REPORT ON

APPLICATION OF EXPERT SYSTEMS

Submitted to: Ms. Alpa Reshamwala

Submitted by: Aashumita (351) Aditya shehkhar (371) Anshu Upadhyay (381)

Abstract :
An expert system is a computer program which captures the knowledge of a human expert on a given problem, and uses this knowledge to solve problems in a fashion similar to the expert. The system can assist the expert during problem-solving, or act in the place of the expert in those situations where the expertise is lacking. Expert systems have been developed in such diverse areas as science, engineering, business, and medicine. In these areas, they have increased the quality, efficiency, and competitive leverage of the organizations employing the technology.

Introduction :

Expert system technology has captured the interest of professionals in a number of fields in recent years. Systems have been developed in such diverse areas as science, engineering, business, and medicine. Almost every professional and computer society currently has a special interest group for expert systems technology. This widespread interest can be attributed to the ability of the expert system to aid various organizations in solving practical, real-world problems.

Expert system is an off spring of artificial intelligence. Al is the study of developing computer
Programs which exhibit human-like intelligence. Early Al researchers focused on such problems as game theory, robotic control, and vision systems. these studies showed that reasoning alone is not a sufficient measurement of intelligent behavior, but rather, one had to have a rich set of knowledge with which to reason. It was also determined that the problem needed to be well-focused, using only the knowledge relevant to a specific problem. These two requirements led Al researchers to use human experts for their source of problem-solving knowledge.

An expert system is a computer program designed to model the problem-solving ability of a human expert. The program models the following characteristics of the human expert:

1. Knowledge
2. Reasoning
3. Conclusions
4. Explanation.

Expert system structure :

The structure and operation of an expert system are modeled after the human expert. Experts use their knowledge about a given domain coupled with specific information about the current problem to arrive at a solution.

1. Knowledge base:

The knowledge base contains specialized knowledge on a given subject that makes the human a true expert on the subject. This knowledge is obtained from the human expert and encoded in the knowledge base using one of several knowledge representation techniques. One of the most common techniques used today for representing the knowledge in an expert system is rules.

Representing knowledge in rules has two major advantages. First, each rule is a separate declarative statement about the problem, allowing one to add rules to the system as needed. Secondly, rules appear to match the way many experts formulate their knowledge about a problem in a natural "cause and effect" manner. Other knowledge representation techniques used are frames, semantic networks, and predicate calculus .

2. Working memory:

Specific information on a current problem is represented as case facts and entered in the expert system's working memory. The 'working memory contains both the facts entered by the user from questions asked by the expert system, and facts inferred by the system. The working memory could also acquire information from databases, spreadsheets, or sensors, and be used by the expert system to conclude additional information about the problem by using the general knowledge contained in the knowledge base.

3. Inference engine:

The analogy of human reasoning is performed in the expert system with the inference engine. The role of the inference engine is to work with the available information contained in the working memory and the general knowledge contained in the knowledge base to derive new information about the problem.

Two principle inference techniques are employed in the design of an expert system. The first technique relies upon first establishing a goal or hypothesis, and then attempting to prove it true. For example, a technician believes a particular fault exists, then collects data to verify this hypothesis. This style of reasoning is known as backward chaining. The second style of inference first collects information about the problem and then attempts to infer other information . This style of reasoning is known as forward chaining.

4. Explanation Facility:

Besides providing final results or conclusions, both human experts and expert systems can explain how they arrived at their results. This capability is often important because the types of problems to which expert systems are applied require that a justification of the results be provided to the user.

Expert systems also have the capability of explaining why a given question is being asked. When an individual consults with a human expert, the conversation is highly interactive, and on occasion, the individual may ask why a certain line of reasoning is being pursued. The explanation given can make the user feel more comfortable with the line of questioning and also help to clarify what issues
The expert believes are important for the problem.

Conventional Program vs. Expert system :

It is important to understand and appreciate the differences between conventional computer programs and knowledge processing or expert systems. Knowledge processing represents an evolution, rather than a revolution, in the way individuals and computers interact to solve problems.

The most basic difference between the two is that conventional programs process data, while expert systems process knowledge. This basic difference influences both the nature of the processing technique used and the results obtained.

Conventional programs process data which is usually in numeric form, while an expert system works with symbolic information. Data are isolated bits of information about a problem, whereas symbolic information represents statements or facts concerning the problem which can be used with general knowledge to infer new information. Conventional programs process data by means of algorithms, whereas an expert system will use heuristic reasoning techniques.

A conventional program requires complete and precise information. An expert system can work with the available information whether it is incomplete or uncertain. In this sense, an expert system can provide some results even under the constraints of limited or uncertain information.
A conventional program would be severely limited under such constraints. The interface of an expert system permits questions to be asked and answers given using a natural language style.

