Abstract
This paper presents a novel formulation for the problem of finding objects in a known environment while minimizing the search cost. Our approach consists in formalizing this class of problems as Stochastic Shortest Path (SSP) problems, a decision-theoretic framework for probabilistic environments. The obtained problems are solved by using off-the-shelf domain-independent probabilistic planners. The advantages of this approach includes: (i) a well defined optimization problem in which the probability of finding the object is maximized while minimizing the cost of searching for the object; and (ii) being able to take advantage, without any modifications to our model, of any (future) technique in the field of domain-independent probabilistic planners, such as better algorithms and better heuristics. We also contribute by empirically comparing three probabilistic planners algorithms, namely FF-Replan, UCT and SSiPP, using our proposed class of problems.
Original language | English |
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Pages | 547-554 |
Number of pages | 8 |
Publication status | Published - 2013 |
Externally published | Yes |
Event | 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 - Saint Paul, MN, United States Duration: 6 May 2013 → 10 May 2013 |
Conference
Conference | 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 |
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Country/Territory | United States |
City | Saint Paul, MN |
Period | 6/05/13 → 10/05/13 |