Assistant Agents for Sequential Planning Problems
Author(s)
Macindoe, Owen
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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.
Date issued
2012-10Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence LaboratoryJournal
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