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» Learning Heuristic Functions from Relaxed Plans
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SBIA
2004
Springer
15 years 3 months ago
Heuristically Accelerated Q-Learning: A New Approach to Speed Up Reinforcement Learning
This work presents a new algorithm, called Heuristically Accelerated Q–Learning (HAQL), that allows the use of heuristics to speed up the well-known Reinforcement Learning algori...
Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna...
AIPS
2000
14 years 11 months ago
New Results about LCGP, a Least Committed GraphPlan
Planners from the family of Graphplan (Graphplan, IPP, STAN...) are presently considered as the most efficient ones on numerous planning domains. Their partially ordered plans can...
Michel Cayrol, Pierre Régnier, Vincent Vida...
AI
2008
Springer
14 years 10 months ago
An approach to efficient planning with numerical fluents and multi-criteria plan quality
Dealing with numerical information is practically important in many real-world planning domains where the executability of an action can depend on certain numerical conditions, an...
Alfonso Gerevini, Alessandro Saetti, Ivan Serina
IJCAI
2007
14 years 11 months ago
Transferring Learned Control-Knowledge between Planners
As any other problem solving task that employs search, AI Planning needs heuristics to efficiently guide the problem-space exploration. Machine learning (ML) provides several tec...
Susana Fernández, Ricardo Aler, Daniel Borr...
SAT
2009
Springer
132views Hardware» more  SAT 2009»
15 years 4 months ago
Relaxed DPLL Search for MaxSAT
We propose a new incomplete algorithm for the Maximum Satisfiability (MaxSAT) problem on unweighted Boolean formulas, focused specifically on instances for which proving unsatis...
Lukas Kroc, Ashish Sabharwal, Bart Selman