In this section
@inproceedings{Kobialka:2026,
author = {Kobialka, P and Pferscher, A and Leofante, F and Abraham, E and Tapia, Tarifa SL and Broch, Johnsen E},
publisher = {IJCAI},
title = {Attribution-based explanations for Markov decision processes},
year = {2026}
}
TY - CPAPER
AB - 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 representin 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 sequentialdecision-making agents.
AU - Kobialka,P
AU - Pferscher,A
AU - Leofante,F
AU - Abraham,E
AU - Tapia,Tarifa SL
AU - Broch,Johnsen E
PB - IJCAI
PY - 2026///
TI - Attribution-based explanations for Markov decision processes
ER -
