{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:42:38Z","timestamp":1780609358323,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","funder":[{"name":"Research Ireland","award":["18\\CRT\\6223;21\\FFP-A\\8957"],"award-info":[{"award-number":["18\\CRT\\6223;21\\FFP-A\\8957"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,16]]},"DOI":"10.1145\/3708319.3733653","type":"proceedings-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T15:17:00Z","timestamp":1749741420000},"page":"228-233","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Personalised and User-Friendly Counterfactual Sequences for Failure Correction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8731-1236","authenticated-orcid":false,"given":"Jasmina","family":"Gajcin","sequence":"first","affiliation":[{"name":"IBM Ireland, Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3272-9114","authenticated-orcid":false,"given":"Jovan","family":"Jeromela","sequence":"additional","affiliation":[{"name":"School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0621-5400","authenticated-orcid":false,"given":"Ivana","family":"Dusparic","sequence":"additional","affiliation":[{"name":"School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"e_1_3_3_1_2_2","first-page":"1168","volume-title":"Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems","author":"Amir Dan","year":"2018","unstructured":"Dan Amir and Ofra Amir. 2018. Highlights: Summarizing agent behavior to people. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. 1168\u20131176."},{"key":"e_1_3_3_1_3_2","volume-title":"International Conference on Learning Representations","author":"Atrey Akanksha","year":"2019","unstructured":"Akanksha Atrey, Kaleigh Clary, and David Jensen. 2019. Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning. In International Conference on Learning Representations."},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"David\u00a0S Boninger Faith Gleicher and Alan Strathman. 1994. Counterfactual thinking: From what might have been to what may be. Journal of personality and social psychology 67 2 (1994) 297.","DOI":"10.1037\/\/0022-3514.67.2.297"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Nyla\u00a0R Branscombe Susan Owen Teri\u00a0A Garstka and Jason Coleman. 1996. Rape and accident counterfactuals: Who might have done otherwise and would it have changed the outcome? Journal of Applied Social Psychology 26 12 (1996) 1042\u20131067.","DOI":"10.1111\/j.1559-1816.1996.tb01124.x"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/876"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Antonio Coronato Muddasar Naeem Giuseppe De\u00a0Pietro and Giovanni Paragliola. 2020. Reinforcement learning for intelligent healthcare applications: A survey. Artificial Intelligence in Medicine 109 (2020) 101964.","DOI":"10.1016\/j.artmed.2020.101964"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58112-1_31"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","unstructured":"Richard Dazeley Peter Vamplew Cameron Foale Charlotte Young Sunil Aryal and Francisco Cruz. 2021. Levels of explainable artificial intelligence for human-aligned conversational explanations. Artificial Intelligence 299 (2021) 103525. 10.1016\/j.artint.2021.103525","DOI":"10.1016\/j.artint.2021.103525"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Kalyanmoy Deb Amrit Pratap Sameer Agarwal and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6 2 (2002) 182\u2013197.","DOI":"10.1109\/4235.996017"},{"key":"e_1_3_3_1_11_2","unstructured":"Eoin Delaney Derek Greene and Mark\u00a0T Keane. 2021. Uncertainty estimation and out-of-distribution detection for counterfactual explanations: Pitfalls and solutions. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2107.09734 (2021)."},{"key":"e_1_3_3_1_12_2","unstructured":"Jasmina Gajcin and Ivana Dusparic. 2023. RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2303.04475 (2023)."},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","unstructured":"Jasmina Gajcin and Ivana Dusparic. 2024. Redefining Counterfactual Explanations for Reinforcement Learning: Overview Challenges and Opportunities. ACM Comput. Surv. 56 9 (2024) Article 219. 10.1145\/3648472","DOI":"10.1145\/3648472"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3687272.3688324"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3458455"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"publisher","unstructured":"Vittorio Girotto Paolo Legrenzi and Antonio Rizzo. 