TEMPORAL-ORIENTED MODEL OF CAUSAL RELATIONSHIP FOR CONSTRUCTING EXPLANATIONS FOR DECISION-MAKING PROCESS
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Abstract
The subject of research in the article is the decision-making process in intelligent systems. The goal is to develop a model of the causal relationship between the states of the decision-making process in an intelligent information system, taking into account the temporal aspect of this process, in order to build cause-and-effect relationships between the actions of the process and further use these dependencies to form explanations for the sequence of actions to obtain a decision. The formation of causal relations between the states of the decision-making process makes it possible to substantiate the sequence of actions of this process, considering incomplete information regarding external influences on this process. Tasks: structuring the decision-making process in an intelligent information system as a specialized business process; development of a three-element model of the causal relationship between the states of the decision-making process, considering the temporal aspect of this process; substantiation of the possibility of using three-element relationships to build causal dependencies for decision making in intelligent systems. The approaches used are: the set-theoretical approach used to describe the elements of the decision-making process in intelligent systems; a logical approach that provides a representation of the relationship between the states of the decision-making process; probabilistic approach to describe the probabilistic component of the decision-making process. The following results are obtained. The decision-making process in an intelligent information system was structured as a specialized business process that, using additional information from the user, turns the input data into a result that is valuable for this user; a three-element model of the causal relationship between the states of the decision-making process is proposed, which makes it possible to take into account external influences on the process; using a probabilistic approach, the possibility of using three-element causal relations to describe the decision-making process in intelligent systems is substantiated, taking into account uncontrolled external influences. Conclusions. The scientific novelty of the obtained results is as follows. A three-element model of the causal relationship between the states of the decision-making process is proposed, based on a model of a temporal rule of the "future" type, containing a state-cause, a state-effect and an intermediate state that reflects external influences. The model makes it possible to build a base of cause-and-effect dependencies for the decision-making process in an intelligent information system, considering external influences and use these dependencies to build explanations for this process.
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References
Castelvecchi D. (2016), “Can we open the black box of AI?” Nature, Vol. 538 (7623), pp. 20-23, doi: https://doi.org/10.1038/538020a.
Miller T. (2019), “Explanation in artificial intelligence: Insights from the social sciences”, Artificial Intelligence, vol. 267, pp.1-38, doi: https://doi.org/10.1016/j.artint.2018.07.007.
Tintarev N., Masthoff J. (2012), “Evaluating the effectiveness of explanations for recommender systems”, User Model User-Adap Inter., Vol. 22, pp. 399– 439, doi: https://doi.org/10.1007/s11257-011-9117-5.
Chalyi, S., Leshchynskyi, V. and Leshchynska I. (2021), “Counterfactual temporal model of causal relationships for constructing explanations in intelligent systems [Контрфактуальна темпоральна модель причинно-наслідкових зв'язків для побудови пояснень в інтелектуальних системах]”, Вісник Національного технічного університету "ХПІ". Сер. Системний аналіз, управління та інформаційні технології [Bulletin of the National Technical University "KhPI". Ser. System analysis, control and information technology] : coll. of science Ave., No. 2 (6), National Technical University "KhPI", Kharkiv, pp. 41-46, doi: https://doi.org/10.20998/2079-0023.2021.02.07.
Chalyi, S., Leshchynskyi, V. and Leshchynska I. (2021), “Relational-Temporal Model Of Set Of Substances Of Subject Area For The Process Of Solution Formation In Intellectual Information Systems [Реляційно-темпоральна модель набору сутностей предметної області для процесу формування рішення в інтелектуальній інформаційній системі], Вісник Національного технічного університету "ХПІ". Сер. Системний аналіз, управління та інформаційні технології [Bulletin of the National Technical University "KhPI". Ser. System analysis, control and information technology] : coll. of science Ave., No. 1 (7), National Technical University "KhPI", Kharkiv, pp. 84-89, doi: https://doi.org/10.20998/2079-0023.2022.01.14.
