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Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement - Extended Version
Download2010
Nakhost, Hootan, Müller, Martin
Technical report TR10-01. Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC-2008 used the cost of the...
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2013
Sturtevant, Nathan R., Valenzano, Richard, Schaeffer, Jonathan
While greedy best-first search (GBFS) is a popular algorithm for solving automated planning tasks, it can exhibit poor performance if the heuristic in use mistakenly identifies a region of the search space as promising. In such cases, the way the algorithm greedily trusts the heuristic can cause...
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2013
Valenzano, Richard, Müller, Martin, Xie, Fan
Most of the satisficing planners which are based on heuristic search iteratively improve their solution quality through an anytime approach. Typically, the lowest-cost solution found so far is used to constrain the search. This avoids areas of the state space which cannot directly lead to lower...
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2003
Greiner, Russ, Poulin, B., Lu, Paul, Anvik, J., Lu, Z., Macdonell, Cam, Wishart, David, Eisner, Roman, Szafron, Duane
Technical report TR03-09. Naive Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a...
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2010
Mueller, Martin, Hoffman, Joerg, Nakhost, Hootan
Technical report TR10-02. A ubiquitous feature of planning problems -- problems involving the automatic generation of action sequences for attaining a given goal -- is the need to economize limited resources such as fuel or money. While heuristic search, mostly based on standard algorithms such...
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2009
Bhatnagar, Shalabh, Sutton, Richard, Ghavamzadeh, Mohammad, Lee, Mark
Technical report TR09-10. We present four new reinforcement learning algorithms based on actor-critic, function approximation, and natural gradient ideas, and we provide their convergence proofs. Actor-critic reinforcement learning methods are online approximations to policy iteration in which...
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2012
Bowling, Michael, Zinkevich, Martin
Online learning aims to perform nearly as well as the best hypothesis in hindsight. For some hypothesis classes, though, even finding the best hypothesis offline is challenging. In such offline cases, local search techniques are often employed and only local optimality guaranteed. For online...
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2006
Poulin, Brett, Wan, Xiang, Kolacz, Tom
Technical report TR06-03. Single nucleotide polymorphisms (SNPs) are genetic markers that may be used to identify the causes and risks of cancer. The sheer volume of data generated by SNP studies is difficult to analyze by hand. Machine learning techniques have been developed to address the types...
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2003
Greiner, Russell, Wishart, David, Eisner, Roman, Lu, Z., Lu, Paul, Macdonell, Cam, Poulin, B., Szafron, Duane, Anvik, J.
Technical report TR03-14. Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy...