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An expert system is a software system that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence. A software system is a System based on Software forming part of a Computer system (a combination of hardware and software An "expert" ( is someone widely recognized as a reliable source of technique or Skill whose faculty for judging or deciding rightly justly A problem domain is a domain where the Parameters defining the boundaries of the domain and sufficient Mappings into a Set of Ranges including A wide variety of methods can be used to simulate the performance of the expert however common to most or all are 1) the creation of a so-called "knowledgebase" which uses some knowledge representation formalism to capture the subject matter experts (SME) knowledge and 2) a process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering. A knowledge base (or knowledgebase; abbreviated KB, kb or Δ is a special kind of Database for Knowledge management. Knowledge representation is an area in Artificial intelligence that is concerned with how to formally "think" that is how to use a symbol system to represent Knowledge engineering (KE has been defined by Feigenbaum and McCorduck (1983 as follows ""KE is an engineering discipline that involves integrating knowledge into Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.

As a premiere application of computing and artificial intelligence, the topic of expert systems has many points of contact with general systems theory, operations research, business process reengineering and various topics in applied mathematics and management science. Systems theory is an Interdisciplinary field of Science and the study of the nature of Complex systems in Nature, Society, and Operations Research (OR in North America South Africa and Australia and Operational Research in Europe is an interdisciplinary branch of applied Mathematics and Business process reengineering (BPR is a Management approach aiming at improvements by means of elevating efficiency and effectiveness of the processes Applied mathematics is a branch of Mathematics that concerns itself with the mathematical techniques typically used in the application of mathematical knowledge to other domains Management science (MS, is the discipline of using Mathematical modeling and other analytical methods to help make better business Management decisions

Two illustrations of actual expert systems can give an idea of how they work. In one real world case at a chemical refinery a senior employee was about to retire and the company was concerned that the loss of his expertise in managing a fractionating tower would severely impact operations of the plant. A knowledge engineer was assigned to produce an expert system reproducing his expertise saving the company the loss of the valued knowledge asset. Similary a system called Mycin was developed from the expertise of best diagnosticians of bacterial infections whose performance was found to be as good or better than the average clinician. MYCIN was an early Expert system developed over five or six years in the early 1970s at Stanford University. An early commercial success and illustration of another typical application (a task generally considered overly complex for a human) was an expert system fielded by DEC in the 1980s to quality check the configurations of their computers prior to delivery. The 1980s was the decade spanning from January 1 1980 to December 31 1989. The eighties were the time of greatest popularity of expert systems and interest lagged after the onset of the AI Winter. See also History of artificial intelligence the first AI winter and the second AI winter An AI Winter is a collapse in the perception

Contents

Overview

The most common form of expert system is a computer program, with a set of rules, that analyzes information (usually supplied by the user of the system) about a specific class of problems, and recommends one or more courses of user action. A business rules engine is a Software system that executes one or more Business rules in a runtime production environment In Logic, a rule of inference (also called a transformation rule) is a function from sets of formulae to formulae The expert system may also provide mathematical analysis of the problem(s). Analysis has its beginnings in the rigorous formulation of Calculus. The expert system utilizes what appears to be reasoning capabilities to reach conclusions.

A related term is wizard. A wizard is a User interface element where the user is presented with a sequence of dialog boxes. A wizard is an interactive computer program that helps a user solve a problem. Originally the term wizard was used for programs that construct a database search query based on criteria supplied by the user. A Computer Database is a structured collection of records or data that is stored in a computer system However, some rule-based expert systems are also called wizards. Other "Wizards" are a sequence of online forms that guide users through a series of choices, such as the ones which manage the installation of new software on computers, and these are not expert systems.

