Classifier systems and genetic algorithms
Machine learning: paradigms and methods
Proceedings of the seventh international conference (1990) on Machine learning
Made-up minds: a constructivist approach to artificial intelligence
Made-up minds: a constructivist approach to artificial intelligence
A bottom-up mechanism for behavior selection in an artificial creature
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Lookahead planning and latent learning in a classifier system
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Cognitive action theory as a control architecture
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
An approach to anytime learning
ML92 Proceedings of the ninth international workshop on Machine learning
An extension of Maes' action selection mechanism for animats
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Autonomous Learning from the Environment
Autonomous Learning from the Environment
YACS: Combining Dynamic Programming with Generalization in Classifier Systems
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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How can we expect an A-life Agent to learn how to perform tasks when it is not told what those tasks are, and it is not provided any indication or feedback as to its performance? This is at the heart of the unsujjervised learning problem. If the Agent were able to learn in this manner, how could specific tasks be communicated to it? This is the Goal setting problem. Having been set a task, how would the Agent go about choosing things to do that will lead it to perform those tasks in an orderly manner? This is at the heart of the action selection problem.