An Introduction To Expert Systems

An Insight into Expert Systems

Abstract

To improve speed of operations, programming practices for practical purposes are moving away from the data centric, procedural problem solving paradigm to a heuristic, declarative problem solving paradigm. Though theoretically there is no guarantee that a solution shall be found and even if it is found, that it be correct, practically it has been proven that expert systems employing , heuristics are indeed a faster and more effective manner of problem solving , with an added advantage of having an explanation for the answer arrived at. Having started out as a diagnostic tool, it has now found acceptance all over, be it Manufacturing Firms or IT Solution Providers and is definitely here to stay. Its dependence on Artificial Intelligence furthermore proves its capabilities to branch out to more areas of deployment. With the advent of commercial-off-the-shelf expert system development tools making the process of designing an expert system a simple task, now the real challenge lies with the experts to be able to put these their knowledge and expertise in their domain to effective use to create systems which can be put to use effectively.

Expert Systems are a branch of Artificial Intelligence that makes an extensive use of specialized knowledge to solve problems at the level of a human expert. AI's scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior.

The term intelligence covers many cognitive skills, including the ability to solve problems, learn, and understand language; AI addresses all of those. But most progress to date in AI has been made in the area of problem solving -- concepts and methods for building programs that reas...

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...tive. The IFE may also use a variety of techniques, particularly when carrying out the dialogue with the user to produce the specification of the user's problem.

Research has flattened out when compared to the days of its inception as a practice, as more efforts have been employed in tapping its commercial value. To maximize this, other systems such as database and fuzzy logic systems are being embedded into expert systems.

Drawbacks

Expert systems are said to have a narrow domain and limited focus. Also they do not have a learning ability which is something AI systems are expected to. They require rigorous maintenance procedures and incur huge developmental costs.

Bibliography

1. Knowledge Based Systems in Japan

(http://www.wtec.org/loyola/kb/)

2. Databases and Artificial Intelligence 3 by Alison Cawsey (http://www.macs.hw.ac.uk/~alison/ai3notes/)