Prominent expert systems and languages

The study of expert systems

Knowledge representation

Main article: Knowledge representation

Knowledge representation is an issue that arises in both cognitive science and artificial intelligence. Knowledge representation is an area in Artificial intelligence that is concerned with how to formally "think" that is how to use a symbol system to represent Cognitive science may be broadly defined as the multidisciplinary study of mind and behavior In cognitive science, it is concerned with how people store and process information. In artificial intelligence (AI) the primary aim is to store knowledge so that programs can process it and achieve the verisimilitude of human intelligence. AI researchers have borrowed representation theories from cognitive science. Thus there are representation techniques such as frames, rules and semantic networks which have originated from theories of human information processing. Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to represent knowledge in a manner as to facilitate inferencing i. e. drawing conclusions from knowledge.

Knowledge engineer

Main article: Knowledge engineers

Knowledge engineers are concerned with the representation chosen for the expert's knowledge declarations and with the inference engine used to process that knowledge. Knowledge engineers are computer systems experts who are trained in the field of Expert systems. Knowledge representation is an area in Artificial intelligence that is concerned with how to formally "think" that is how to use a symbol system to represent In Computer science, and specifically the branches of Knowledge engineering and Artificial intelligence, an inference engine is a Computer program He / she can use the knowledge acquisition component of the expert system to input the several characteristics known to be appropriate to a good inference technique, including:

Expert systems topics

Chaining

There are two main methods of reasoning when using inference rules: backward chaining and forward chaining.

Forward chaining starts with the data available and uses the inference rules to conclude more data until a desired goal is reached. Forward chaining is one of the two main methods of reasoning when using Inference rules (in Artificial intelligence) A goal or objective consists of a projected state of affairs which a Person or a System plans or intends to achieve or bring about — a personal or An inference engine using forward chaining searches the inference rules until it finds one in which the if-clause is known to be true. In Logic and Mathematics, a logical value, also called a truth value, is a value indicating the extent to which a Proposition is true It then concludes the then-clause and adds this information to its data. Debt AIDS Trade in Africa (or DATA) is a Multinational non-government organization founded in January 2002 in London by U2 's It would continue to do this until a goal is reached. Because the data available determines which inference rules are used, this method is also called data driven.

Backward chaining starts with a list of goals and works backwards to see if there is data which will allow it to conclude any of these goals. Backward chaining (or backward reasoning) is an inference method used in Artificial intelligence. An inference engine using backward chaining would search the inference rules until it finds one which has a then-clause that matches a desired goal. If the if-clause of that inference rule is not known to be true, then it is added to the list of goals. For example, suppose a rulebase contains two rules:

(1) If Fritz is green then Fritz is a frog.
(2) If Fritz is a frog then Fritz hops.

Suppose a goal is to conclude that Fritz hops. The rulebase would be searched and rule (2) would be selected because its conclusion (the then clause) matches the goal. It is not known that Fritz is a frog, so this "if" statement is added to the goal list. The rulebase is again searched and this time rule (1) is selected because its then clause matches the new goal just added to the list. This time, the if-clause (Fritz is green) is known to be true and the goal that Fritz hops is concluded. Because the list of goals determines which rules are selected and used, this method is called goal driven.

Certainty factors

One advantage of expert systems over traditional methods of programming is that they allow the use of "confidences" (or "certainty factors"). When a human reasons he does not always conclude things with 100% confidence. He might say, "If Fritz is green, then he is probably a frog" (after all, he might be a chameleon). This type of reasoning can be imitated by using numeric values called confidences. For example, if it is known that Fritz is green, it might be concluded with 0. 85 confidence that he is a frog; or, if it is known that he is a frog, it might be concluded with 0. 95 confidence that he hops. These numbers are similar in nature to probabilities, but they are not the same. Nature, in the broadest sense is equivalent to the natural world, physical universe, material world or material universe. Probability is the likelihood or chance that something is the case or will happen They are meant to imitate the confidences humans use in reasoning rather than to follow the mathematical definitions used in calculating probabilities.

Expert system architecture

The following general points about expert systems and their architecture have been illustrated.

