Multi-objective AI Planning: Evaluating DaE YAHSP on a Tunable Benchmark
نویسندگان
چکیده
All standard Artifical Intelligence (AI) planners to-date can only handle a single objective, and the only way for them to take into account multiple objectives is by aggregation of the objectives. Furthermore, and in deep contrast with the single objective case, there exists no benchmark problems on which to test the algorithms for multi-objective planning. Divide-and-Evolve (DaE) is an evolutionary planner that won the (singleobjective) deterministic temporal satisficing track in the last International Planning Competition. Even though it uses intensively the classical (and hence single-objective) planner YAHSP (Yet Another Heuristic Search Planner), it is possible to turn DaEYAHSP into a multi-objective evolutionary planner. A tunable benchmark suite for multi-objective planning is first proposed, and the performances of several variants of multi-objectiveDaEYAHSP are compared on different instances of this benchmark, hopefully paving the road to further multi-objective competitions in AI planning.
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