Meta-Modeling of AI for Software Modularization

Authors

  • Ahmet Egesoy Assistant Professor, Department of Computer Engineering, Ege University, İzmir, Turkey Author

Keywords:

AI, Model-driven, Semiotic

Abstract

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

Download data is not yet available.

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Report, University of Pennsylvania, Scholarly Commons, Department of Computer and Information Science, June 1988, unpublished.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/

Downloads

Published

2021-01-30

Issue

Vol. 9 No. 1 (2021): International Journal of Innovative Research in Computer Science and Technology (Jan)2021

Section

Articles

How to Cite

Meta-Modeling of AI for Software Modularization. (2021). International Journal of Innovative Research in Computer Science & Technology, 9(1), 26–32. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/11700