1991. Event controllability in counterfactual thinking. Acta Psychologica 78 1 (1991) 111\u2013133. 10.1016\/0001-6918(91)90007-M","DOI":"10.1016\/0001-6918(91)90007-M"},{"key":"e_1_3_3_1_17_2","first-page":"1792","volume-title":"International conference on machine learning","author":"Greydanus Samuel","year":"2018","unstructured":"Samuel Greydanus, Anurag Koul, Jonathan Dodge, and Alan Fern. 2018. Visualizing and understanding atari agents. In International conference on machine learning. PMLR, 1792\u20131801."},{"key":"e_1_3_3_1_18_2","unstructured":"Riccardo Guidotti. 2022. Counterfactual explanations and how to find them: literature review and benchmarking. Data Mining and Knowledge Discovery (2022) 1\u201355."},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.5555\/3545946.3598751"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9534363"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3631700.3665182"},{"key":"e_1_3_3_1_22_2","volume-title":"IJCAI\/ECAI Workshop on explainable artificial intelligence","author":"Juozapaitis Zoe","year":"2019","unstructured":"Zoe Juozapaitis, Anurag Koul, Alan Fern, Martin Erwig, and Finale Doshi-Velez. 2019. Explainable reinforcement learning via reward decomposition. In IJCAI\/ECAI Workshop on explainable artificial intelligence."},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","unstructured":"Eoin\u00a0M. Kenny and Mark\u00a0T. Keane. 2021. On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence 35 13 (2021) 11575\u201311585. 10.1609\/aaai.v35i13.17377","DOI":"10.1609\/aaai.v35i13.17377"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"B\u00a0Ravi Kiran Ibrahim Sobh Victor Talpaert Patrick Mannion Ahmad\u00a0A Al\u00a0Sallab Senthil Yogamani and Patrick P\u00e9rez. 2021. Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems 23 6 (2021) 4909\u20134926.","DOI":"10.1109\/TITS.2021.3054625"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3565472.3595611"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3534630"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"publisher","unstructured":"Ulrike Kuhl Andr\u00e9 Artelt and Barbara Hammer. 2023. Let\u2019s go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning. Frontiers in Computer Science Volume 5 - 2023 (2023). 10.3389\/fcomp.2023.1087929","DOI":"10.3389\/fcomp.2023.1087929"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594122"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i19.30127"},{"key":"e_1_3_3_1_30_2","unstructured":"Edouard Leurent. 2018. An Environment for Autonomous Driving Decision-Making. https:\/\/github.com\/eleurent\/highway-env."},{"key":"e_1_3_3_1_31_2","unstructured":"Yuxi Li. 2017. Deep reinforcement learning: An overview. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1701.07274 (2017)."},{"key":"e_1_3_3_1_32_2","unstructured":"Prashan Madumal Tim Miller Liz Sonenberg and Frank Vetere. 2020. Distal explanations for explainable reinforcement learning agents. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2001.10284 (2020)."},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5631"},{"key":"e_1_3_3_1_34_2","volume-title":"Artificial Intelligence for Agriculture and Food Systems (AIAFS)","author":"Maillard Odalric-Ambrym","year":"2023","unstructured":"Odalric-Ambrym Maillard, Timoth\u00e9e Mathieu, and Debabrota Basu. 2023. Farm-gym: A modular reinforcement learning platform for stochastic agronomic games. In Artificial Intelligence for Agriculture and Food Systems (AIAFS)."},{"key":"e_1_3_3_1_35_2","doi-asserted-by":"crossref","unstructured":"Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267 (2019) 1\u201338.","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"publisher","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei\u00a0A. Rusu Joel Veness Marc\u00a0G. Bellemare Alex Graves Martin Riedmiller Andreas\u00a0K. Fidjeland Georg Ostrovski Stig Petersen Charles Beattie Amir Sadik Ioannis Antonoglou Helen King Dharshan Kumaran Daan Wierstra Shane Legg and Demis Hassabis. 2015. Human-level control through deep reinforcement learning. Nature 518 7540 (2015) 529\u2013533. 10.1038\/nature14236","DOI":"10.1038\/nature14236"},{"key":"e_1_3_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372850"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"crossref","unstructured":"Matthew\u00a0L Olson Roli Khanna Lawrence Neal Fuxin Li and Weng-Keen Wong. 2021. Counterfactual state explanations for reinforcement learning agents via generative deep learning. Artificial Intelligence 295 (2021) 103455.","DOI":"10.1016\/j.artint.2021.103455"},{"key":"e_1_3_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/541"},{"key":"e_1_3_3_1_40_2","volume-title":"The book of why: the new science of cause and effect","author":"Pearl Judea","year":"2018","unstructured":"Judea Pearl and Dana Mackenzie. 