systems
Natural Computing: an international journal
Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Intuitive Action Set Formation in Learning Classifier Systems with Memory Registers
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning
IEEE Transactions on Evolutionary Computation
Effect of pure error-based fitness in XCS
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Towards a mapping of modern AIS and LCS
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Engineering Applications of Artificial Intelligence
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Learning classifier systems traditionally use genetic algorithms to facilitate rule discovery, where rule fitness is payoff based. Current research has shifted to the use of accuracy-based fitness. This paper re-examines the use of a particular payoff-based learning classifier system - ZCS. By using simple difference equation models of ZCS, we show that this system is capable of optimal performance subject to appropriate parameter settings. This is demonstrated for both single- and multistep tasks. Optimal performance of ZCS in well-known, multistep maze tasks is then presented to support the findings from the models.