Principles of artificial intelligence
Principles of artificial intelligence
Search in Artificial Intelligence
Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Multiple sequence alignment using anytime A*
Eighteenth national conference on Artificial intelligence
Anytime Heuristic Searc: First Results TITLE2:
Anytime Heuristic Searc: First Results TITLE2:
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Breadth-first heuristic search
Artificial Intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Comparing best-first search and dynamic programming for optimal multiple sequence alignment
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Best-first heuristic search for multicore machines
Journal of Artificial Intelligence Research
Heuristic search when time matters
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
In many shortest-path problems of practical interest, insufficient time is available to find a provably optimal solution. One can only hope to achieve a balance between search time and solution cost that respects the user's preferences, expressed as a utility function over time and cost. Current stateof-the-art approaches to this problem rely on anytime algorithms such as Anytime A* or ARA*. These algorithms require the use of extensive training data to compute a termination policy that respects the user's utility function. We propose a more direct approach, called BUGSY, that incorporates the utility function directly into the search, obviating the need for a separate termination policy. Experiments in several challenging problem domains, including sequence alignment and temporal planning, demonstrate that this direct approach can surpass anytime algorithms without requiring expensive performance profiling.