arXiv:2411. 18714v3 Announce Type: replace-cross Abstract: Self-driving cars increasingly rely on deep neural networks to achieve human-like driving.
By Eoin M. Kenny, Akshay Dharmavaram, Sang Uk Lee, Tung Phan-Minh, Shreyas Rajesh, Yunqing Hu, Laura Major, Momchil S. Tomov, Julie A. Shah
arXiv:2509. 14925v2 Announce Type: replace Abstract: Deep reinforcement learning (DRL) methods, though powerful, often lack transparency, which limits their adoption in critical domains.
By Konrad Nowosadko, Franco Ruggeri, Ahmad Terra
arXiv:2607. 27905v1 Announce Type: new Abstract: Counterfactual explanations (CFEs) enhance the interpretability of black-box models by generating alternative instances with adjusted feature values that achieve a contrastive outcome.
By Muhammad Adil Saleem, Syed Ali Raza, Mary-Anne Williams
Counterfactual explanations (CFEs) enhance the interpretability of black-box models by generating alternative instances with adjusted feature values that achieve a contrastive outcome. Reinforcement learning (RL) offers a promising approach for CFE generation, enabling efficient exploration of counterfactual instances while ensuring control over key metrics like validity, sparsity, and proximity.
The paper investigates how human observers interpret the learning processes of reinforcement learning (RL) agents. Using a novel observation-based paradigm, the authors conducted two experiments: an exploratory interview study with nine participants that identified four core themes—Agent Goals, Knowledge, Decision Making, and Learning Mechanisms—and a confirmatory study with 34 participants that applied the paradigm across navigation and manipulation tasks and two RL algorithms. Analyses of 816 responses validated the paradigm’s reliability and refined the thematic framework, showing how these themes evolve over time and interrelate.
By Bernhard Hilpert, Muhan Hou, Kim Baraka, Joost Broekens
arXiv:2607. 21209v1 Announce Type: cross Abstract: In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal.
By Heather Merhout (Miami University), Daniela Inclezan (Miami University)