Artificial Intelligence and Expert Systems
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Artificial Intelligence and Expert Systems
Abhinav Maurya (Ph. #:9821618787 [email protected])
Manisha Ankam (Ph. #:9920449969 [email protected])
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 Turings 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 program. Discoveries are continually made about how to do this more efficiently in various domains.
Pattern Recognition
When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most.
Representation
Facts about the world have to be represented in some way. Usually languages of mathematical logic are used.
Inference
From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. For example, when we hear of a bird, we man infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning. Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic increasing function of the premises. Circumscription is another form of non-monotonic reasoning.
Common Sense Knowledge and Reasoning
This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. While