1. The sequence of steps taken to reach a conclusion is dynamically synthesized with each new case. It is not explicitly programmed when the system is built.
2. Expert systems can process multiple values for any problem parameter. This permits more than one line of reasoning to be pursued and the results of incomplete (not fully determined) reasoning to be presented.
3. Problem solving is accomplished by applying specific knowledge rather than specific technique. Problem solving forms part of thinking. Considered the most complex of all intellectual functions problem solving has been defined as higher-order Cognitive This is a key idea in expert systems technology. It reflects the belief that human experts do not process their knowledge differently from others, but they do possess different knowledge. With this philosophy, when one finds that their expert system does not produce the desired results, work begins to expand the knowledge base, not to re-program the procedures. In Mathematics, Computing, Linguistics and related subjects an algorithm is a sequence of finite instructions often used for Calculation

There are various expert systems in which a rulebase and an inference engine cooperate to simulate the reasoning process that a human expert pursues in analyzing a problem and arriving at a conclusion. In these systems, in order to simulate the human reasoning process, a vast amount of knowledge needed to be stored in the knowledge base. Generally, the knowledge base of such an expert system consisted of a relatively large number of "if then" type of statements that were interrelated in a manner that, in theory at least, resembled the sequence of mental steps that were involved in the human reasoning process.

Because of the need for large storage capacities and related programs to store the rulebase, most expert systems have, in the past, been run only on large information handling systems. Recently, the storage capacity of personal computers has increased to a point where it is becoming possible to consider running some types of simple expert systems on personal computers. A personal computer ( PC) is any Computer whose original sales price size and capabilities make it useful for individuals and which is intended to be operated

In some applications of expert systems, the nature of the application and the amount of stored information necessary to simulate the human reasoning process for that application is just too vast to store in the active memory of a computer. Application software is a subclass of Computer software that employs the capabilities of a computer directly and thoroughly to a task that the user wishes to perform Computer data storage, often called storage or memory, refers to Computer components devices and recording media that retain digital A computer is a Machine that manipulates data according to a list of instructions. In other applications of expert systems, the nature of the application is such that not all of the information is always needed in the reasoning process. An example of this latter type application would be the use of an expert system to diagnose a data processing system comprising many separate components, some of which are optional. When that type of expert system employs a single integrated rulebase to diagnose the minimum system configuration of the data processing system, much of the rulebase is not required since many of the components which are optional units of the system will not be present in the system. Nevertheless, earlier expert systems require the entire rulebase to be stored since all the rules were, in effect, chained or linked together by the structure of the rulebase.

When the rulebase is segmented, preferably into contextual segments or units, it is then possible to eliminate portions of the Rulebase containing data or knowledge that is not needed in a particular application. The segmenting of the rulebase also allows the expert system to be run with systems or on systems having much smaller memory capacities than was possible with earlier arrangements since each segment of the rulebase can be paged into and out of the system as needed. The segmenting of the rulebase into contextual segments requires that the expert system manage various intersegment relationships as segments are paged into and out of memory during execution of the program. Since the system permits a rulebase segment to be called and executed at any time during the processing of the first rulebase, provision must be made to store the data that has been accumulated up to that point so that at some time later in the process, when the system returns to the first segment, it can proceed from the last point or rule node that was processed. Also, provision must be made so that data that has been collected by the system up to that point can be passed to the second segment of the rulebase after it has been paged into the system and data collected during the processing of the second segment can be passed to the first segment when the system returns to complete processing that segment.

The user interface and the procedure interface are two important functions in the information collection process. The user interface (or Human Computer Interface) is the aggregate of means by which people&mdash the users '&mdash interact with the System

End user

The end-user usually sees an expert system through an interactive dialog, an example of which follows:

Q. Do you know which restaurant you want to go to?
A. No
Q. Is there any kind of food you would particularly like?
A. No
Q. Do you like spicy food?
A. No
Q. Do you usually drink wine with meals?
A. Yes
Q. When you drink wine, is it French wine?
A. Why

