Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
An analysis of stochastic shortest path problems
Mathematics of Operations Research
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The first probabilistic track of the international planning competition
Journal of Artificial Intelligence Research
Utilizing object-object and object-scene context when planning to find things
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
An effective personal mobile robot agent through symbiotic human-robot interaction
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
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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.