Publication | Open Access
The 2023 International Planning Competition
22
Citations
39
References
2024
Year
Artificial IntelligenceEngineeringGlobal PlanningReinforcement Learning (Educational Psychology)Intelligent SystemsTask PlanningSocial SciencesReinforcement Learning (Computer Engineering)Systems EngineeringDigital PlanningPublic PolicyRobot Motion PlanningInternational Planning CompetitionInternational RelationsDesignStrategyLearning TrackPlanning MethodologiesPlanning TheoryAi PlanningPhysical PlanningMotion PlanningPlanning PracticePlanning
Abstract In this article, we present an overview of the 2023 International Planning Competition. It featured five distinct tracks designed to assess cutting‐edge methods and explore the frontiers of planning within these settings: the classical (deterministic) track, the numeric track, the Hierarchical Task Networks (HTN) track, the learning track, and the probabilistic and reinforcement learning track. Each of these tracks evaluated planning methodologies through one or more subtracks, with the goal of pushing the boundaries of current planner performance. To achieve this objective, the competition introduced a combination of well‐established challenges and entirely novel ones. Within this article, each track offers an exploration of its historical context, justifies its relevance within the planning landscape, discusses emerging domains and trends, elucidates the evaluation methodology, and ultimately presents the results.
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