Adadi, A. and Berrada, M. (2018), “Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)”, IEEE Access, Vol. 6, 16 September 2018, INSPEC Accession Number: 18158981, pp. 52138-52160, Electronic ISSN: 2169-3536, doi: https://doi.org/10.1109/ACCESS.2018.2870052.
Gunning, D. and Aha, D. (2019), “DARPA’s Explainable Artificial Intelligence (XAI) Program”, AI Magazine, Vol. 40(2), pp. 44-58, doi: https://doi.org/10.1609/aimag.v40i2.2850.
Maier, M., Marazopoulou, K. and Jensen, D. (2014), “Reasoning about Independence in Probabilistic Models of Relational Data”, arXiv : 1302.4381, doi: https://doi.org/10.48550/arXiv.1302.4381.
Pearl, J (2009), Causality: Models, Reasoning and Inference, 2nd ed. Cambridge University Press, September 14, 2009, USA, 484 p.
Halpern, J. Y. and Pearl, J. (2005), “Causes and explanations: A structural-model approach. Part II: Explanations.” The British Journal for the Philosophy of Science, arXiv:cs/0208034, revised 19 Nov 2005, Vol. 56 (4). pp. 889-911, doi: https://doi.org/10.48550/arXiv.cs/0208034.
Levykin, V., and Chala, O. (2018), “Support decision-making in information control systems using the temporal knowledge base”, Advanced Information Systems [Сучасні інформаційні системи], Vol. 2, No. 4, pp. 101–107, doi: https://doi.org/10.20998/2522-9052.2018.4.17.
Levykin, V. and Chala, O.(2018), “Development of a method of probabilistic inference of sequences of business process activities to support business process management”, Eastern-European Journal of Enterprise Technologies, No. 5/3(95), pp. 16-24. doi: https://doi.org/10.15587/1729-4061.2018.142664
Chala, O. (2018), “Models of temporal dependencies for a probabilistic knowledge base”, Econtechmod, An International Quarterly Journal, Vol. 7, No. 3, pp. 53 – 58.
Chala, О. (2020), “Model of generalized representation of temporal knowledge for tasks of support of administrative decisions [Модель узагальненого представлення темпоральних знань для задач підтримки управлінських рішень]“, Вісник Національного технічного університету "ХПІ". Сер. Системний аналіз, управління та інформаційні технології [Bulletin of the National Technical University "KhPI". Ser. System analysis, control and information technology] : coll. of science Ave., No. 1 (3), National Technical University "KhPI", Kharkiv, pp. 14-18, doi: https://doi.org/10.20998/2079-0023.2020.01.03.
Chala, О. (2019), “Development of information technology for the automated construction and expansion of the temporal knowledge base in the tasks of supporting management decisions”, Technology audit and production reserves, Vol. 1/2(45), pp. 9-14, doi: https://doi.org/10.15587/2312-8372.2019.160205.
Chalyi, S., & Leshchynskyi, V. (2020), “Temporal representation of causality in the construction of explanations in intelligent systems”, Advanced Information Systems [Сучасні інформаційні системи], Vol. 4, No. 3, pp. 113–117, doi: https://doi.org/10.20998/2522-9052.2020.3.16
Marazopoulou, Katerina, Maier, Marc, and Jensen, David (2015), “Learning the structure of causal models with relational and temporal dependence”, Proceedings of the Thirty-First Conference on Uncertainty in Articial Intelligence, 12 July 2015, pp. 572–581.
Chalyi S., Leshchynskyi V. and Leshchynska I. (2019), “Modeling explanations for the recommended list of items based on the temporal dimension of user choice”, Control, navigation and communication systems [Системи управлінняб навігації та зв’язку], Vol. 6 (58), pp. 97-101. doi: https://doi.org/10.26906/SUNZ.2019.6.097.