Attribution-based Explanations for Markov Decision Processes
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911675207122944 |
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| author | Kobialka, Paul Pferscher, Andrea Leofante, Francesco Ábrahám, Erika Tarifa, Silvia Lizeth Tapia Johnsen, Einar Broch |
| author_facet | Kobialka, Paul Pferscher, Andrea Leofante, Francesco Ábrahám, Erika Tarifa, Silvia Lizeth Tapia Johnsen, Einar Broch |
| contents | Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on attributing importance to static input features at a single point in time, and thus fail to generalize to sequential decision-making settings. This paper fills this gap by introducing techniques to generate attribution-based explanations for Markov Decision Processes (MDPs). We give a formal characterization of what attributions should represent in MDPs, focusing on explanations that assign importance scores to both individual states and execution paths. We show how importance scores can be computed by leveraging techniques for strategy synthesis, enabling the efficient computation of these scores despite the non-determinism inherent in an MDP. We evaluate our approach on five case-studies, demonstrating its utility in providing interpretable insights into the logic of sequential decision-making agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09780 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Attribution-based Explanations for Markov Decision Processes Kobialka, Paul Pferscher, Andrea Leofante, Francesco Ábrahám, Erika Tarifa, Silvia Lizeth Tapia Johnsen, Einar Broch Artificial Intelligence I.2.m Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on attributing importance to static input features at a single point in time, and thus fail to generalize to sequential decision-making settings. This paper fills this gap by introducing techniques to generate attribution-based explanations for Markov Decision Processes (MDPs). We give a formal characterization of what attributions should represent in MDPs, focusing on explanations that assign importance scores to both individual states and execution paths. We show how importance scores can be computed by leveraging techniques for strategy synthesis, enabling the efficient computation of these scores despite the non-determinism inherent in an MDP. We evaluate our approach on five case-studies, demonstrating its utility in providing interpretable insights into the logic of sequential decision-making agents. |
| title | Attribution-based Explanations for Markov Decision Processes |
| topic | Artificial Intelligence I.2.m |
| url | https://arxiv.org/abs/2605.09780 |