Abstract
Although Prolog is considered to be the language of choice for decision support systems and most of the other fields of applied artificial intelligence, developing large projects with Prolog can become a complicated task. A programming language with adequate support for modularization can facilitate easier handling and reuse of different forms of control such as fuzzy logic. It is our view that this support can be provided without modifying the original logical programming paradigm. This paper aims to present an initial approach to this challenge by presenting an analysis of the requirements of modularization that originate from the artificial intelligence domain and the peculiarities of the Prolog language itself.
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Kemtongue, M.J., Egesoy, A. (2020). Modularization Challenges in Prolog: What to Divide and Conquer in AI. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_41
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DOI: https://doi.org/10.1007/978-3-030-23756-1_41
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