Schematic invariants by reduction to ground invariants

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Rintanen, Jussi
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Satinder Singh, Shaul Markovitch

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2017
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San Francisco, United States

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Abstract

Computation of invariants, which are approximate reachability information for state-space search problems such as AI planning, has been considered to be more scalable when using a schematic representation of actions/events rather than an instantiated/ground representation. A disadvantage of schematic algorithms, however, is their complexity, which also leads to high runtimes when the number of schematic events/actions is high. We propose algorithms that reduce the problem of finding schematic invariants to solving a smaller ground problem.

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Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence

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Artificial Intelligence and Image Processing not elsewhere classified

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