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» Learning Heuristic Functions from Relaxed Plans
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AIPS
2006
13 years 6 months ago
Learning Heuristic Functions from Relaxed Plans
Sung Wook Yoon, Alan Fern, Robert Givan
AIPS
2008
13 years 7 months ago
Learning Relational Decision Trees for Guiding Heuristic Planning
The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computat...
Tomás de la Rosa, Sergio Jiménez, Da...
AIPS
2008
13 years 7 months ago
Learning Heuristic Functions through Approximate Linear Programming
Planning problems are often formulated as heuristic search. The choice of the heuristic function plays a significant role in the performance of planning systems, but a good heuris...
Marek Petrik, Shlomo Zilberstein
AIPS
2010
13 years 7 months ago
Classical Planning in MDP Heuristics: with a Little Help from Generalization
Heuristic functions make MDP solvers practical by reducing their time and memory requirements. Some of the most effective heuristics (e.g., the FF heuristic function) first determ...
Andrey Kolobov, Mausam, Daniel S. Weld
IJCAI
2001
13 years 6 months ago
Local Search Topology in Planning Benchmarks: An Empirical Analysis
Many state-of-the-art heuristic planners derive their heuristic function by relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. ...
Jörg Hoffmann