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.

online planning

automated planning

abstraction refinement

planning as satisfiability

artificial intelligence

compositional planning

intelligent automation

SAT solvers

Författare

Endre Erös

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Martin Dahl

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Petter Falkman

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Kristofer Bengtsson

Chalmers, Elektroteknik, System- och reglerteknik, Automation

IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

Vol. September 416-423

25th IEEE ETFA
Vienna, Austria,

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Ämneskategorier

Reglerteknik

Datavetenskap (datalogi)

DOI

10.1109/ETFA46521.2020.9212040

Mer information

Senast uppdaterat

2020-12-28