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AI
2000
Springer
13 years 9 months ago
Learning Rewrite Rules versus Search Control Rules to Improve Plan Quality
Domain independent planners can produce better-quality plans through the use of domain-speci c knowledge, typically encoded as search control rules. The planning-by-rewriting appro...
Muhammad Afzal Upal, Renee Elio
ICML
1996
IEEE
14 years 5 months ago
Representing and Learning Quality-Improving Search Control Knowledge
Generating good, production-quality plans is an essential element in transforming planners from research tools into real-world applications, but one that has been frequently overl...
M. Alicia Pérez
AAAI
1994
13 years 5 months ago
Learning Explanation-Based Search Control Rules for Partial Order Planning
This paper presents snlp+ebl, the first implementation of explanation based learning techniques for a partial order planner. We describe the basic learning framework of snlp+ebl, ...
Suresh Katukam, Subbarao Kambhampati
AIPS
2010
13 years 4 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
GECCO
2006
Springer
177views Optimization» more  GECCO 2006»
13 years 8 months ago
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
The learning classifier system XCS is an iterative rulelearning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classifi...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson