Collaborative planning with encoding of users' high-level strategies
Name
Kim_AAAI_2017_preprint.pdf
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1.95 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Banks, Christopher J.
Kim, Joseph
Shah, Julie A
Date Issued
February 2017
Journal
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Kim, Joseph, Christopher J. Banks and Julie A. Shah. "Collaborative planning with encoding of users' high-level strategies." In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, California, 4-9 February. Palo Alto, California, Association for the Advancement of Artificial Intelligence 2017, pp. 955-961.
Version
Author's final manuscript
Abstract
© Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The generation of near-optimal plans for multi-agent systems with numerical states and temporal actions is computationally challenging. Current off-the-shelf planners can take a very long time before generating a near-optimal solution. In an effort to reduce plan computation time, increase the quality of the resulting plans, and make them more interpretable by humans, we explore collaborative planning techniques that actively involve human users in plan generation. Specifically, we explore a framework in which users provide high-level strategies encoded as soft preferences to guide the low-level search of the planner. Through human subject experimentation, we empirically demonstrate that this approach results in statistically significant improvements to plan quality, without substantially increasing computation time. We also show that the resulting plans achieve greater similarity to those generated by humans with regard to the produced sequences of actions, as compared to plans that do not incorporate userprovided strategies.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14840/13867
