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Encyclopedia > Expert system

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 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. 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. A software system is a system based on software forming part of a computer system (a combination of hardware and software). ... Look up expert in Wiktionary, the free dictionary. ... A problem domain is a domain where the parameters defining the boundaries of the domain and sufficient mappings into a set of ranges including itself are not well enough understood to provide a systematic description of the domain. ... AI redirects here. ... A knowledge base is a special kind of database for knowledge management. ... Knowledge representation is an issue that arises in both cognitive science and artificial intelligence. ... The process of building knowledge-based systems is called knowledge engineering (KE). ...


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. ... Operations Research or Operational Research (OR) is an interdisciplinary branch of mathematics which uses methods like mathematical modeling, statistics, and algorithms to arrive at optimal or good decisions in complex problems which are concerned with optimizing the maxima (profit, faster assembly line, greater crop yield, higher bandwidth, etc) or minima... Business process reengineering (BPR) is a management approach aiming at improvements by means of elevating efficiency and effectiveness of the processes that exist within and across organizations. ... 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, or MS, is the discipline of using mathematics, and other analytical methods, to help make better business 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. 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 eighties were the time of greatest popularity of expert systems and interest lagged after the onset of the AI Winter. Mycin was an expert system developed over 5 or six years in the early 1970s at the Stanford University, written in Lisp, by Edward Shortliffe under Bruce Buchanan and others; it derived from Dendral, but considerably modified it. ... The 1980s refers to the years from 1980 to 1989. ... To meet Wikipedias quality standards, this article may require cleanup. ...

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. The expert system may also provide mathematical analysis of the problem(s). The expert system utilizes what appears to be reasoning capabilities to reach conclusions. A rule engine is a software system that helps managing business rules. ... In logic, especially in mathematical logic, a rule of inference is a scheme for constructing valid inferences. ... Analysis has its beginnings in the rigorous formulation of calculus. ...


A related term is wizard. 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. 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. A wizard is an interactive computer program acting as an interface to lead a user through a complex task using dialog steps. ... This article is about computing. ...


Prominent expert systems and languages

  • ART - An early general-purpose programming language used in the development of expert systems
  • CADUCEUS (expert system) - Blood-borne infectious bacteria
  • CLIPS - Programming language used in the development of expert systems
  • Drools - An open source offering from JBOSS labs
  • Dendral - Analysis of mass spectra
  • Dipmeter Advisor - Analysis of data gathered during oil exploration
  • Jess - Java Expert System Shell. A CLIPS engine implemented in Java used in the development of expert systems
  • KnowledgeBench – expert system for building new product development applications
  • LogicNets - Web based expert system modeling environment to create expert systems (in collaboration with NASA)
  • MQL 4 - MetaQuotes Language 4, a customized language for financial strategy programming
  • Mycin - Diagnose infectious blood diseases and recommend antibiotics (by Stanford University)
  • NEXPERT Object - An early general-purpose commercial backwards-chaining inference engine used in the development of expert systems
  • Prolog - Programming language used in the development of expert systems
  • Forth - Programming language used in the development of expert systems
  • R1/Xcon - Order processing
  • SHINE Real-time Expert System - Spacecraft Health INference Engine
  • STD Wizard - Expert system for recommending medical screening tests
  • PyKe - Pyke is a knowledge-based inference engine (expert system)
  • Vidwan - Vidwan, an expert system framework, does knowledge inference using in-built backward chaining inference engine.

