{"type":"ScholarlyArticle","title":"On the Computational Complexity of Model Reconciliations","authors":[{"name":"Sarath Sreedharan","slug":"sarath-sreedharan","given":"Sarath","family":"Sreedharan","affiliation":"School of Computing & AI , ASU"},{"name":"Pascal Bercher","slug":"pascal-bercher","given":"Pascal","family":"Bercher","affiliation":"The Australian National University"},{"name":"Subbarao Kambhampati","slug":"subbarao-kambhampati","given":"Subbarao","family":"Kambhampati","affiliation":"School of Computing & AI , ASU"}],"abstract":"Model-reconciliation explanation is a popular framework for generating explanations for planning problems. While the framework has been extended to multiple settings since its introduction for classical planning problems, there is little agreement on the computational complexity of generating minimal model reconciliation explanations in the basic setting. In this paper, we address this lacuna by introducing a decision-version of the model-reconciliation explanation generation problem\r\nand we show that it is Sigma-2-P Complete.","doi":"10.24963/ijcai.2022/646","url":"https://ijcai.org/proceedings/2022/646","pdf":"https://ijcai.org/proceedings/2022/0646.pdf","markdown":"https://ijcai.org/proceedings/2022/646.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/646","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Planning and Scheduling","track":"Main Track","number":646,"pages":{"first":4657,"last":4664},"keywords":["AI Ethics, Trust, Fairness: Explainability and Interpretability","Planning and Scheduling: Theoretical Foundations of Planning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986642","length_sec":91,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984785","length_sec":340,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Arora and Barak, 2009] Sanjeev Arora and Boaz Barak. Computational Complexity -A Modern Approach. Cam- bridge University Press, 2009.","title":"Computational Complexity -A Modern Approach","authors":"Sanjeev Arora, Boaz Barak","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bylander, 1994] Tom Bylander. The computational com- plexity of propositional STRIPS planning. Artif. Intell., 69(1-2):165-204, 1994.","title":"The computational complexity of propositional STRIPS planning","authors":"Tom Bylander, Bylander","year":"1994","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chakraborti and Kambhampati, 2019] Tathagata Chakraborti and Subbarao Kambhampati. (when) can ai bots lie? In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pages 53-59, 2019.","title":"when) can ai bots lie?","authors":"Tathagata Chakraborti, Subbarao Kambhampati","year":"2019","venue":"Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chakraborti et al., 2017] Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang, and Subbarao Kambhampati. Plan explanations as model reconciliation: Moving beyond explanation as soliloquy. In IJCAI 2017, pages 156-163. IJCAI Organization, 2017.","title":"Plan explanations as model reconciliation: Moving beyond explanation as soliloquy","authors":"Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang, Subbarao Kambhampati","year":"2017","venue":"IJCAI 2017","doi":null,"url":null,"internal_url":"/proceedings/2017/23"},{"position":5,"raw":"[Chakraborti et al., 2020] Tathagata Chakraborti, Sarath Sreedharan, and Subbarao Kambhampati. The emerging landscape of explainable automated planning & decision making. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 4803-4811. IJCAI Organization, 2020.","title":"The emerging landscape of explainable automated planning & decision making","authors":"Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati","year":"2020","venue":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020","doi":null,"url":null,"internal_url":"/proceedings/2020/669"},{"position":6,"raw":"[Fox et al., 2017] Maria Fox, Derek Long, and Daniele Mag- azzeni. Explainable Planning. In IJCAI XAI Workshop, pages 24-30. XAI, 2017.","title":"Explainable Planning","authors":"Maria Fox, Derek Long, Daniele Magazzeni","year":"2017","venue":"IJCAI XAI Workshop","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Geffner and Bonet, 2013] Hector Geffner and Blai Bonet. A concise introduction to models and methods for automated planning, volume 7 of Synthesis Lectures on Artificial In- telligence and Machine Learning. Morgan & Claypool Publishers, 2013.","title":"A