Towards compositional automated planning
Paper i proceeding, 2020

The development of efficient propositional satisfiability problem solving algorithms (SAT solvers) in the past two decades has made automated planning using SAT-solvers an established AI planning approach. Modern SAT solvers can accommodate a wide variety of planning problems with a large number of variables. However, fast computing of reasonably long plans proves challenging for planning as satisfiability. In order to address this challenge, we present a compositional approach based on abstraction refinement that iteratively generates, solves and composes partial solutions from a parameterized planning problem. We show that this approach decomposes the monolithic planning problem into smaller problems and thus significantly speeds up plan calculation, at least for a class of tested planning problems.

compositional planning

artificial intelligence

abstraction refinement

automated planning

planning as satisfiability

intelligent automation

SAT solvers

online planning


Endre Erös

Chalmers, Elektroteknik, System- och reglerteknik

Martin Dahl

Chalmers, Elektroteknik, System- och reglerteknik

Petter Falkman

Chalmers, Elektroteknik, System- och reglerteknik

Kristofer Bengtsson

Chalmers, Elektroteknik, System- och reglerteknik

IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

Vol. September 416-423
9781728189567 (ISBN)

Vienna, Austria,

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