CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Extraction of rules from discrete-time recurrent neural networks
Neural Networks
Learning what is relevant to the effects of actions for a mobile robot
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Action representation, predicition and concepts TITLE2:
Action representation, predicition and concepts TITLE2:
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Continuous categories for a mobile robot
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Using simulation and critical points to define states in continuous search spaces
Proceedings of the 32nd conference on Winter simulation
Sequence Learning via Bayesian Clustering by Dynamics
Sequence Learning - Paradigms, Algorithms, and Applications
Learning Elements of Representations for Redescribing Robot Experiences
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Maps for verbs: the relation between interaction dynamics and verb use
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Review: learning like a baby: A survey of artificial intelligence approaches
The Knowledge Engineering Review
Autonomous construction of ecologically and socially relevant semantics
Cognitive Systems Research
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This paper describes a way of extracting concepts from streams of sensor readings. In particular, we demonstrate the value of attract or reconstruction techniques for transforming time series into clusters of points. These clusters, in turn, represent perceptual categories with predictive value to the agent/environment system. We also discuss the relationship between categories and concepts, with particular emphasis on class membership and predictive inference.