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State Space Search with Stochastic Costs and Risk Aversion

  • Anisse IsmailiEmail author
Conference paper
  • 393 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9310)

Abstract

In this paper we study state space search problems where the costs of transitions are uncertain. Cost uncertainty can be due to the existence of several scenarios impacting the entire set of transitions; it can also result from local random factors impacting each transition independently, or from more complex combinations of these two cases. This leads us to consider three different settings for handling cost uncertainty in state space graphs. For each of them, we recall some key properties of first-order and second-order stochastic dominance. Then we propose dominance-based heuristic search algorithms to determine the set of possibly optimal solutions with respect to the expected utility model and Yaari’s model, with and without assuming risk aversion. Finally, to preserve scalability on large-size instances, we adapt these algorithms for the fast determination of an \(\varepsilon \)-covering of the potentially optimal solutions.

Keywords

State space search Uncertainty Stochastic dominance 

Notes

Acknowledgements

I wish to thank Patrice Perny and Olivier Spanjaard for their interesting discussions on the problem, and the reviewers for their comments.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Sorbonne Universités, UPMC Univ Paris 06, LIP6, UMR 7606ParisFrance

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