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Author Valenzano, Richard ♦ Sturtevant, Nathan R. ♦ Schaeffer, Jonathan ♦ Xie, Fan
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description GBFS-based satisficing planners often augment their search with knowledge-based enhancements such as preferred oper-ators and multiple heuristics. These techniques seek to im-prove planner performance by making the search more in-formed. In our work, we will focus on how these enhance-ments impact coverage and we will use a simple technique called -greedy node selection to demonstrate that planner coverage can also be improved by introducing knowledge-free random exploration into the search. We then revisit the existing knowledge-based enhancements so as to determine if the knowledge these enhancements employ is offering nec-essary guidance, or if the impact of this knowledge is to add exploration which can be achieved more simply using ran-domness. This investigation provides further evidence of the importance of preferred operators and shows that the knowl-edge added when using an additional heuristic is crucial in certain domains, while not being as effective as random ex-ploration in others. Finally, we demonstrate that random ex-ploration can also improve the coverage of LAMA, a planner which already employs multiple enhancements. This suggests that even future knowledge-based enhancements need to be compared to appropriate knowledge-free random baselines so as to ensure the importance of knowledge being used. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2014-01-01
Publisher Institution In Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, ICAPS