PostScript language reference manual (2nd ed.)
PostScript language reference manual (2nd ed.)
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Adding temporary memory to ZCS
Adaptive Behavior
Adaptive Behavior
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Transition network grammars for natural language analysis
Communications of the ACM
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Tale of Two Classifier Systems
Machine Learning
A Critical Review of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
A Comparison Between ATNoSFERES And XCSM
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Finite-memory control of partially observable systems
Finite-memory control of partially observable systems
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Memory exploitation in learning classifier systems
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Strongly typed genetic programming
Evolutionary Computation
Schema theory for genetic programming with one-point crossover and point mutation
Evolutionary Computation
Proceedings of the 1st annual conference on Genetic and evolutionary computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Learning finite-state controllers for partially observable environments
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Adding memory condition to learning classifier systems to solve partially observable environments
International Journal of Computer Applications in Technology
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The automated design of the controller of software agents embedded in an environment is an important class of problems addressed in information sciences. In that class of problems, the case where agents face perceptual aliasing problems is particularly difficult. Within the evolutionary and adaptive approaches to controller design, there are several families of systems capable of dealing with such problems. This paper is devoted to a comparison of the way perceptual aliasing problems are solved by one family, namely Learning Classifier Systems, compared to our own model, ATNoSFERES. We present this model based on an indirect encoding Genetic Algorithm which builds Augmented Transition Network controllers, and we compare it with different Learning Classifier Systems, namely XCSM (a memory-based extension of the most studied Learning Classifier System, XCS) and ACS (an Anticipatory Learning Classifier System). To solve perceptual aliasing problems, the first uses an explicit internal state management mechanism while the second uses a rule-chaining mechanism. To carry out our comparison, we apply these systems to three different benchmark experiments. Our results raise a discussion of the respective properties of the mechanisms used by XCSM and ACS. Then we show that ATNoSFERES is endowed with enough expressive power to represent the mechanisms used by the other two systems to deal with perceptual aliasing problems. We conclude that ATNoSFERES provides a powerful framework to deal with such problems.