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» Learning Control Knowledge for Forward Search Planning
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AIPS
2010
13 years 6 months ago
Iterative Learning of Weighted Rule Sets for Greedy Search
Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider learning sets of weighted actio...
Yuehua Xu, Alan Fern, Sung Wook Yoon
FLAIRS
2004
13 years 7 months ago
Using Previous Experience for Learning Planning Control Knowledge
Machine learning (ML) is often used to obtain control knowledge to improve planning efficiency. Usually, ML techniques are used in isolation from experience that could be obtained...
Susana Fernández, Ricardo Aler, Daniel Borr...
AIPS
1998
13 years 7 months ago
Making Forward Chaining Relevant
Planning by forward chaining through the world space has long been dismissed as being "obviously" infeasible. Nevertheless, this approach to planning has many advantages...
Fahiem Bacchus, Yee Whye Teh
AIPS
2008
13 years 8 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...
JAIR
2000
94views more  JAIR 2000»
13 years 5 months ago
Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan
This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search technique...
Subbarao Kambhampati