As can be seen from this dialog, the system is leading the user through a set of questions, the purpose of which is to determine a suitable set of restaurants to recommend. A dialogue (sometimes spelled dialog) is a reciprocal Conversation between two or more entities. A question may be either a linguistic expression used to make a request for Information, or else the request itself made by such an expression This dialog begins with the system asking if the user already knows the restaurant choice (a common feature of expert systems) and immediately illustrates a characteristic of expert systems; users may choose not to respond to any question. In expert systems, dialogs are not pre-planned. There is no fixed control structure. In Computer science control flow (or alternatively flow of control refers to the order in which the individual statements, instructions or Function Dialogs are synthesized from the current information and the contents of the knowledge base. Because of this, not being able to supply the answer to a particular question does not stop the consultation.

Explanation system

Another major distinction between expert systems and traditional systems is illustrated by the following answer given by the system when the user answers a question with another question, "Why", as occurred in the above example. The answer is:

A. I am trying to determine the type of restaurant to suggest. So far Chinese is not a likely choice. It is possible that French is a likely choice. I know that if the diner is a wine drinker, and the preferred wine is French, then there is strong evidence that the restaurant choice should include French.

It is very difficult to implement a general explanation system (answering questions like "Why" and "How") in a traditional computer program. An expert system can generate an explanation by retracing the steps of its reasoning. The response of the expert system to the question WHY is an exposure of the underlying knowledge structure. It is a rule; a set of antecedent conditions which, if true, allow the assertion of a consequent. In Proof theory, a sequent is a formalized statement of provability that is frequently used when specifying calculi for deduction. A consequent is the second half of a hypothetical Proposition. The rule references values, and tests them against various constraints or asserts constraints onto them. This, in fact, is a significant part of the knowledge structure. There are values, which may be associated with some organizing entity. An entity is something that has a distinct separate Existence, though it need not be a material existence For example, the individual diner is an entity with various attributes (values) including whether they drink wine and the kind of wine. There are also rules, which associate the currently known values of some attributes with assertions that can be made about other attributes. In Computer science, a value is a sequence of Bits that is interpreted according to some Data type. It is the orderly processing of these rules that dictates the dialog itself.

Expert systems versus problem-solving systems

The principal distinction between expert systems and traditional problem solving programs is the way in which the problem related expertise is coded. Problem solving forms part of thinking. Considered the most complex of all intellectual functions problem solving has been defined as higher-order Cognitive In traditional applications, problem expertise is encoded in both program and data structures.

In the expert system approach all of the problem related expertise is encoded in data structures only; none is in programs. A data structure in Computer science is a way of storing Data in a computer so that it can be used efficiently This organization has several benefits.

An example may help contrast the traditional problem solving program with the expert system approach. The example is the problem of tax advice. In the traditional approach data structures describe the taxpayer and tax tables, and a program in which there are statements representing an expert tax consultant's knowledge, such as statements which relate information about the taxpayer to tax table choices. It is this representation of the tax expert's knowledge that is difficult for the tax expert to understand or modify.

In the expert system approach, the information about taxpayers and tax computations is again found in data structures, but now the knowledge describing the relationships between them is encoded in data structures as well. The programs of an expert system are independent of the problem domain (taxes) and serve to process the data structures without regard to the nature of the problem area they describe. A problem domain is a domain where the Parameters defining the boundaries of the domain and sufficient Mappings into a Set of Ranges including For example, there are programs to acquire the described data values through user interaction, programs to represent and process special organizations of description, and programs to process the declarations that represent semantic relationships within the problem domain and an algorithm to control the processing sequence and focus. In Programming languages, a declaration specifies the Identifier, type, and other aspects of language elements such as variables and functions In Mathematics, Computing, Linguistics and related subjects an algorithm is a sequence of finite instructions often used for Calculation

The general architecture of an expert system involves two principal components: a problem dependent set of data declarations called the knowledge base or rule base, and a problem independent (although highly data structure dependent) program which is called the inference engine. The term architecture (from Greek αρχιτεκτονικήarchitektoniki) can be used to mean a process a profession or documentation Rule base is arranges of rules which control judgement of what it is true and not In Computer science, and specifically the branches of Knowledge engineering and Artificial intelligence, an inference engine is a Computer program

