State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding

21 September 2023

Papers citing "State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding"

8 / 8 papers shown

Title
Explainable Reinforcement Learning Agents Using World Models Madhuri Singh Amal Alabdulkarim Gennie Mansi Mark O. Riedl 14 0 0 12 May 2025
Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation Jaechang Kim Jinmin Goh Inseok Hwang Jaewoong Cho Jungseul Ok ELM 18 1 0 28 Oct 2024
Inferring Belief States in Partially-Observable Human-Robot Teams Jack Kolb K. Feigh 15 0 0 18 Mar 2024
Natural Language Reinforcement Learning Xidong Feng Ziyu Wan Mengyue Yang Ziyan Wang Girish A. Koushiks Yali Du Ying Wen Jun Wang OffRL 29 3 0 11 Feb 2024
Experiential Explanations for Reinforcement Learning Amal Alabdulkarim Madhuri Singh Gennie Mansi Kaely Hall Mark O. Riedl Mark O. Riedl OffRL 36 2 0 10 Oct 2022
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off M. Zarlenga Pietro Barbiero Gabriele Ciravegna G. Marra Francesco Giannini ... F. Precioso S. Melacci Adrian Weller Pietro Lio' M. Jamnik 71 52 0 19 Sep 2022
Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures Devleena Das Sonia Chernova 23 20 0 08 Aug 2021
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations S. Sreedharan Utkarsh Soni Mudit Verma Siddharth Srivastava S. Kambhampati 63 29 0 04 Feb 2020