PERFEX: Classifier Performance Explanations for Trustworthy AI Systems

Autor: Walraven, Erwin, Adhikari, Ajaya, Veenman, Cor J.

Explainability of a classification model is crucial when deployed in real-world decision support systems. Explanations make predictions actionable to the user and should inform about the capabilities and limitations of the system. Existing explanatio

Externí odkaz: http://arxiv.org/abs/2212.06045

Simultaneous modelling of access, egress & transit line choice for public transport

Autor: van der Tuin, Marieke, Zhou, Han, Walraven, Erwin

Publikováno v: In Transportation Research Procedia 2023 72:3793-3800

Bootstrapping LPs in Value Iteration for Multi-Objective and Partially Observable MDPs

Autor: Roijers, Diederik M., Walraven, Erwin, Spaan, Matthijs T.J., de Weerdt, Mathijs, Koenig, Sven, Röger, Gabriele, Spaan, Matthijs

Publikováno v: Proceedings of the 28th International Conference on Automated Planning and Scheduling
Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, 218-226
STARTPAGE=218;ENDPAGE=226;TITLE=Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling

Iteratively solving a set of linear programs (LPs) is a common strategy for solving various decision-making problems in Artificial Intelligence, such as planning in multi-objective or partially observable Markov Decision Processes (MDPs). A prevalent

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::593ec27f19c3dd6573f8c3524c0f5397
http://resolver.tudelft.nl/uuid:46e473c9-4feb-4735-b142-61555270eea3

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Information gathering in POMDPs using active inference

Autor: Walraven, Erwin, Sijs, Joris, Burghouts, Gertjan J.

Publikováno v: Autonomous Agents and Multi-Agent Systems; June 2025, Vol. 39 Issue: 1