Map learning with uninterpreted sensors and effectors
Artificial Intelligence
Automated layout of concept lattices using layered diagrams and additive diagrams
ACSC '01 Proceedings of the 24th Australasian conference on Computer science
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Artificial Intelligence Review
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The marchitecture: a cognitive architecture for a robot baby
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The Übercruncher: concept formation by analogy discovery
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Using analogy discovery to create abstractions
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
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We present The Cruncher, a simple representation framework and algorithm based on minimum description length for automatically forming an ontology of concepts from attribute-value data sets. Although unsupervised, when The Cruncher is applied to an animal data set, it produces a nearly zoologically accurate categorization. We demonstrate The Cruncher's utility for finding useful macro-actions in Reinforcement Learning, and for learning models from uninterpreted sensor data. We discuss advantages The Cruncher has over concept lattices and hierarchical clustering.