Individuals involved with expert systems

There are generally three individuals having an interaction with expert systems. Primary among these is the end-user; the individual who uses the system for its problem solving assistance. Economics and Commerce define an end-user as the person who uses a product. In the building and maintenance of the system there are two other roles: the problem domain expert who builds and supplies the knowledge base providing the domain expertise, and a knowledge engineer who assists the experts in determining the representation of their knowledge, enters this knowledge into an explanation module and who defines the inference technique required to obtain useful problem solving activity. A domain expert or subject matter expert (SME is a person with special knowledge or skills in a particular area Knowledge engineers are computer systems experts who are trained in the field of Expert systems. Knowledge representation is an area in Artificial intelligence that is concerned with how to formally "think" that is how to use a symbol system to represent Explanation modules, used in Expert systems, is a function that enables the Knowledge worker to understand why the information explained and concluded by the Inference is the act or process of deriving a Conclusion based solely on what one already knows Usually, the knowledge engineer will represent the problem solving activity in the form of rules which is referred to as a rule-based expert system. Knowledge engineers are computer systems experts who are trained in the field of Expert systems. Logic programming is in its broadest sense the use of mathematical logic for computer programming When these rules are created from the domain expertise, the knowledge base stores the rules of the expert system.

Inference rule

An understanding of the "inference rule" concept is important to understand expert systems. In Logic, a rule of inference (also called a transformation rule) is a function from sets of formulae to formulae An inference rule is a statement that has two parts, an if-clause and a then-clause. This rule is what gives expert systems the ability to find solutions to diagnostic and prescriptive problems. In Linguistics, prescription can refer both to the codification and the enforcement of rules governing how a language is to be used An example of an inference rule is:

If the restaurant choice includes French, and the occasion is romantic,
Then the restaurant choice is definitely Paul Bocuse. Paul Bocuse (born on February 11, 1926 in Collonges-au-Mont-d'Or near Lyon) is a French Chef, considered

An expert system's rulebase is made up of many such inference rules. They are entered as separate rules and it is the inference engine that uses them together to draw conclusions. A conclusion is a Proposition, which is arrived at after the consideration of Evidence, Arguments or Premises Logic Because each rule is a unit, rules may be deleted or added without affecting other rules (though it should affect which conclusions are reached). One advantage of inference rules over traditional programming is that inference rules use reasoning which more closely resemble human reasoning. Reasoning is the cognitive process of looking for Reasons for beliefs conclusions actions or feelings

Thus, when a conclusion is drawn, it is possible to understand how this conclusion was reached. Furthermore, because the expert system uses knowledge in a form similar to the expert, it may be easier to retrieve this information from the expert. An "expert" ( is someone widely recognized as a reliable source of technique or Skill whose faculty for judging or deciding rightly justly

Procedure node interface

The function of the procedure node interface is to receive information from the procedures coordinator and create the appropriate procedure call. In Computer science, a subroutine ( function, method, procedure, or subprogram) is a portion of code within a larger The ability to call a procedure and receive information from that procedure can be viewed as simply a generalization of input from the external world. In Computer science, a subroutine ( function, method, procedure, or subprogram) is a portion of code within a larger Generalization is a foundational element of Logic and human reasoning. Input is the term denoting either an entrance or changes which are inserted into a System and which activate/modify a Process. While in some earlier expert systems external information has been obtained, that information was obtained only in a predetermined manner so only certain information could actually be acquired. This expert system, disclosed in the cross-referenced application, through the knowledge base, is permitted to invoke any procedure allowed on its host system. This makes the expert system useful in a much wider class of knowledge domains than if it had no external access or only limited external access.

In the area of machine diagnostics using expert systems, particularly self-diagnostic applications, it is not possible to conclude the current state of "health" of a machine without some information. The best source of information is the machine itself, for it contains much detailed information that could not reasonably be provided by the operator.

