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» Incremental Learning of Planning Operators in Stochastic Dom...
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ICML
2010
IEEE
13 years 8 months ago
Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...
Tobias Lang, Marc Toussaint
MLMTA
2007
13 years 8 months ago
GenInc: An Incremental Context-Free Grammar Learning Algorithm for Domain-Specific Language Development
- While grammar inference (or grammar induction) has found extensive application in the areas of robotics, computational biology, speech and pattern recognition, its application to...
Faizan Javed, Marjan Mernik, Barrett R. Bryant, Al...
ICTAI
2010
IEEE
13 years 4 months ago
Combining Learning Techniques for Classical Planning: Macro-operators and Entanglements
Planning techniques recorded a significant progress during recent years. However, many planning problems remain still hard even for modern planners. One of the most promising appro...
Lukás Chrpa
AIPS
2007
13 years 9 months ago
Discovering Relational Domain Features for Probabilistic Planning
In sequential decision-making problems formulated as Markov decision processes, state-value function approximation using domain features is a critical technique for scaling up the...
Jia-Hong Wu, Robert Givan
ICML
2009
IEEE
14 years 8 months ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint