Multiobjective heuristic state-space planning
Artificial Intelligence
An approach to efficient planning with numerical fluents and multi-criteria plan quality
Artificial Intelligence
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Domain independent approaches for finding diverse plans
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generating diverse plans to handle unknown and partially known user preferences
Artificial Intelligence
An Empirical Analysis of Some Heuristic Features for Planning through Local Search and Action Graphs
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
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In many real-world planning scenarios, the users are interested in optimizing multiple objectives (such as makespan and execution cost), but are unable to express their exact tradeoff between those objectives. When a planner encounters such partial preference models, rather than look for a single optimal plan, it needs to present the pareto set of plans and let the user choose from them. This idea of presenting the full pareto set is fraught with both computational and user-interface challenges. To make it practical, we propose the approach of finding a representative subset of the pareto set. We measure the quality of this representative set using the Integrated Convex Preference (ICP) model, originally developed in the OR community. We implement several heuristic approaches based on the Metric-LPG planner to find a good solution set according to this measure. We present empirical results demonstrating the promise of our approach.