Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Factored planning: how, when, and when not
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Structure and complexity in planning with unary operators
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
Temporal planning using subgoal partitioning and resolution in SGPlan
Journal of Artificial Intelligence Research
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Trees of shortest paths vs. Steiner trees: understanding and improving delete relaxation heuristics
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Inference and Learning in Planning (Extended Abstract)
DS '09 Proceedings of the 12th International Conference on Discovery Science
Improving Plan Quality in SAT-Based Planning
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Set-structured and cost-sharing heuristics for classical planning
Annals of Mathematics and Artificial Intelligence
Soft goals can be compiled away
Journal of Artificial Intelligence Research
Planning with h+in theory and practice
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Sound and Complete Landmarks for And/Or Graphs
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Fast forward planning by guided enforced hill climbing
Engineering Applications of Artificial Intelligence
A look-ahead B&B search for cost-based planning
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Planning with incomplete information
MoChArt'10 Proceedings of the 6th international conference on Model checking and artificial intelligence
Experimental evaluation of pheromone models in ACOPlan
Annals of Mathematics and Artificial Intelligence
Landmark-based heuristics and search control for automated planning (extended abstract)
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
The complexity of optimal monotonic planning: the bad, the good, and the causal graph
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
We introduce a simple variation of the additive heuristic used in the HSP planner that combines the benefits of the original additive heuristic, namely its mathematical formulation and its ability to handle non-uniform action costs, with the benefits of the relaxed planning graph heuristic used in FF, namely its compatibility with the highly effective enforced hill climbing search along with its ability to identify helpful actions. We implement a planner similar to FF except that it uses relaxed plans obtained from the additive heuristic rather than those obtained from the relaxed planning graph. We then evaluate the resulting planner in problems where action costs are not uniform and plans with smaller overall cost (as opposed to length) are preferred, where it is shown to compare well with cost-sensitive planners such as SGPlan, Sapa, and LPG. We also consider a further variation of the additive heuristic, where symbolic labels representing action sets are propagated rather than numbers, and show that this scheme can be further developed to construct heuristics that can take delete-information into account.