Conventional programs provide a final solution usually in the form of a result from a computation. The computation may have involved a complex series of tasks, but the user will only see the final result and not the intermediate steps that led to the final result. Expert systems provide a result in the form of a recommendation, with a justification in the form of a tracing of its reasoning.

Expert systems work on types of problems which are less structured than conventional programs, and the information available may not be sufficient to obtain an exact solution. However, the expert system will still be able to reach some reasonable conclusion, even if it is not optimal, whereas a conventional program will fail if not provided with all of the information it needs. This ability of an expert system to be able to make decisions in the absence of complete or certain information is the result of developments in the area of inexact reasoning.
Expert system application areas:

1. Chemistry:

DENDRAL is capable of inferring the molecular structure of an unknown compound from the mass
Spectrogram data. SPEX (Iwasaki 1982) assists scientists in planning laboratory experiments in the area of molecular biology. The scientist defines and describes the various objects to be used in an experiment, such as the physical environment and the structure of the experiment. The system assists in developing a plan for achieving the goal of the experiment.
GA1 (Stefik 1978) determines possible DNA structures from restriction enzyme segmentation data. The system uses a model of enzyme digestion analysis of DNA structures, coupled with knowledge of possible errors in laboratory test environments, to formulate its result.

2. Agriculture:

In the area of agriculture, expert systems have been applied to such problems as crop management, insect control, and productivity considerations for raising a given crop. PLANT (Boulanger 1983) predicts the damage to corn caused by the invasion of black cutworms. The system first obtains information on the current field situation, including such information as the concentration of weeds, soil condition, and corn variety being grown. This information, coupled with black worm simulation programs, is used to predict the expected level of damage from this pest.

COMAX (Baker and Lemmon 1985) incorporates the knowledge of a model of cotton production to provide advice on the growing and management of this crop. CROPPRO (Durkin and Godine 1989) was developed to aid farmers in four major areas of crop production such as: crop management problems, pest control, financial considerations, and tutoring on various crop topics.

3. Geology:

The dominant use of expert systems in the area of geology has been applied to the problem of exploration.
Expert systems can aid a geophysicist in the interpretation of survey data or act in their place for those situations where one is not available. PROSPECTOR was an expert system developed at the Stanford Research Institute to aid geologists in the exploration of ore deposits (Duda et al. 1977). PROSPECTOR uses knowledge based on five different models which describe various mineral deposits.

DIPMETER (Davis et al. 1981) determines the subsurface geological structure of a given site by interpreting dipmeter logs. The system uses knowledge about dipmeter data and basic geology to uncover features in the data that aid in the identification of geological structures. This capability is of particular importance in oil or mineral exploration.

4. Space Technology:

ECESIS (Dickey and Toussaint 1984) controls the life support systems aboard a manned space station. NAVEX (Marsh 1984) monitors radar data that estimates the velocity and position of the space shuttle. The system detects any errors or predicts if a problem may occur.

References :

Baker, J. and H. Lemmon 1985 Expert Systems For Agriculture, Computers and Electronics in Agriculture. 1: 31-40.

Barr, A. and E. A. Feigenbaum (eds.) 1981 The Handbook of Artificial Intelligence, Vol. 1. William Kaufman Publishing.

Bennett, J. and C. Hollander 1981 DART: An Expert System for Computer Fault Diagnosis. Proceedings IJCAI-81. p. 843-845.

Boulanger, A. G. 1983 The Expert System PLANT/CD: A Case Study in Applying the General Purpose Inference System ADVISE to
Predicting Black Cutworm Damage in Corn. M. S. Thesis, Computer Science Dept., Univ. of Illinois at Champaign-Urbana.

Buchanan, B. and E. Feigenbaum 1978 DENDRAL and Meta- DENDRAL: Their Applications Dimension. Artificial Intelligence. 11:
5-24. Davis, R., H. Austin, I. Carlbom, B. Frawley, P. Pruchnik, R. Sneiderman, and J. Gilreath 1981 The Dipmeter Advisor: Interpretation of
Geological Signals. Proceedings IJCAI-81. p. 846-849.

Dickey, F.J. and A. L.Toussaint 1984 ECESIS: An Application of Expert Systems to Manned Space Stations. Proceedings of the Fifth Conf. on
Artificial Intelligence, IEEE Computer Society, p. 173-178.

Dincbas, M. 1980 A Knowledge-based Expert System For Automatic Analysis and Synthesis in CAD. Information Processing 80, IFIPS
Proceedings, p. 705-710. Duda, R., P. E. Hart, N. J. Nilsson,