AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Autonomous Hierarchical POMDP Planning from Low-Level Sensors
Shawn Squire, Marie desJardins

Last modified: 2013-06-29

Abstract


There are currently no strong methods for planning in a stochastic domain, with low-level sensors that are limited and possibly inaccurate. Existing architectures have flaws that make their use in a real-world environment impractical. We propose an architecture that utilizes POMDPs to create a hierarchical planning system. This system is capable of developing macro-actions that can expedite planning on a large scale, and can learn new plans quickly and efficiently, without deliberate design by the programmer.

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