{"type":"ScholarlyArticle","title":"Attribution-based Explanations for Markov Decision Processes","authors":[{"name":"Paul Kobialka","slug":"paul-kobialka","given":"Paul","family":"Kobialka","affiliation":""},{"name":"Andrea Pferscher","slug":"andrea-pferscher","given":"Andrea","family":"Pferscher","affiliation":""},{"name":"Francesco Leofante","slug":"francesco-leofante","given":"Francesco","family":"Leofante","affiliation":""},{"name":"Erika Ábrahám","slug":"erika-abraham","given":"Erika","family":"Ábráham","affiliation":""},{"name":"Silvia Lizeth Tapia Tarifa","slug":"silvia-lizeth-tapia-tarifa","given":"Silvia Lizeth","family":"Tapia Tarifa","affiliation":""},{"name":"Einar Broch Johnsen","slug":"einar-broch-johnsen","given":"Einar Broch","family":"Johnsen","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2026/74","url":"https://ijcai.org/proceedings/2026/74","pdf":"https://ijcai.org/proceedings/2026/0074.pdf","markdown":"https://ijcai.org/proceedings/2026/74.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/74","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI Ethics, Trust, Fairnes","track":"Main Track","number":74,"pages":{"first":652,"last":660},"keywords":["Uncertainty in AI: Sequential decision making","AI Ethics, Trust, Fairnes: Explainability and interpretability"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}