In this section
@inproceedings{Kobialka:2025:ijcai.2025/47,
author = {Kobialka, P and Gerlach, L and Leofante, F and Abraham, E and Silvia, Lizeth TT and Broch, Johnsen E},
doi = {ijcai.2025/47},
pages = {412--420},
publisher = {IJCAI Organization},
title = {Counterfactual strategies for Markov decision processes},
url = {http://dx.doi.org/10.24963/ijcai.2025/47},
year = {2025}
}
TY - CPAPER
AB - Counterfactuals are widely used in AI to explain how minimal changes to a model’s input can lead to a different output. However, established methods for computing counterfactuals typically focus on one-step decision-making, and are not directly applicable to sequential decision-making tasks. This paper fills this gap by introducing counterfactual strategies for Markov Decision Processes (MDPs). During MDP execution, a strategy decides which of the enabled actions (with known probabilistic effects) to execute next. Given an initial strategy that reaches an undesired outcome with a probability above some limit, we identify minimal changes to the initial strategy to reduce that probability below the limit. We encode such counterfactual strategies as solutions to non-linear optimization problems, and further extend our encoding to synthesize diverse counterfactual strategies. We evaluate our approach on four real-world datasets and demonstrate its practical viability in sophisticated sequential decision-making tasks.
AU - Kobialka,P
AU - Gerlach,L
AU - Leofante,F
AU - Abraham,E
AU - Silvia,Lizeth TT
AU - Broch,Johnsen E
DO - ijcai.2025/47
EP - 420
PB - IJCAI Organization
PY - 2025///
SP - 412
TI - Counterfactual strategies for Markov decision processes
UR - http://dx.doi.org/10.24963/ijcai.2025/47
ER -
