Proceedings of the seventh international conference (1990) on Machine learning
Reinforcement learning architectures for animats
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Classifiers that approximate functions
Natural Computing: an international journal
Properties of the Bucket Brigade
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Biasing Exploration in an Anticipatory Learning Classifier System
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
An Algorithmic Description of ACS2
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Zcs: A zeroth level classifier system
Evolutionary Computation
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Evolutionary Computation
Automated global structure extraction for effective local building block processing in XCS
Evolutionary Computation
Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Anticipatory Learning Classifier Systems and Factored Reinforcement Learning
Anticipatory Behavior in Adaptive Learning Systems
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MACS (Modular Anticipatory Classifier System) is a new Anticipatory Classifier System. With respect to its predecessors, ACS ACS2 and YACS, the latent learning process in MACS is able to take advantage of new regularities. Instead of anticipating all attributes of the perceived situations in the same classifier, MACS only anticipates one attfribute per claasifier. In this paper we describe how the model of the environment represented by the classifiers can be used to perform active exploration and how this exploration policy is aggregated with the exploitation policy. The architecture is validated expermentally. Then we draw more general principles from the architectural choices giving rise to MACS. We show that building a model of the environment can be seen as a function approximation problem which can be solved with Anticipatory Classifier Systems such as MACS, but also with accuracy-based systems like XCS or XCSF, organized into a Dyna architecture.