The knowledge that is represented in the system appears in the rulebase. In the rulebase described in the cross-referenced applications, there are basically four different types of objects, with associated information present.

  1. Classes--these are questions asked to the user.
  2. Parameters--a parameter is a place holder for a character string which may be a variable that can be inserted into a class question at the point in the question where the parameter is positioned.
  3. Procedures--these are definitions of calls to external procedures.
  4. Rule Nodes--The inferencing in the system is done by a tree structure which indicates the rules or logic which mimics human reasoning. The nodes of these trees are called rule nodes. There are several different types of rule nodes.

The rulebase comprises a forest of many trees. The top node of the tree is called the goal node, in that it contains the conclusion. Each tree in the forest has a different goal node. The leaves of the tree are also referred to as rule nodes, or one of the types of rule nodes. A leaf may be an evidence node, an external node, or a reference node.

An evidence node functions to obtain information from the operator by asking a specific question. In responding to a question presented by an evidence node, the operator is generally instructed to answer "yes" or "no" represented by numeric values 1 and 0 or provide a value of between 0 and 1, represented by a "maybe. "

Questions which require a response from the operator other than yes or no or a value between 0 and 1 are handled in a different manner.

A leaf that is an external node indicates that data will be used which was obtained from a procedure call.

A reference node functions to refer to another tree or subtree.

A tree may also contain intermediate or minor nodes between the goal node and the leaf node. An intermediate node can represent logical operations like And or Or. Table of logic symbolsIn Logic, two sentences (either in a formal language or a natural language may be joined by means of a logical connective to form a compound sentence

The inference logic has two functions. It selects a tree to trace and then it traces that tree. A tree is a perennial Woody plant. It is most often defined as a woody plant that has many secondary branches supported clear of the ground on a single main stem or Once a tree has been selected, that tree is traced, depth-first, left to right.

The word "tracing" refers to the action the system takes as it traverses the tree, asking classes (questions), calling procedures, and calculating confidences as it proceeds.

As explained in the cross-referenced applications, the selection of a tree depends on the ordering of the trees. The original ordering of the trees is the order in which they appear in the rulebase. This order can be changed, however, by assigning an evidence node an attribute "initial" which is described in detail in these applications. The first action taken is to obtain values for all evidence nodes which have been assigned an "initial" attribute. Using only the answers to these initial evidences, the rules are ordered so that the most likely to succeed is evaluated first. The trees can be further re-ordered since they are constantly being updated as a selected tree is being traced.

It has been found that the type of information that is solicited by the system from the user by means of questions or classes should be tailored to the level of knowledge of the user. In many applications, the group of prospective uses is nicely defined and the knowledge level can be estimated so that the questions can be presented at a level which corresponds generally to the average user. However, in other applications, knowledge of the specific domain of the expert system might vary considerably among the group of prospective users.

One application where this is particularly true involves the use of an expert system, operating in a self-diagnostic mode on a personal computer to assist the operator of the personal computer to diagnose the cause of a fault or error in either the hardware or software. In general, asking the operator for information is the most straightforward way for the expert system to gather information assuming, of course, that the information is or should be within the operator's understanding. For example, in diagnosing a personal computer, the expert system must know the major functional components of the system. An electronic component is a basic electronic element usually packaged in a discrete form with two or more connecting leads or metallic pads It could ask the operator, for instance, if the display is a monochrome or color display. A display device is an Output device for presentation of Information for Visual or Tactile reception acquired stored or transmitted The operator should, in all probability, be able to provide the correct answer 100% of the time. The expert system could, on the other hand, cause a test unit to be run to determine the type of display. The accuracy of the data collected by either approach in this instance probably would not be that different so the knowledge engineer could employ either approach without affecting the accuracy of the diagnosis. Knowledge engineering (KE has been defined by Feigenbaum and McCorduck (1983 as follows ""KE is an engineering discipline that involves integrating knowledge into However, in many instances, because of the nature of the information being solicited, it is better to obtain the information from the system rather than asking the operator, because the accuracy of the data supplied by the operator is so low that the system could not effectively process it to a meaningful conclusion.