CADUCEUS was a medical expert system developed in the mid-1980s (but first begun in the 1970s- it took that long to build the knowledge base) by Harry Pople (of the University of Pittsburgh), building on Poples years of itnerviews with Dr. Jack Meyers, one of the top internal... CLIPS is a public-domain software tool for building expert systems. ... JBoss Rules, previously known as Drools is a forward chaining inference based rule engine, more correctly known as a production rule system, using an enhanced implementation of the Rete algorithm. ... Dendral was one of the earliest expert systems. ... The Dipmeter Advisor was an early expert system developed in the 1980s by Schlumberger Doll Research to aid in the analysis of data gathered during oil exploration. ... Jess, a rule engine for the Java platform, is a superset of CLIPS programming language, developed by Ernest Friedman-Hill of Sandia National Labs. ... Java language redirects here. ... For other uses, see NASA (disambiguation). ... Mycin was an expert system developed over 5 or six years in the early 1970s at the Stanford University, written in Lisp, by Edward Shortliffe under Bruce Buchanan and others; it derived from Dendral, but considerably modified it. ... Stanford redirects here. ... Prolog is a logic programming language. ... Forth is a programming language and programming environment, initially developed by Charles H. Moore at the US National Radio Astronomy Observatory in the early 1970s. ... The R1 (later called XCON, for eXpert CONfigurer) program was a production-rule-based system written in OPS5 by John P. McDermott of CMU in 1978 to assist in the ordering of DECs VAX computer systems by automatically selecting the computer system components based on the customers requirements. ... // Knowledge acquisition and implementation from experts is an inefficient and painful process for most automation implementation projects. ... The STD Wizard is an publicly available expert system for determining which screening tests, vaccinations, and evaluations are recommended, related to sexually transmitted diseases. ...

The study of expert systems

Knowledge representation

Knowledge representation is an issue that arises in both cognitive science and artificial intelligence. 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 representation is an issue that arises in both cognitive science and artificial intelligence. ... Cognitive science is usually defined as the scientific study either of mind or of intelligence (e. ... AI redirects here. ...


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. 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: Knowledge Engineers Knowledge engineering is a relatively new profession – the purpose of the job is to work with a client who wants an expert system created for them or their business. ... Knowledge representation is an issue that arises in both cognitive science and artificial intelligence. ... An inference engine tries to derive answers from a knowledge base. ...

  • A good inference technique is independent of the problem domain.
  • In order to realize the benefits of explanation, knowledge transparency, and reusability of the programs in a new problem domain, the inference engine must not contain domain specific expertise.
  • Inference techniques may be specific to a particular task, such as diagnosis of hardware configuration. Other techniques may be committed only to a particular processing technique.
  • Inference techniques are always specific to the knowledge structures.
  • Successful examples of rule processing techniques are forward chaining and backward chaining.

An explanation is a statement which points to causes, context, and consequences of some object, process, state of affairs, etc. ... In computer science and software engineering, reusability is the likelihood a segment of structured code can be used again to add new functionalities with slight or no modification. ... In common language, a task is part of a set of actions which accomplish a job; the sense is that useful work is getting done. Task analysis is the analysis or a breakdown of exactly how a task is accomplished, such as what sub-tasks are required. ... As a subfield in artificial intelligence, Diagnosis is concerned with the development of algorithms and techniques that are able to determine whether the behaviour of a system is correct. ... Forward chaining is one of the two main methods of reasoning when using inference rules (in artificial intelligence). ... Backward chaining is one of the two main methods of reasoning when using inference rules. ...

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. An inference engine using forward chaining searches the inference rules until it finds one in which the if-clause is known to be true. It then concludes the then-clause and adds this information to its data. 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. Forward chaining is one of the two main methods of reasoning when using inference rules (in artificial intelligence). ... A goal is a state of affairs or a state of a concrete activity domain which a person or a system is going/tends to achieve or obtain. ... In logic and mathematics, a logical value, also called a truth value, is a value indicating to what extent a proposition is true. ... For other uses, see Data (disambiguation). ...


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. 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: Backward chaining is one of the two main methods of reasoning when using inference 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. They are meant to imitate the confidences humans use in reasoning rather than to follow the mathematical definitions used in calculating probabilities. This article is about the physical universe. ... Probability is the likelihood that something is the case or will happen. ...


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. 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.

