Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 28/06/2022 |
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Host publication | AAAI-22 Technical Tracks 9 |
Pages | 9858-9867 |
Number of pages | 10 |
ISBN (electronic) | 1577358767, 9781577358763 |
<mark>Original language</mark> | English |
Name | Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 |
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Volume | 36 |
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 riskaware control is feasible on several moderately sized models.