Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Adapting Weighted Aggregation for Multiobjective Evolution Strategies
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multiobjective heuristic state-space planning
Artificial Intelligence
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
An approach to efficient planning with numerical fluents and multi-criteria plan quality
Artificial Intelligence
Branching and pruning: an optimal temporal POCL planner based on constraint programming
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
Journal of Artificial Intelligence Research
Optimal symbolic planning with action costs and preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
On the benefit of sub-optimality within the divide-and-evolve scheme
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Divide-and-Evolve: a new memetic scheme for domain-independent temporal planning
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
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Most real-world Planning problems are multi-objective, trying to minimize both the makespan of the solution plan, and some cost of the actions involved in the plan. But most, if not all existing approaches are based on single-objective planners, and use an aggregation of the objectives to remain in the single-objective context. Divide-and-Evolve is an evolutionary planner that won the temporal deterministic satisficing track at the last International Planning Competitions (IPC). Like all Evolutionary Algorithms (EA), it can easily be turned into a Pareto-based Multi-Objective EA. It is however important to validate the resulting algorithm by comparing it with the aggregation approach: this is the goal of this paper. The comparative experiments on a recently proposed benchmark set that are reported here demonstrate the usefulness of going Pareto-based in AI Planning.