In many situations the information is already in the system, in a form of which permits the correct answer to a question to be obtained through a process of inductive or deductive reasoning. An answer was originally a solemn assertion in opposition to some one or something and thus generally any counter-statement or defense a reply to a question or objection or a correct solution The data previously collected by the system could be answers provided by the user to less complex questions that were asked for a different reason or results returned from test units that were previously run.

User interface

The function of the user interface is to present questions and information to the user and supply the user's responses to the inference engine.

Any values entered by the user must be received and interpreted by the user interface. Some responses are restricted to a set of possible legal answers, others are not. The user interface checks all responses to insure that they are of the correct data type. Any responses that are restricted to a legal set of answers are compared against these legal answers. Whenever the user enters an illegal answer, the user interface informs the user that his answer was invalid and prompts him to correct it.

Application of expert systems

Expert systems are designed and created to facilitate tasks in the fields of accounting, medicine, process control, financial service, production, human resources etc. Accountancy or accounting is the measurement statement or provision of assurance about financial information primarily used by Lenders managers, Process control is a Statistics and Engineering discipline that deals with Architectures mechanisms and Algorithms for controlling Financial services refer to services provided by the finance industry. Manufacturing (from Latin manu factura, "making by hand" is the use of tools and labor to make things for use or sale Indeed, the foundation of a successful expert system depends on a series of technical procedures and development that may be designed by certain technicians and related experts.

A good example of application of expert systems in banking area is expert systems for mortgages. An Expert system for mortgages is a Computer program that contains the knowledge and analytical skills of human experts related to Mortgage banking Loan departments are interested in expert systems for mortgages because of the growing cost of labour which makes the handling and acceptance of relatively small loans less profitable. A mortgage is the pledging of a property to a Lender as a security for a Mortgage loan. They also see in the application of expert systems a possibility for standardised, efficient handling of mortgage loan, and appreciate that for the acceptance of mortgages there are hard and fast rules which do not always exist with other types of loans. A mortgage loan is a Loan secured by Real property through the use of a Mortgage (a legal instrument A mortgage is the pledging of a property to a Lender as a security for a Mortgage loan.

While expert systems have distinguished themselves in AI research in finding practical application, their application has been limited. Expert systems are notoriously narrow in their domain of knowledge—as an amusing example, a researcher used the "skin disease" expert system to diagnose his rustbucket car as likely to have developed measles—and the systems were thus prone to making errors that humans would easily spot. Knowledge is defined ( Oxford English Dictionary) variously as (i expertise and skills acquired by a person through experience or education the theoretical or practical understanding The word error has different meanings and usages relative to how it is conceptually applied Human beings, humans or man (Origin 1590–1600 L homō man OL hemō the earthly one (see Humus Additionally, once some of the mystique had worn off, most programmers realized that simple expert systems were essentially just slightly more elaborate versions of the decision logic they had already been using. A programmer is someone who writes Computer software. The term computer programmer can refer to a specialist in one area of computer programming or to a generalist Therefore, some of the techniques of expert systems can now be found in most complex programs without any fuss about them.

An example, and a good demonstration of the limitations of, an expert system used by many people is the Microsoft Windows operating system troubleshooting software located in the "help" section in the taskbar menu. Microsoft Windows is a series of Software Operating systems and Graphical user interfaces produced by Microsoft. An operating system (commonly abbreviated OS and O/S) is the software component of a Computer system that is responsible for the management and coordination Troubleshooting is a form of Problem solving. It is the systematic search for the source of a problem so that it can be solved In Computing, the taskbar is a term for an application desktop bar which is used to launch and monitor applications Obtaining expert / technical operating system support is often difficult for individuals not closely involved with the development of the operating system. Microsoft has designed their expert system to provide solutions, advice, and suggestions to common errors encountered throughout using the operating systems.

