Risk-Aware Stochastic Shortest Path

Authors

  • Tobias Meggendorfer Institute of Science and Technology Austria

DOI:

https://doi.org/10.1609/aaai.v36i9.21222

Keywords:

Planning, Routing, And Scheduling (PRS)

Abstract

We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models.

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Published

2022-06-28

How to Cite

Meggendorfer, T. (2022). Risk-Aware Stochastic Shortest Path. Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 9858-9867. https://doi.org/10.1609/aaai.v36i9.21222

Issue

Section

AAAI Technical Track on Planning, Routing, and Scheduling