Learning with genetic algorithms: an overview
Machine Language
Models of incremental concept formation
Artificial Intelligence
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
Search control, utility, and concept induction
Proceedings of the seventh international conference (1990) on Machine learning
Beyond inversion of resolution
Proceedings of the seventh international conference (1990) on Machine learning
Active perception and reinforcement learning
Proceedings of the seventh international conference (1990) on Machine learning
A formal framework for learning in embedded systems
Proceedings of the sixth international workshop on Machine learning
A general framework for parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Grounding Meaning in Perception
GWAI '90 Proceedings of the 14th German Workshop on Artificial Intelligence
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In most research on concept formation within machine learning and cognitive psychology, the features from which concepts are built are assumed to be provided as elementary vocabulary. In this paper, we argue that this is an unnecessarily limited paradigm within which to examine concept formation. Based on evidence from psychology and machine learning, we contend that a principled account of the origin of features can only be given with a grounded model of concept formation, i.e., with a model that incorporates direct access to the world via sensors and manipulators. We discuss the domain of process control as a suitable framework for research into such models, and present a first approach to the problem of developing elementary vocabularies from perceptual sensor data.