Assistant Agents for Sequential Planning Problems
Name
Assistant agents.pdf
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264.69 KB
Format
Adobe PDF
Checksum (MD5)
66d160b08cabbdc7831c6f43f26b62a5
Author(s)
Macindoe, Owen
Date Issued
October 2012
Journal
Proceedings of the 8th Artificial Intelligence and Interactive Digital Entertainment Conference
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Macindoe, Owen. "Assistant Agents for Sequential Planning Problems." 8th Artificial Intelligence and Interactive Digital Entertainment Conference (October 2012).
Version
Author's final manuscript
Abstract
The problem of optimal planning under uncertainty in collaborative multi-agent domains is known to be deeply intractable but still demands a solution. This thesis will explore principled approximation methods that yield tractable approaches to planning for AI assistants, which allow them to understand the intentions of humans and help them achieve their goals. AI assistants are ubiquitous in video games, mak- ing them attractive domains for applying these planning techniques. However, games are also challenging domains, typically having very large state spaces and long planning horizons. The approaches in this thesis will leverage recent advances in Monte-Carlo search, approximation of stochastic dynamics by deterministic dynamics, and hierarchical action representation, to handle domains that are too complex for existing state of the art planners. These planning techniques will be demonstrated across a range of video game domains.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://www.aaai.org/ocs/index.php/AIIDE/AIIDE12/paper/view/5495/5774
