EXPLORING THE LANDSCAPE OF EXPERT SYSTEMS: A REVIEW

Dusanka Barisic

doi:10.58898/ijmt.v4i1.58-68

Authors

Dusanka Barisic DTS, Nova Pazova, Serbia

DOI:

https://doi.org/10.58898/ijmt.v4i1.58-68

Keywords:

Expert Systems, Knowledge Representation, Reasoning, Decision Support, Artificial Intelligence

Abstract

Expert systems are computer programs designed to mimic the decision-making of human experts. This paper explores the fundamental components of ES, including knowledge acquisition, representation, and reasoning. Various techniques for acquiring knowledge, such as interviews, observation, and document analysis, are discussed, along with prominent knowledge representation schemes like production rules, semantic networks, frames, and ontologies. The reasoning process, including inference methods and explanation facilities, is also examined. The paper further analyses the challenges and limitations of ES, such as the difficulty in capturing common sense reasoning and the complexity of knowledge base maintenance. Finally, it explores future research directions, including the integration of emerging technologies like big data and cloud computing, the development of more transparent and explainable ES, and addressing ethical considerations surrounding bias and accountability. This comprehensive overview provides a foundational understanding of expert systems, their capabilities, limitations, and potential future advancements.

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References

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-159. https://doi.org/10.48550/arXiv.1712.03586

Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-Based Expert Systems: The MYCIN Experiment. Addison-Wesley.

Durkin, J. (1994). Expert Systems: Design and Development. Macmillan.

Feigenbaum, E. A., & McCorduck, P. (1983). The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World. Addison-Wesley.

Giarratano, J. C., & Riley, G. D. (2022). Expert Systems: Principles and Programming (2nd ed.). Prentice Hall.

Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199-220. DOI: 10.1006/knac.1993.1008

Hasan, P., & Pawan, E. (2021). Expert System for Early Diagnosis of Tropical Malaria and Tertiana Malaria using Certainty Factor. In International Journal of Computer and Information System (IJCIS) (Vol. 2, Issue 4, p. 155). https://doi.org/10.29040/ijcis.v2i4.49

Jackson, P. (1998). Introduction to Expert Systems (3rd ed.). Addison-Wesley.

Kolodner, J. (1993). Case-Based Reasoning. Morgan Kaufmann.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. DOI: 10.1038/nature14539

Lenat, D. B. (1995). CYC: A large-scale investment in knowledge infrastructure. Communications of the ACM, 38(11), 33-38. DOI: 10.1145/219717.219745

Luger, G. F. (2005). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). Pearson.

Minsky, M. (1975). A framework for representing knowledge. In P. H. Winston (Ed.), The Psychology of Computer Vision (pp. 211-277). McGraw-Hill.

Newell, A., & Simon, H. A. (1972). Human Problem Solving. Prentice Hall.

Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.

Stankić, R. (2010). Electronic Business. Faculty of Economics, Belgrade.

Tanenbaum, A. S. (2011). Structured Computer Organization (6th ed.). Pearson.

Turban, E., & Aronson, J. E. (2001). Decision Support Systems and Intelligent Systems (6th ed.). Prentice Hall.

Veljović, A. (2018). Management Information Systems. Technical Faculty in Čačak.

Veljović, A. (2019). Expert Systems. CET Čitalište 72.

Zadeh, L. A. (1996). Fuzzy sets, fuzzy logic, and fuzzy systems: Selected papers. Advances in Fuzzy Systems—Applications and Theory, 6, 1-762.

Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An overview of blockchain technology: Architecture, consensus, and future trends. Proceedings of the 2017 IEEE International Congress on Big Data (BigData Congress), 557-564. 10.1109/BigDataCongress.2017.85