2018. The book of why: the new science of cause and effect. Basic books."},{"key":"e_1_3_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-57321-8_5"},{"key":"e_1_3_3_1_42_2","volume-title":"International Conference on Learning Representations","author":"Puri Nikaash","year":"2019","unstructured":"Nikaash Puri, Sukriti Verma, Piyush Gupta, Dhruv Kayastha, Shripad Deshmukh, Balaji Krishnamurthy, and Sameer Singh. 2019. Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution. In International Conference on Learning Representations."},{"key":"e_1_3_3_1_43_2","doi-asserted-by":"crossref","unstructured":"Carl\u00a0Orge Retzlaff Srijita Das Christabel Wayllace Payam Mousavi Mohammad Afshari Tianpei Yang Anna Saranti Alessa Angerschmid Matthew\u00a0E Taylor and Andreas Holzinger. 2024. Human-in-the-loop reinforcement learning: A survey and position on requirements challenges and opportunities. Journal of Artificial Intelligence Research 79 (2024) 359\u2013415.","DOI":"10.1613\/jair.1.15348"},{"key":"e_1_3_3_1_44_2","doi-asserted-by":"publisher","unstructured":"Philipp Schmidt Felix Biessmann and Timm Teubner. 2020. Transparency and trust in artificial intelligence systems. Journal of Decision Systems 29 4 (2020) 260\u2013278. 10.1080\/12460125.2020.1819094doi: 10.1080\/12460125.2020.1819094.","DOI":"10.1080\/12460125.2020.1819094"},{"key":"e_1_3_3_1_45_2","doi-asserted-by":"crossref","unstructured":"Pedro Sequeira and Melinda Gervasio. 2020. Interestingness elements for explainable reinforcement learning: Understanding agents\u2019 capabilities and limitations. Artificial Intelligence 288 (2020) 103367.","DOI":"10.1016\/j.artint.2020.103367"},{"key":"e_1_3_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-14923-8_2"},{"key":"e_1_3_3_1_47_2","volume-title":"Reinforcement learning: An introduction","author":"Sutton Richard\u00a0S","year":"2018","unstructured":"Richard\u00a0S Sutton and Andrew\u00a0G Barto. 2018. Reinforcement learning: An introduction. MIT press."},{"key":"e_1_3_3_1_48_2","unstructured":"Stratis Tsirtsis Abir De and Manuel Rodriguez. 2021. Counterfactual explanations in sequential decision making under uncertainty. Advances in Neural Information Processing Systems 34 (2021) 30127\u201330139."},{"key":"e_1_3_3_1_49_2","unstructured":"Stratis Tsirtsis and Manuel Gomez-Rodriguez. 2023. Finding Counterfactually Optimal Action Sequences in Continuous State Spaces. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2306.03929 (2023)."},{"key":"e_1_3_3_1_50_2","unstructured":"Sahil Verma John Dickerson and Hines Keegan. 2020. Counterfactual explanations for machine learning: A review."},{"key":"e_1_3_3_1_51_2","doi-asserted-by":"crossref","unstructured":"Gary\u00a0L Wells Brian\u00a0R Taylor and John\u00a0W Turtle. 1987. The undoing of scenarios. Journal of personality and social psychology 53 3 (1987) 421.","DOI":"10.1037\/\/0022-3514.53.3.421"},{"key":"e_1_3_3_1_52_2","doi-asserted-by":"crossref","unstructured":"Lindsay Wells and Tomasz Bednarz. 2021. Explainable ai and reinforcement learning\u2014a systematic review of current approaches and trends. Frontiers in artificial intelligence 4 (2021) 550030.","DOI":"10.3389\/frai.2021.550030"},{"key":"e_1_3_3_1_53_2","unstructured":"Zhao Yang Song Bai Li Zhang and Philip\u00a0HS Torr. 2018. Learn to interpret atari agents. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1812.11276 (2018)."},{"key":"e_1_3_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340631.3394848"}],"event":{"name":"UMAP '25: 33rd ACM Conference on User Modeling, Adaptation and Personalization","location":"New York City USA","acronym":"UMAP '25","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3708319.3733653","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T11:15:54Z","timestamp":1751109354000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708319.3733653"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,12]]},"references-count":53,"alternative-id":["10.1145\/3708319.3733653","10.1145\/3708319"],"URL":"https:\/\/doi.org\/10.1145\/3708319.3733653","relation":{},"subject":[],"published":{"date-parts":[[2025,6,12]]},"assertion":[{"value":"2025-06-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}