Unifying and Certifying Top-Quality Planning
The growing utilization of planning tools in practical scenarios has sparked an interest in generating multiple high-quality plans. Consequently, a range of computational problems under the general umbrella of top-quality planning were introduced over a short time period, each with its own definition. In this work, we show that the existing definitions can be unified into one, based on a dominance relation. The different computational problems, therefore, simply correspond to different dominance relations. Given the unified definition, we can now certify the top-quality of the solutions, leveraging existing certification of unsolvability and optimality. We show that task transformations found in the existing literature can be employed for the efficient certification of various top-quality planning problems and propose a novel transformation to efficiently certify loopless top-quality planning.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation
Personalized natural language generation for explainable recommendations plays a key role in justifying why a recommendation might match a user's interests. Existing models usually control the generation process by aspec…
DiversityExplainable RecommendationExplanation GenerationInformativeness+1A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, a…
Gaussian ProcessesMotion PlanningBridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation
End-to-end autonomous driving aims to generate safe and plausible planning policies from raw sensor input. Driving world models have shown great potential in learning rich representations by predicting the future evoluti…
Autonomous DrivingScene GenerationVideo GenerationMotion PlanningA Unifying Framework for Reinforcement Learning and Planning
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and pl…
Decision Makingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Multi-Agent Reachability Calibration with Conformal Prediction
We investigate methods to provide safety assurances for autonomous agents that incorporate predictions of other, uncontrolled agents' behavior into their own trajectory planning. Given a learning-based forecasting model …
Autonomous DrivingConformal PredictionPredictionquantile regression+2