Another 1970s and 1980s application of expert systems — which we today would simply call AI — was in computer games. A personal computer Game (also known as a computer game or simply PC game) is a Video game played on a Personal computer, rather For example, the computer baseball games Earl Weaver Baseball and Tony La Russa Baseball each had highly detailed simulations of the game strategies of those two baseball managers. Baseball is a Bat-and-ball Sport played between two teams of nine players each Earl Weaver Baseball is a Baseball Computer game ( 1987) designed by Don Daglow and Eddie Dombrower and published by Electronic Tony La Russa Baseball is a Baseball computer and Video game console Sports game series (1991-1997 designed by Don Daglow When a human played the game against the computer, the computer queried the Earl Weaver or Tony La Russa Expert System for a decision on what strategy to follow. Earl Sidney Weaver (born August 14, 1930 in St Louis Missouri) is a former Major League Baseball manager. Anthony "Tony" La Russa Jr (ləˈɹuːsə born October 4 1944, in Tampa, Florida) is a manager in Major League Even those choices where some randomness was part of the natural system (such as when to throw a surprise pitch-out to try to trick a runner trying to steal a base) were decided based on probabilities supplied by Weaver or La Russa. Today we would simply say that "the game's AI provided the opposing manager's strategy. "

Advantages and disadvantages

Advantages:

Disadvantages:

Types of problems solved by expert systems

Expert systems are most valuable to organizations that have a high-level of know-how experience and expertise that cannot be easily transferred to other members. In the context of industrial property now generally viewed as intellectual property (IP know-how (or knowhow as it is sometimes written is a component in the transfer They are designed to carry the intelligence and information found in the intellect of experts and provide this knowledge to other members of the organization for problem-solving purposes. Problem solving forms part of thinking. Considered the most complex of all intellectual functions problem solving has been defined as higher-order Cognitive

Typically, the problems to be solved are of the sort that would normally be tackled by a medical or other professional. This article is about people called professionals For the Movie, see The Professional or Leon. Real experts in the problem domain (which will typically be very narrow, for instance "diagnosing skin conditions in human teenagers") are asked to provide "rules of thumb" on how they evaluate the problems, either explicitly with the aid of experienced systems developers, or sometimes implicitly, by getting such experts to evaluate test cases and using computer programs to examine the test data and (in a strictly limited manner) derive rules from that. A rule of thumb is a principle with broad application that is not intended to be strictly accurate or reliable for every situation A software development process is a structure imposed on the development of a software product A test case in Software engineering is a set of conditions or variables under which a tester will determine if a Requirement or Use case upon In Mathematics, an operator is a function which operates on (or modifies another function Generally, expert systems are used for problems for which there is no single "correct" solution which can be encoded in a conventional algorithm — one would not write an expert system to find shortest paths through graphs, or sort data, as there are simply easier ways to do these tasks.

Simple systems use simple true/false logic to evaluate data. Logic is the study of the principles of valid demonstration and Inference. more sophisticated systems are capable of performing at least some evaluation, taking into account real-world uncertainties, using such methods as fuzzy logic. Evaluation is systematic determination of merit worth and significance of something or someone using criteria against a set of standards Fuzzy logic is a form of Multi-valued logic derived from Fuzzy set theory to deal with Reasoning that is approximate rather than precise Such sophistication is difficult to develop and still highly imperfect.

Expert Systems Shells or Inference Engine

Shell is a complete development environment for building and maintaining knowledge-based applications. It provides a step-by-step methodology for knowledge engineer that allows the domain experts themselves to be directly involved in structuring and encoding the knowledge. Use of shells reduces development time up to 50%. Many commercial shells are available.

See also

Bibliography

External links

The Open Directory Project ( ODP) also known as dmoz (from directory

Dictionary

expert system

-noun

  1. (computing) A type of artificially intelligent software system, which stores expertise concerning some subject matter in a knowledge base and attempts to answer questions or solve problems in a manner which simulates the thought processes of a human expert.
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