Abstract
This paper explores a yet another approach to Explainable Artificial Intelligence. The proposal consists in application of Constraint Programming to discovery of internal structure and parameters of a given black-box system. Apart from specification of a sample of the input and output values, some presupposed knowledge about the possible internal structure and functional components is required. This knowledge can be parameterized with respect to functional specification of internal components, connections among them, and internal parameters. Models of constraints are put forward and example case studies illustrate the proposed ideas.
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Notes
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We use the MiniZinc Constraint Programming language: https://www.minizinc.org/ run under Linux Mint.
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Ligęza, A. et al. (2021). Explainable Artificial Intelligence. Model Discovery with Constraint Programming. In: Stettinger, M., Leitner, G., Felfernig, A., Ras, Z.W. (eds) Intelligent Systems in Industrial Applications. ISMIS 2020. Studies in Computational Intelligence, vol 949. Springer, Cham. https://doi.org/10.1007/978-3-030-67148-8_13
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