Expert Systems and Hybrid AI Systems

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1. EXPERT SYSTEMS

In 1956, John McCarthy proposed the use of the term, Artificial Intelligence (AI) to describe computers with the ability to mimic or duplicate the functions of the human brain. AI is the study of ideas that enable computers to be intelligent. In other words, AI means programming a computer to perform activities that if done by a person would be thought to require intelligence (Winston, 1984).

The goals of the field of AI can be defined as:

• To make computers more useful
• To understand the principles that makes intelligence possible

One application of AI extensively used in businesses for various decision support activities is Expert Systems (ES).

When an organisation has a complex decision to make or problem to solve, it often turns to experts for advice. These experts have specific knowledge and experience in the problem area. They are aware of the alternatives, the chances of success, and the costs the business may incur. Companies engage experts for advice on such matters as which equipment to buy, merges and acquisitions, and advertising strategy. The more unstructured the situation, the more specialised (and expensive) is the advice.

Typically ES is a decision making and/or problem solving package of computer hardware and software that can reach a level of performance comparable to – or even exceeding that of – a human expert in some specialised and usually narrow problem area.

It is a system that captures knowledge, experience, and judgement of skilled professionals to solve problems that normally require human expertise. These systems may be used by non-experts to improve their problem solving capabilities. Experts may use them as knowledgeable assistants.

The difference between a DSS and an ES is that an ES uses rule-based or expert knowledge to solve problems. The ES acts like an expert consultant, asking for information, applying this information to the rules it has learned, and drawing conclusions.

2. HYBRID AI SYSTEMS

Systems with intelligence have become increasingly more valuable in business information systems and in the decision making process. A hybrid AI systems is a system where a number of AI technologies are integrated together into a single application so as to take the advantage of the best features of each technology. For example: the difficulties in acquiring and representing knowledge in expert systems can be offset by how neural networks learn from sample data. Similarly, an ES’s limited ability to handle incomplete or ambitious data can be offset by a fuzzy logic system’s ability to process imprecise data values to better describe a real-world problem situation.

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Reference(s)
	Book		Stair, R. M. & Reynolds, G. W. (1999) Principles of Information Systems: A Managerial Approach. 4th Edition. Thomson Course Technology: United States of America (USA), Massachusetts (MA), Middlesex, Cambridge. [ISBN: 9780760010792]. [Available on: Amazon: https://amzn.to/3F8ncY4].
	Book		Winston, P. H. (1984) Artificial Intelligence. 2nd Edition. Pearson Education - Addison-Wesley: United States of America (USA), Massachusetts (MA), Middlesex, Reading. [ISBN: 9780201533774]. [Available on: Amazon: https://amzn.to/3NcJ2fe].

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Reference (or cite) Article
	Kahlon, R. S. (2012) Expert Systems & Hybrid AI Systems [Online]. dkode: United Kingdom, England, London. [Published on: 2012-05-21]. [Article ID: RSK666-0000043]. [Available on: dkode | Ravi - https://ravi.dkode.co/2012/05/expert-systems-hybrid-ai-systems.html].

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