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Prior work on generating explanations in a planning and decision-making context has focused on providing the rationale behind an AI agent's decision making.
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T. Chakraborti, S. Sreedharan, Y. Zhang, and S. Kambhampati, “Plan explanations as model reconciliation: Moving beyond explanation as soliloquy. ijcai (2017), 156–163,” 2017
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Y. Zhang, S. Sreedharan, A. Kulkarni, T. Chakraborti, H. H. Zhuo, and S. Kambhampati, “Plan explicability and predictability for robot task planning,” in ICRA . IEEE, 2017
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2018
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T. Chakraborti, A. Kulkarni, S. Sreedharan, D. E. Smith, and S. Kambhampati, “Explicability? legibility? predictability? transparency? privacy? security? the emerging landscape of interpretable agent behavior,” in Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling, ICAPS 2018, Berkeley, CA, USA, July 11-15, 2019 , J. Benton, N. Lipovetzky, E. Onaindia, D. E. Smith, and S. Srivastava, Eds. AAAI Press, 2019, pp. 86–96. [Online]. Available: https://aaai.org/ojs/index.php/ICAPS/article/view/3463
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