Artificial Intelligence and Expert Systems

Artificial Intelligence and Expert Systems

Abhinav Maurya (Ph. #:9821618787 abhinav.maurya@yahoo.com)

Manisha Ankam (Ph. #:9920449969 manisha_ankam@yahoo.co.in)

T.Y.B.Tech.(Computers),

V.J.T.I.

Abstract

Artificial Intelligence is the study of computers so as to imbue them with the simulation of human reasoning.

Artificial Intelligence was initially used to perform formal tasks such as game playing and theorem proving. Later it was used to tackle harder tasks such as natural language processing, building expert planning systems and truth maintenance systems, perception, speech recognition, etc.

The biggest advantage of Artificial Intelligence is the ability of its systems to adapt to new surroundings and solve new problems by learning about the problem at hand.

Expert systems are complex Artificial Intelligence programs which can construct expert knowledge bases, come up with a set of candidate solutions for a given problem belonging to their domain with the help of iterative heuristic

reasoning mechanisms, and use differentiating knowledge to determine which solution is best. Expert systems are intelligent because they can incorporate changes that enable the system to do the same task or tasks drawn from the same population more efficiently and more effectively the next time.

The paper addresses the nature of work being done in this field, and applications of AI and Expert Systems in developing breakthrough technology. It also presents a case-study underscoring the basic principles of Expert Systems.

1. An Introduction to Artificial Intelligence:

"Artificial intelligence is the art of creating machines that perform functions that require intelligence when performed by people." (Kurzweil)

Artificial Intelligence (AI) can be defined as the study of methods by which a computer can simulate aspects of human intelligence. The fundamental working hypothesis of AI is that intelligent behavior can be precisely described as symbol manipulation and can be modeled with the symbol processing capabilities of the computer. One aim of this study is to design a computer that might be able to reason for itself. A more "attainable" objective of work on AI is the development of systems that can work with natural language, meaning the language that we speak and write as distinct from any programmed computer language. Another aspect of AI is the ability of the computer to search knowledge in a database for the best possible reply to a question, because this has strong parallels with the way that we solve problems ourselves.

Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think".

In order to classify machines as "thinking", it is necessary to define intelligence. To what degree does intelligence consist of, for example, solving complex problems, or making generalizations and relationships? And what about perception and comprehension? Research into the areas of learning, of language, and of

sensory perception have aided scientists in building intelligent machines. One of the most challenging approaches facing experts is building systems that mimic the behavior of the

human brain, made up of billions of neurons, and arguably the most complex matter in the universe. Perhaps the best way to gauge the intelligence of a machine is British computer scientist Alan Turing's test. He stated that a computer would deserves to be called intelligent if it could deceive a human into believing that it was human.

2. Branches of AI:

Logical AI

What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. The program decides what to do by inferring that certain actions are appropriate for achieving its goals

Search

AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem proving