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.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 email@example.com
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 firstname.lastname@example.org