Attribution-based Explanations for Markov Decision Processes
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 sy...
| Authors | Paul Kobialka, Andrea Pferscher, Francesco Leofante et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 May 2026 |
| Last updated at source | 12 May 2026 |
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