Improving performance by reformulating PDDL into a bagged representation

Pat Riddle, Jordan Douglas, Mike Barley, Santiago Franco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes Baggy - a system which automatically transforms a PDDL representation into a revised PDDL representation, solves the problem using
the revised representation, and transforms the solution back into the original representation. The basic approach involves counting objects that are indistinguishable, rather than treating them as individual objects. This eliminates some unnecessary combinatorial explosion. We report encouraging results on a number of IPC11/14 domains and give algorithm details including soundness proof sketches. We conclude by discussing related work and outlining plans for future research.
Original languageEnglish
Title of host publicationProceedings of the 8th Workshop on Heuristics and Search for Domain-independent Planning (HSDIP)
Subtitle of host publicationThe 26th International Conference on Automated Planning and Scheduling
EditorsJ. Benton, Daniel Bryce, Michael Katz, Nir Lipovetzky, Christian Muise, Miquel Ramirez, Alvaro Torralba
Pages28-36
Number of pages9
Publication statusPublished - 2016
Externally publishedYes
EventThe 26th International Conference on Automated Planning and Scheduling - London, United Kingdom
Duration: 12 Jun 201617 Jun 2016
Conference number: 26
http://icaps16.icaps-conference.org/ (Link to Conference Website)

Conference

ConferenceThe 26th International Conference on Automated Planning and Scheduling
Abbreviated titleICAPS 2016
CountryUnited Kingdom
CityLondon
Period12/06/1617/06/16
Internet address

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Cite this

Riddle, P., Douglas, J., Barley, M., & Franco, S. (2016). Improving performance by reformulating PDDL into a bagged representation. In J. Benton, D. Bryce, M. Katz, N. Lipovetzky, C. Muise, M. Ramirez, & A. Torralba (Eds.), Proceedings of the 8th Workshop on Heuristics and Search for Domain-independent Planning (HSDIP): The 26th International Conference on Automated Planning and Scheduling (pp. 28-36)