Meta-Modeling of AI for Software Modularization
Keywords:
AI, Model-driven, SemioticAbstract
Recent developments in artificial intelligence have surprisingly been only on the machine-learning related technologies. This growing trend brings new hardships to the already problematic AI programming sector that looks like a zoo of paradigms. AI is unfortunately full of incompatible technologies that can hardly cooperate in a common multidisciplinary project. These technologies are also under the threat of being abandoned in favor of the emerging machine learning techniques. However, there are many valuable ideas and concepts in the classical AI approaches that can be quite useful in the awaiting challenges of general AI. Such a great endeavor will necessitate everything we know about representing and processing knowledge. Meta-modeling of the AI domain as a whole can bring about model driven development as a glue for the fragmented development efforts. In the long run it also has the capacity to trigger a unification and revival of the art of AI programming around a more structured central paradigm.
Downloads
References
P. H. Winston, “Artificial Intelligence”, Addison Wesley, Reading, Ma, third edition, 1992.
J. Wang, (2021, January) “Symbolism vs. Connectionism”, A Closing Gap in Artificial Intelligence”, Jieshu’s Blog, 2017, Available: http://wangjieshu.com/2017/12/23/symbol-vs-connecti
onism-a-closing-gap-in-artificial-intelligence/ [3] J. Bezivin, “On the unification power of models”, Journal on Software and Systems Modeling 4, 2005, pp. 171–188.
A. B. Arrieta, et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI”, Information Fusion, Vol 58, June, Elsevier, 2020, pp. 82-115.
M. J. Kemtongue, “Modularization Challenges in Prolog: What to Divide and Conquer in AI”, in Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making, Springer, January 2020, pp.330-337.
G. Nadathur, D. Miller, “An Overview of Lambda Prolog”, Technical Report, University of Pennsylvania, Scholarly Commons, Department of Computer and Information Science, June 1988, unpublished.
D. Miller. “A proposal for modules in lambda Prolog”, in Proceedings of the 1993 Workshop on Extensions to Logic Programming, volume 798 of Lecture Notes in Computer Science, 1994, pp. 206–221.
M. Ishizuka, N. Kanai, “Prolog-Elf Incorporating Fuzzy Logic” in: IJCAI 9, vol. 2, 1985, pp. 701-703. [9] J. F. Baldwin, T. P. Martin, B. W. Pilsworth, “Fril: Fuzzy and Evidential Reasoning in Artificial Intelligence”, John Wiley and Sons, 1995.
D. Li, D. Liu, “A Fuzzy Prolog Database System”, John Wiley & Sons, New York, 1990.
R. C. T. Lee, “Fuzzy Logic and the Resolution Principle”, Journal of the Association for Computing Machinery, 19(1), 1972, pp. 119-129.
J. Gao, Q. Jiang, B. Zhou, D. Chen, “Convolutional Neural Networks for Computer-aided Detection or Diagnosis in Medical Image Analysis: An Overview”, Mathematical Biosciences and Engineering, 16(6) 2019, pp.6536-6561.
J. J. Titiano, M. Badgeley, J. Schefflein et al., “Automatic Deep-neural-network Surveillance of Cranial Images for Accute Neurological Events”, Nat Med 24, 2018, pp.1337-1341.
F. P. Brooks, “No Silver Bullet: Essence and Accidents of Software Engineering,” Computer, IEEE Computer Society Press, 20(4), 10-19, April, 1987.
J. M. Favre, T. Nguyen, “Towards a Megamodel to Model Software Evolution through Transformations”, SETRA Workshop, Elsevier ENCTS, 2004.
A. Egesoy, “Context-Aware Formalization of Inter-Model Relations”, International Journal of Computer and Information Technology (IJCIT), Vol:3, No:6, November 2014, pp.1461-1467.
D. Chandler, (2021, January) “Semiotics for Beginners”, Aberystwyth University, Available: http://visual-memory.co.uk/daniel/Documents/S4B/
