Polynomial Algorithm For Learning From Interpretation Transition
Abstract
Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. The previously proposed General Usage LFIT Algorithm (GULA) serves as the core block to several methods of the framework that capture different dynamics. But its exponential complexity limits the use of the whole framework to relatively small systems. In this paper, we introduce an approximated algorithm (PRIDE) which trades the completeness of GULA for a polynomial complexity.
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ilp21_extended_abstract_pride.pdf (295.13 Ko)
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Ribeiro_IJCLR_PRIDE_Poster.pdf (791.05 Ko)
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