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Conference Papers Year : 2021

Exploring Lifted Planning Encodings in Essence Prime

Joan Espasa
  • Function : Author
Ian Miguel
  • Function : Author
Mateu Villaret
  • Function : Author


State-space planning is the de-facto search method of the automated planning community. Planning problems are typically expressed in the Planning Domain Definition Language (PDDL), where action and variable templates describe the sets of actions and variables that occur in the problem. Typically, a planner begins by generating the full set of instantiations of these templates, which in turn are used to derive useful heuristics that guide the search. Thanks to this success, there has been limited research in other directions. We explore a different approach, keeping the compact representation by directly reformulating the problem in PDDL into ESSENCE PRIME, a Constraint Programming language with support for distinct solving technologies including SAT and SMT. In particular, we explore two different encodings from PDDL to ESSENCE PRIME, how they represent action parameters, and their performance. The encodings are able to maintain the compactness of the PDDL representation, and while they differ slightly, they perform quite differently on various instances from the International Planning Competition.

Dates and versions

hal-03604624 , version 1 (10-03-2022)



Joan Espasa, Jordi Coll, Ian Miguel, Mateu Villaret. Exploring Lifted Planning Encodings in Essence Prime. 23rd International Conference of the Catalan Association for Artificial Intelligence, Oct 2021, Lleida, Spain. ⟨10.3233/FAIA210117⟩. ⟨hal-03604624⟩
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