concise introduction to models and methods for automated planning","authors":"Bonet Geffner, Hector Geffner, Blai Bonet","year":"2013","venue":"7 of Synthesis Lectures on Artificial Intelligence and Machine Learning","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hoffmann and Magazzeni, 2019] Jörg Hoffmann and Daniele Magazzeni. Explainable AI planning (XAIP): overview and the case of contrastive explanation (ex- tended abstract). In Reasoning Web. Explainable Artificial Intelligence, volume 11810 of Lecture Notes in Computer Science, pages 277-282. Springer, 2019.","title":"Explainable AI planning (XAIP): overview and the case of contrastive explanation (extended abstract)","authors":"Jörg Hoffmann, Daniele Magazzeni","year":"2019","venue":"Reasoning Web. Explainable Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kautz et al., 1996] Henry A. Kautz, David A. McAllester, and Bart Selman. Encoding plans in propositional logic. In KR 96, pages 374-384.","title":"Encoding plans in propositional logic","authors":"Henry A Kautz, David A Mcallester, Bart Selman","year":null,"venue":"KR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"Morgan Kaufmann, 1996.","title":null,"authors":"Morgan Kaufmann","year":"1996","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lin and Bercher, 2021] Songtuan Lin and Pascal Bercher. Change the world -how hard can that be? on the com- putational complexity of fixing planning models. In IJCAI 2021, pages 4152-4159. IJCAI Organization, 2021.","title":"Change the world -how hard can that be? on the computational complexity of fixing planning models","authors":"Lin, Bercher ;, Songtuan Lin, Pascal Bercher","year":"2021","venue":"IJCAI 2021","doi":null,"url":null,"internal_url":"/proceedings/2021/571"},{"position":12,"raw":"[Sreedharan et al., 2018] Sarath Sreedharan, Tathagata Chakraborti, and Subbarao Kambhampati. Handling model uncertainty and multiplicity in explanations via model reconciliation. In ICAPS 2018, pages 518-526. AAAI Press, 2018.","title":"Handling model uncertainty and multiplicity in explanations via model reconciliation","authors":"Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati","year":"2018","venue":"ICAPS 2018","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Sreedharan et al., 2019] Sarath Sreedharan, Alberto Olmo Hernandez, Aditya Prasad Mishra, and Subbarao Kamb- hampati. Model-free model reconciliation. In IJCAI 2019, pages 587-594. IJCAI Organization, 2019.","title":"Model-free model reconciliation","authors":"Sarath Sreedharan, Alberto Olmo Hernandez, Aditya Prasad Mishra, Subbarao Kambhampati","year":"2019","venue":"IJCAI 2019","doi":null,"url":null,"internal_url":"/proceedings/2019/83"},{"position":14,"raw":"[Sreedharan et al., 2020] Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, and Subbarao Kambham- pati. Expectation-aware planning: A unifying framework for synthesizing and executing self-explaining plans for human-aware planning. In AAAI 2020, pages 2518-2526. AAAI Press, 2020.","title":"Expectation-aware planning: A unifying framework for synthesizing and executing self-explaining plans for human-aware planning","authors":"Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Subbarao Kambhampati","year":"2020","venue":"AAAI 2020","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Sreedharan et al., 2021] Sarath Sreedharan, Tathagata Chakraborti, and Subbarao Kambhampati. Founda- tions of explanations as model reconciliation. Artificial Intelligence, 301:103558, 2021.","title":"Foundations of explanations as model reconciliation","authors":"Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati","year":"2021","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Stockmeyer, 1976] Larry J Stockmeyer. The polynomial- time hierarchy. Theoretical Computer Science, 3(1):1-22, 1976.","title":"The polynomialtime hierarchy","authors":"J Larry, Stockmeyer","year":"1976","venue":"Theoretical Computer Science","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Vasileiou et al., 2021] Stylianos Loukas Vasileiou, Alessan- dro Previti, and William Yeoh. On exploiting hitting sets for model reconciliation. In AAAI 2021, pages 6514-6521. AAAI Press, 2021.","title":"On exploiting hitting sets for model reconciliation","authors":"Loukas Stylianos, Alessandro Vasileiou, William Previti, Yeoh","year":"2021","venue":"AAAI 2021","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}