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AIPS
2008
13 years 10 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
JAIR
2011
134views more  JAIR 2011»
13 years 2 months ago
Scaling up Heuristic Planning with Relational Decision Trees
Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are wort...
Tomás de la Rosa, Sergio Jiménez, Ra...
ISHPC
2003
Springer
14 years 1 months ago
Evaluating Heuristic Scheduling Algorithms for High Performance Parallel Processing
Most cluster systems used in high performance computing do not allow process relocation at run-time. Finding an allocation that results in minimal completion time is NP-hard and (n...
Lars Lundberg, Magnus Broberg, Kamilla Klonowska
CP
2005
Springer
14 years 1 months ago
Robust Constraint Solving Using Multiple Heuristics
Abstract. Constraint Programming is a proven successful technique, but it requires skill in modeling problems, and knowledge on how algorithms interact with models. What can be a g...
Alfio Vidotto, Kenneth N. Brown, J. Christopher Be...
FLAIRS
2008
13 years 10 months ago
A New Approach to Heuristic Estimations for Cost-Based Planning
Solving relaxed problems is a commonly used technique in heuristic search to derive heuristic estimates. In heuristic planning, this is usually done by expanding a planning (reach...
Raquel Fuentetaja, Daniel Borrajo, Carlos Linares ...