Planning as search: a quantitative approach
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Complexity results for standard benchmark domains in planning
Artificial Intelligence
Recent Progress in the Design and Analysis of Admissible Heuristic Functions
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Searching with Pattern Databases
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Faster heuristic search algorithms for planning with uncertainty and full feedback
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Finding optimal solutions to the twenty-four puzzle
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A heuristic search approach to planning with temporally extended preferences
Artificial Intelligence
A heuristic search approach to planning with temporally extended preferences
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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We study the problem of building effective heuristics for achieving cunjunctive goals from heuristics for individual goals. We consider a straightforward method for building conjunctive heuristics that smoothly trades off between previous common methods. In addition to first explicitly formulating the problem of designing conjunctive heuristics. our major contribution is the discovery that this straightforward method substantially outperforms previously used methods across a wide range of domains. Based on a single positive real parameter k, our heuristic measure sums the individual heuristic values for the subgoal conjuncts, each raised to the k'th power. Varying k allows loose approximation and combination of the previous min, max. and sum approaches, while mitigating some of the weaknesses in those approaches. Our empirical work shows that for many benchmark planning domains there exist fixed parameter values that perform well-- we give evidence that these values can be found automatically by training. Our method, applied to top-level conjunctive goals, shows dramatic improvements over the heuristic used in the FF planner across a wide range of planning competition benchmarks. Also, our heuristic, without computing landmarks, consistently improves upon the success ratio of a recently published landmark-based planner FF-L.