[2305.00931] Explanation through Reward Model Reconciliation using POMDP Tree Search Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2305.00931 (cs) [Submitted on 1 May 2023] Title: Explanation through Reward Model Reconciliation using POMDP Tree Search Authors: Benjamin D. Kraske , Anshu Saksena , Anna L. Buczak , Zachary N. Sunberg View a PDF of the paper titled Explanation through Reward Model Reconciliation using POMDP Tree Search, by Benjamin D. Kraske and 3 other authors View PDF HTML (experimental) Abstract: As artificial intelligence (AI) algorithms are increasingly used in mission-critical applications, promoting user-trust of these systems will be essential to their success. Ensuring users understand the models over which algorithms reason promotes user trust. This work seeks to reconcile differences between the reward model that an algorithm uses for online partially observable Markov decision (POMDP) planning and the implicit reward model assumed by a human user. Action discrepancies, differences in decisions made by an algorithm and user, are leveraged to estimate a user's objectives as expressed in weightings of a reward function. Subjects: Artificial Intelligence (cs.AI) ; Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) Cite as: arXiv:2305.00931 [cs.AI] (or arXiv:2305.00931v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2305.00931 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE ICAA (2023), pg. 137-140 Related DOI : https://doi.org/10.1109/ICAA58325.2023.00027 Focus to learn more DOI(s) linking to related resources Submission history From: Benjamin Kraske [ view email ] [v1] Mon, 1 May 2023 16:32:45 UTC (573 KB) Full-text links: Access Paper: View a PDF of the paper titled Explanation through Reward Model Reconciliation using POMDP Tree Search, by Benjamin D. Kraske and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2023-05 Change to browse by: cs cs.HC cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from