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. Problem solving forms part of thinking. ... Flowcharts are often used to graphically represent algorithms. ...


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.


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. 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. 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. ... This article does not cite any references or sources. ... This article is about the machine. ...


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 virus has been detected. Be alert.


The user interface and the procedure interface are two important functions in the information collection process. The user interface is the part of a system exposed to users. ...


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. 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. 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. A dialog or dialogue is a reciprocal conversation between two or more persons. ... For other uses, see Question (disambiguation). ... In computer science and in computer programming, statements in pseudocode or in a program are normally obeyed one after the other in the order in which they are written (sequential flow of control). ... Look up content in Wiktionary, the free dictionary. ...


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. 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. 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. It is the orderly processing of these rules that dictates the dialog itself. The logical assertion is a statement that asserts that a certain premise is true, and is useful for statements in proof. ... A consequent is the second half of a hypothetical proposition. ... A constraint is a limitation of possibilities. ... This article is about the concept of an entity. ... In computer science, a value is a sequence of bits that is interpreted according to some data type. ... Look up attribute in Wiktionary, the free dictionary. ...


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. In traditional applications, problem expertise is encoded in both program and data structures. Problem solving forms part of thinking. ...


In the expert system approach all of the problem related expertise is encoded in data structures only; none is in programs. This organization has several benefits. A binary tree, a simple type of branching linked data structure. ...


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. 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. A problem domain is a domain where the parameters defining the boundaries of the domain and sufficient mappings into a set of ranges including itself are not well enough understood to provide a systematic description of the domain. ... The current version of this article or section is written in an informal style and with a personally invested tone. ... In computer science, a declaration specifies a variables dimensions, identifier, type, and other aspects. ... Flowcharts are often used to graphically represent algorithms. ...


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. This article is about building architecture. ... This article does not cite any references or sources. ... An inference engine tries to derive answers from a knowledge base. ...


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. 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. 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. When these rules are created from the domain expertise, the knowledge base stores the rules of the expert system. Economics and commerce define an end-user as the person who uses a product. ... A problem domain expert is a person who has become extremely able to solve problems in a particular domain, due to his or her training and experience gathered through the years. ... Knowledge Engineers Knowledge engineering is a relatively new profession – the purpose of the job is to work with a client who wants an expert system created for them or their business. ... Knowledge representation is an issue that arises in both cognitive science and artificial intelligence. ... Explanation modules, used in expert systems, is a function that enables the knowledge worker to understand why the information explained and concluded by the domain expert is viable. ... Inference is the act or process of deriving a conclusion based solely on what one already knows. ... It has been suggested that Knowledge engineers be merged into this article or section. ... Logic programming is a declarative programming paradigm in which a set of attributes that a solution should have are specified rather than set of steps to obtain such a solution. ...


Inference rule

An understanding of the "inference rule" concept is important to understand expert systems. 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. An example of an inference rule is: In logic, especially in mathematical logic, a rule of inference is a scheme for constructing valid inferences. ... The term statement can have several meanings: In programming, a statement is an instruction to execute something that will not return a value. ... In linguistics, prescription is the laying down or prescribing of normative rules of the language. ...

If the restaurant choice includes French, and the occasion is romantic,
Then the restaurant choice is definitely Paul Bocuse.

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. 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. Le Nord, one of Bocuses chain of brasseries in central Lyon. ... A conclusion is a final proposition, which is arrived at after the consideration of evidence, arguments or premises. ... Reasoning is the mental (cognitive) process of looking for reasons to support 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. Look up expert in Wiktionary, the free dictionary. ...


Procedure node interface

The function of the procedure node interface is to receive information from the procedures coordinator and create the appropriate procedure call. 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. 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 computer science, a subroutine (function, procedure, or subprogram) is a sequence of code which performs a specific task, as part of a larger program, and is grouped as one, or more, statement blocks; such code is sometimes collected into software libraries. ... In computer science, a subroutine (function, method, procedure, or subprogram) is a portion of code within a larger program, which performs a specific task and can be relatively independent of the remaining code. ... For the term in the context of mathematical logic, see Generalization (logic). ... Input3 is the term denoting either an entrance or changes which are inserted into a system and which activate/modify a process. ...


