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 action-selection rules for guiding greedy search with application to automated planning. We make two primary contributions over prior work on learning for greedy search. First, we introduce weighted sets of action-selection rules as a new form of control knowledge for greedy search. Prior work has shown the utility of action-selection rules for greedy search, but has treated the rules as hard constraints, resulting in brittleness. Our weighted rule sets allow multiple rules to vote, helping to improve robustness to noisy rules. Second, we give a new iterative learning algorithm for learning weighted rule sets based on RankBoost, an efficient boosting algorithm for ranking. Each iteration considers the actual performance of the current rule set and directs learning based on the observed search errors. This is in co...

• Type: Conference
• Year: 2010
• Where: AIPS
• Authors: Yuehua Xu, Alan Fern, Sung Wook Yoon
• Keywords: Action-selection Rules, AIPS 2010, Artificial Intelligence, Greedy Search, Weighted Rule Sets