Symbol acquisition for probabilistic high-level planning
Name
Lozano-Perez_Symbol acquisition.pdf
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1.15 MB
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Adobe PDF
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Author(s)
Konidaris, George
Kaelbling, Leslie P
Lozano-Perez, Tomas
Date Issued
July 2015
Journal
24th International Joint Conference on Artificial Intelligence (IJCAI 2015)
Publisher
AAAI Press / International Joint Conferences on Artificial Intelligence
Citation
Konidaris, George et al. "Symbol Acquisition for Probabilistic High-Level Planning" Proceedings of the Twenty Fourth International Joint Conference on Artificial Intelligence (IJCAI),Buenos Aires, Argentina, AAAI Press / International Joint Conferences on Artificial Intelligence, 2015.
Version
Author's final manuscript
Abstract
We introduce a framework that enables an agent to autonomously learn its own symbolic representation of a low-level, continuous environment. Propositional symbols are formalized as names for probability distributions, providing a natural means of dealing with uncertain representations and probabilistic plans. We determine the symbols that are sufficient for computing the probability with which a plan will succeed, and demonstrate the acquisition of a symbolic representation in a computer game domain.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://dl.acm.org/citation.cfm?id=2832754