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. In mathematics, a finitary boolean function is a function of the form f : Bk → B, where B = {0, 1} is a boolean domain and where k is a nonnegative integer. ...


The inference logic has two functions. It selects a tree to trace and then it traces that tree. Once a tree has been selected, that tree is traced, depth-first, left to right. The coniferous Coast Redwood, the tallest tree species on earth. ...


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. It could ask the operator, for instance, if the display is a monochrome or color display. 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. 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. Various components An electronic component is a basic electronic element usually packaged in a discrete form with two or more connecting leads or metallic pads. ... It has been suggested that this article or section be merged into output device. ... The process of building knowledge-based systems is called knowledge engineering (KE). ... In the fields of science, engineering, industry and statistics, accuracy is the degree of conformity of a measured or calculated quantity to its actual (true) value. ...


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. 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. In the common law, an answer is the first pleading by a defendant, usually filed and served upon the plaintiff within a certain strict time limit after a civil complaint or criminal information or indictment has been served upon the defendant. ...


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. 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. It has been suggested that Accounting scholarship be merged into this article or section. ... Process control is a statistics and engineering discipline that deals with architectures, mechanisms, and algorithms for controlling the output of a specific process. ... Financial services is the largest industry (or category of industries) in the world in terms of earnings (20% of market cap in the S&P 500 in 2004). ... This article is about human resources as it applies to business, labor, and economies. ...


A good example of application of expert systems in banking area is expert systems for mortgages. 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. 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. Introduction A mortgage is a device used to create a lien on real estate by contract. ... This article or section does not cite any references or sources. ... Introduction A mortgage is a device used to create a lien on real estate by contract. ...


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. 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. Therefore, some of the techniques of expert systems can now be found in most complex programs without any fuss about them. AI redirects here. ... For other uses, see Knowledge (disambiguation). ... For other uses, see Error (disambiguation). ... This article is about modern humans. ... A programmer or software developer is someone who programs computers, that is, one who writes computer software. ...


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. 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. Windows redirects here. ... An operating system (OS) is a software that manages computer resources and provides programmers with an interface used to access those resources. ... Troubleshooting is a form of problem solving. ... In computing, the taskbar is a term for the application desktop bar which is used to launch and monitor applications in Microsoft Windows 95 and later operating systems. ...


Another 1970s and 1980s application of expert systems — which we today would simply call AI — was in computer games. 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. 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. 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." // This disambiguation page covers alternative uses of the terms Ai, AI, and A.I. Ai (as a word, proper noun and set of initials) can refer to many things. ... This article needs a complete rewrite for the reasons listed on the talk page. ... This article is about the sport. ... Earl Weaver Baseball is a baseball computer simulation game (1987), designed by Don Daglow and Eddie Dombrower and published by Electronic Arts. ... Tony La Russa Baseball is a baseball computer and video game console simulation game (1991-1997), designed by Don Daglow, Mark Buchignani, David Bunnett, Clay Dreslough and Hudson Piehl and developed by Stormfront Studios. ... Earl Sidney Weaver (born August 14, 1930 in St. ... Tony La Russa after the 2006 World Series Anthony La Russa, Jr. ...


Advantages and disadvantages

Advantages:

  • Provides consistent answers for repetitive decisions, processes and tasks
  • Holds and maintains significant levels of information
  • Encourages organizations to clarify the logic of their decision-making
  • Never "forgets" to ask a question, as a human might

Disadvantages:

  • Lacks common sense needed in some decision making
  • Cannot make creative responses as human expert would in unusual circumstances
  • Domain experts not always able to explain their logic and reasoning
  • Errors may occur in the knowledge base, and lead to wrong decisions
  • Cannot adapt to changing environments, unless knowledge base is changed

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. 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. Procedural knowledge or know-how is the knowledge of how to perform some task. ... Problem solving forms part of thinking. ...


