Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Journal of the ACM (JACM)
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
A new approach to multiobjective A* search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Path planning under time-dependent uncertainty
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Optimal factory scheduling using stochastic dominance A
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
An axiomatic approach to robustness in search problems with multiple scenarios
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
International Journal of Approximate Reasoning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Computing rank dependent utility in graphical models for sequential decision problems
Artificial Intelligence
Towards a bridge between cost and wealth in risk-aware planning
Applied Intelligence
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We investigate search problems under risk in state-space graphs, with the aim of finding optimal paths for risk-averse agents. We consider problems where uncertainty is due to the existence of different scenarios of known probabilities, with different impacts on costs of solution-paths. We consider various non-linear decision criteria (EU, RDU, Yaari) to express risk averse preferences; then we provide a general optimization procedure for such criteria, based on a path-ranking algorithm applied on a scalarized valuation of the graph. We also consider partial preference models like second order stochastic dominance (SSD) and propose a multiobjective search algorithm to determine SSD-optimal paths. Finally, the numerical performance of our algorithms are presented and discussed.