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Action Model Acquisition using Sequential Pattern Mining

Résumé

This paper presents an approach to learn the agents' action model (action blueprints orchestrating transitions of the system state) from plan execution sequences. It does so by representing intra-action and interaction dependencies in the form of a maximum satisfiability problem (MAX-SAT), and solving it with a MAX-SAT solver to reconstruct the underlying action model. Unlike previous MAX-SAT driven approaches, our chosen dependencies exploit the relationship between consecutive actions, rendering more accurately learnt models in the end.
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Dates et versions

hal-01614448 , version 1 (18-10-2017)

Identifiants

  • HAL Id : hal-01614448 , version 1

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Ankuj Arora, Humbert Fiorino, Damien Pellier, Sylvie Pesty. Action Model Acquisition using Sequential Pattern Mining. German Conference on Artificial Intelligence, Sep 2017, Dortmund, Germany. pp.107 - 143. ⟨hal-01614448⟩
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