Typically, the problems to be solved are of the sort that would normally be tackled by a medical or other professional. 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. 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. This article is about people called professionals. ... A rule of thumb is an easily learned and easily applied procedure for approximately calculating or recalling some value, or for making some determination. ... A systems analyst is responsible for systems design. ... This article is about the term in software engineering. ... In mathematics, an operator is a function that performs some sort of operation on a number, variable, or function. ...


Simple systems use simple true/false logic to evaluate data. more sophisticated systems are capable of performing at least some evaluation, taking into account real-world uncertainties, using such methods as fuzzy logic. Such sophistication is difficult to develop and still highly imperfect. Logic (from Classical Greek λόγος logos; meaning word, thought, idea, argument, account, reason, or principle) is the study of the principles and criteria of valid inference and demonstration. ... This article is about characterizing and appraising something of interest. ... For the Super Furry Animals album, see Fuzzy Logic (album). ...


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

A production system (or production rule system) is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior. ... AI redirects here. ... An artificial neural network (ANN), often just called a neural network (NN), is a mathematical model or computational model based on biological neural networks. ... Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. ... The term business intelligence (BI) dates to 1958. ... Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. ... Clinical (or Diagnostic) Decision Support Systems (CDSS) are interactive computer programs, which directly assist physicians and other health professionals with decision making tasks. ... Connectionist Expert Systems are artificial neural network (ANN) based expert systems where the ANN generates inferencing rules e. ... Data mining is the principle of sorting through large amounts of data and picking out relevant information. ... For the Super Furry Animals album, see Fuzzy Logic (album). ... In computer science, besides the common use as rule of thumb (see heuristic), the term heuristic has two well-defined technical meanings. ... An inference engine tries to derive answers from a knowledge base. ... KADS ia a structured way of developing knowledge-based systems (expert systems). ... To meet Wikipedias quality standards, this article or section may require cleanup. ... As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to learn. At a general level, there are two types of learning: inductive, and deductive. ... OPS5 is a rule-based or production system computer language, notable as the first such language to be used in a successful expert system, the R1/XCON system used to configure VAX computers. ... A production system consists of a collection of productions (rules), a working memory of facts and an algorithm, known as forward chaining, for producing new facts from old. ... The Rete algorithm is an efficient pattern matching algorithm for implementing rule-based (expert) systems. ... Self service software allows authors (typically subject matter experts) to readily automate the deployment of, the timeliness of and compliance around a variety of processes of which they are involved in communicating without having to physically address the questions, needs and solicitations of end users who are inquiring about the...

Bibliography

  • James Ignizio, Introduction to Expert Systems (1991), ISBN 0-07-909785-5
  • Joseph C. Giarratano, Gary Riley Expert Systems, Principles and Programming (2005), ISBN 0-534-38447-1
  • Peter Jackson Introduction to Expert Systems (1998), ISBN 0-201-87686-8
  • WALKER, Adrian et al (1990). Knowledge Systems and Prolog. Addison-Wesley. ISBN 0-201-52424-4. 

External links

The Open Directory Project (ODP), also known as dmoz (from , its original domain name), is a multilingual open content directory of World Wide Web links owned by Netscape that is constructed and maintained by a community of volunteer editors. ...

  Results from FactBites:
 
What is expert system? - A Word Definition From the Webopedia Computer Dictionary (348 words)
For example, there are expert systems that can diagnose human illnesses, make financial forecasts, and schedule routes for delivery vehicles.
Some expert systems are designed to take the place of human experts, while others are designed to aid them.
Expert systems are part of a general category of computer applications known as artificial intelligence.
  More results at FactBites »

 
 

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