Specific-to-general learning for temporal events

Authors:
Alan Fern;Robert Givan;Jeffrey Mark Siskind
Affiliations:
Electrical and Computer Engineering, Purdue University, West Lafayette IN;Electrical and Computer Engineering, Purdue University, West Lafayette IN;Electrical and Computer Engineering, Purdue University, West Lafayette IN
Venue:
Eighteenth national conference on Artificial intelligence
Year:
2002

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Abstract

We study the problem of supervised learning of event classes in a simple temporal event-description language. We give lower and upper bounds and algorithms for the subsumption and generalization problems for two expressively powerful subsets of this logic, and present a positive-examples-only specific-to-general learning method based on the resulting algorithms. We also present a polynomial-time computable "syntactic" subsumption test that implies semantic subsumption without being equivalent to it. A generalization algorithm based on syntactic subsumption can be used in place of semantic generalization to improve the asymptotic complexity of the resulting learning algorithm. A companion paper shows that our methods can be applied to duplicate the performance of human-coded concepts in the substantial application domain of video event recognition.