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R. de Haan, M. Kronegger, A. Pfandler:
"Fixed-parameter Tractable Reductions to SAT for Planning";
Vortrag: Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina; 25.07.2015 - 31.07.2015; in: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence - IJCAI 2015", Q. Yang, M. Wooldridge (Hrg.); AAAI Press, (2015), ISBN: 978-1-57735-738-4; S. 2897 - 2903.

Planning is an important AI task that gives rise to many hard problems. In order to come up with efficient algorithms for this setting, it is important to understand the sources of complexity. For planning problems that are beyond NP, identifying fragments that allow an efficient reduction to SAT can be a feasible approach due to the great performance of modern SAT solvers. In this paper, we use the framework of parameterized complexity theory to obtain a more fine-grained complexity analysis of natural planning problems beyond NP. With this analysis we are able to point out several variants of planning where the structure in the input makes encodings into SAT feasible. We complement these positive results with some hardness results and a new machine characterization for the intractability class ∃*∀k-W[P].

fixed-parameter tractable reductions, sat for planning

Projektleitung Reinhard Pichler:
Effiziente, parametrisierte Algorithmen in Künstlicher Intelligenz und logischem Schließen