A number of technical approaches had to be employed within the planner, namely, 1) translating expected reward into a probability of goal satisfaction criterion, 2) monitoring belief states with a Rao-Blackwellized particle, and 3) employing Rao-Blackwellized particles in the McLUG probabilistic conformant planning graph heuristic. POND-Hindsight is an action selection mechanism that evaluates each possible action by generating a number of lookahead samples (up to a xed horizon) that greedily select actions based on their heuristic value and samples the actions' observation; the average goal satisfaction probability of the end horizon belief states is used as the value of each action.
| Identifer | oai:union.ndltd.org:UTAHS/oai:digitalcommons.usu.edu:etd-2037 |
| Date | 01 May 2011 |
| Creators | Hirsch, Merilynn Carol |
| Publisher | DigitalCommons@USU |
| Source Sets | Utah State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | All Graduate Theses and Dissertations |
| Rights | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact Andrew Wesolek (andrew.wesolek@usu.edu). |
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