Artificial Intelligence - Special issue on heuristic search in artificial intelligence
A new kind of science
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Cellular Automata: A Discrete Universe
Cellular Automata: A Discrete Universe
Artificial Intelligence
Fully asynchronous behavior of double-quiescent elementary cellular automata
Theoretical Computer Science
Examples of Fast and Slow Convergence of 2D Asynchronous Cellular Systems
ACRI '08 Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Learning generalized plans using abstract counting
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The deterministic part of IPC-4: an overview
Journal of Artificial Intelligence Research
Where "Ignoring delete lists" works: local search topology in planning benchmarks
Journal of Artificial Intelligence Research
Engineering benchmarks for planning: the domains used in the deterministic part of IPC-4
Journal of Artificial Intelligence Research
Compiling uncertainty away in conformant planning problems with bounded width
Journal of Artificial Intelligence Research
A translation-based approach to contingent planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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AI Planning is concerned with the selection of actions towards achieving a goal. Research on cellular automata (CA) is concerned with the question how global behaviours arise from local updating rules relating a cell to its direct neighbours. While these two areas are disparate at first glance, we herein identify a problem that is interesting to both: How to reach a fixed point in an asynchronous CA where cells are updated one-by-one? Considering a particular local updating rule, we encode this problem into PDDL and show that the resulting benchmark is an interesting challenge for AI Planning. For example, our experiments determine that, very atypically, an optimal SAT-based planner outperforms state-of-the-art satisficing heuristic search planners. This points to a severe weakness of current heuristics because, as we prove herein, plans for this problem can always be constructed in time linear in the size of the automaton. Our proof of this starts from a high-level argument and then relies on using a planner for flexible case enumeration within localised parts of the ar gument. Besides the formal result itself, this establishes a new proof technique for CAs and thus demonstrates that the potential benefit of research crossing the two fields is mutual.