Towards compositional automated planning
Paper in 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

Author

Endre Erös

Chalmers, Electrical Engineering, Systems and control, Automation

Martin Dahl

Chalmers, Electrical Engineering, Systems and control, Automation

Petter Falkman

Chalmers, Electrical Engineering, Systems and control, Automation

Kristofer Bengtsson

Chalmers, Electrical Engineering, Systems and control, Automation

IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

Vol. September 416-423

25th IEEE ETFA
Vienna, Austria,

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Subject Categories

Control Engineering

Computer Science

DOI

10.1109/ETFA46521.2020.9212040

More information

Latest update

5/17/2021