A relevance-based compilation method for Conformant probabilistic planning

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3 Scopus citations

Abstract

Conformant probabilistic planning (CPP) differs from conformant planning (CP) by two key elements: the initial belief state is probabilistic, and the conformant plan must achieve the goal with probability ≥ θ, for some 0 < θ ≤ 1. In earlier work we observed that one can reduce CPP to CP by finding a set of initial states whose probability ≥ θ, for which a conformant plan exists. In previous solvers we used the underlying planner to select this set of states and to plan for them simultaneously. Here we suggest an alternative approach: start with relevance analysis to determine a promising set of initial states on which to focus. Then, call an off-the-shelf conformant planner to solve the resulting problem. This approach has a number of advantages. First, instead of depending on the heuristic function to select the set of initial slates, we can introduce specific, efficient relevance reasoning techniques. Second, we can benefit from optimizations used by conformant planners that are unsound when applied lo the original CPP. Finally, we are free to use any existing (or new) CP solver. Consequently, the new planner dominates previous solvers on almost all domains and scales to instances that were not solved before.

Original languageEnglish
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAI Access Foundation
Pages2374-2380
Number of pages7
ISBN (Electronic)9781577356790
StatePublished - 1 Jan 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: 27 Jul 201431 Jul 2014

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume3

Conference

Conference28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
Country/TerritoryCanada
CityQuebec City
Period27/07/1431/07/14

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