Skip to content
aiqbench takeAI Research

aiqbench analysis

This research applies integer programming and constraint generation to solve counterfactual explanations for the shortest path problem, a task central to interpretable machine learning in navigation systems. By efficiently determining minimal network changes that alter route optimality, the method provides actionable insights into how specific infrastructure features influence routing algorithms. The approach demonstrates significant speed advantages over competing submissions while maintaining high solution quality.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Counterfactual Routing Using Integer Programming with Constraint Generation

arXiv2 min read

arXiv:2609.03707v1 Announce Type: new Abstract: We present our submission to the IJCAI 2025 'Counterfactual Routing Competition' (CRC 25). The goal of the competition is to find counterfactual explanations for the shortest path problem. This requires deciding what the minimal changes to a road network would make a route chosen by the user the optimal route. This enables explanations such as "Your suggested route would indeed have been optimal, if road X were not a bicycle path." Our solution models the problem as an integer program, iteratively incorporating constraints until an exact solution is found. In the final evaluation on held-out test instances, our method ranked fourth in solution quality and obtained its solution fastest on every instance, with an average runtime of 9.0 seconds compared to 118.8 seconds for the next-fastest submission.

Abstract:We present our submission to the IJCAI 2025 'Counterfactual Routing Competition' (CRC 25). The goal of the competition is to find counterfactual explanations for the shortest path problem. This requires deciding what the minimal changes to a road network would make a route chosen by the user the optimal route. This enables explanations such as "Your suggested route would indeed have been optimal, if road X were not a bicycle path." Our solution models the problem as an integer program, iteratively incorporating constraints until an exact solution is found. In the final evaluation on held-out test instances, our method ranked fourth in solution quality and obtained its solution fastest on every instance, with an average runtime of 9.0 seconds compared to 118.8 seconds for the next-fastest submission.

Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS)

arXiv:2609.03707 [cs.AI]

(or arXiv:2609.03707v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.03707 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 226 words

Related coverage