Expert system 1

Expert systemIn artificial intelligence, an expert system is a computer system that emulates the decision-making ability of ahuman expert.[1] Expert systems are designed to solve complex problems by reasoning about knowledge, like anexpert, and not by following the procedure of a developer as is the case in conventional programming.[2][3][4] Thefirst expert systems were created in the 1970s and then proliferated in the 1980s.[5] Expert systems were among thefirst truly successful forms of AI software.[6][7][8][9][10][11]

An expert system has a unique structure, different from traditional programs. It is divided into two parts, one fixed,independent of the expert system: the inference engine, and one variable: the knowledge base. To run an expertsystem, the engine reasons about the knowledge base like a human.[12] In the 80's a third part appeared: a dialoginterface to communicate with users.[13] This ability to conduct a conversation with users was later called"conversational".[14][15]

HistoryExpert systems were introduced by researchers in the Stanford Heuristic Programming Project, including the "fatherof expert systems" Edward Feigenbaum, with the Dendral and Mycin systems. Principal contributors to thetechnology were Bruce Buchanan, Edward Shortliffe, Randall Davis, William vanMelle, Carli Scott and others atStanford. Expert systems were among the first truly successful forms of AI software.[6][7][8][9][10][11]

Research is also very active in France, where researchers focus on the automation of reasoning and logic engines.The French Prolog computer language, designed in 1972, marks a real advance over expert systems like Dendral orMycin: it is a shell,[16] that is to say a software structure ready to receive any expert system and to run it. It integratesan engine using First-Order logic, with rules and facts. It's a tool for mass production of expert systems and was thefirst operational declarative language,[17] later becoming the best selling AI language in the world.[18] HoweverProlog is not particularly user friendly and is an order of logic away from human logic.[19][20][21]

In the 1980s, expert systems proliferated as they were recognized as a practical tool for solving real-world problems.Universities offered expert system courses and two thirds of the Fortune 1000 companies applied the technology indaily business activities.[5][22] Interest was international with the Fifth Generation Computer Systems project inJapan and increased research funding in Europe. Growth in the field continued into the 1990s.The development of expert systems was aided by the development of the symbolic processing languages Lisp andProlog. To avoid re-inventing the wheel, expert system shells were created that had more specialized features forbuilding large expert systems.[23]

In 1981 the first IBM PC was introduced, with MS-DOS operating system. Its low price started to multiply users andopened a new market for computing and expert systems. In the 80's the image of AI was very good and peoplebelieved it would succeed within a short time[15]. Many companies began to market expert systems shells fromuniversities, renamed "generators" because they added to the shell a tool for writing rules in plain language and thus,theoretically, allowed to write expert systems without a programming language nor any other software[16]. The bestknown: Guru (USA) inspired by Mycin[17][18], Personal Consultant Plus (USA)[19][20], Nexpert Object(developed by Neuron Data, company founded in California by three French)[21][22], Genesia (developed by Frenchpublic company Electricit de France and marketed by Steria)[23], VP Expert (USA)[24]. But eventually the toolswere only used in research projects. They did not penetrate the business market, showing that AI technology was notmature.In 1986, a new expert system generator for PCs appeared on the market, derived from the French academic research: Intelligence Service,[24][25] sold by GSI-TECSI software company. This software showed a radical innovation: it used propositional logic ("Zeroth order logic") to execute expert systems, reasoning on a knowledge base written with everyday language rules, producing explanations and detecting logic contradictions between the facts. It was the

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first tool showing the AI defined by Edward Feigenbaum in his book about the Japanese Fifth Generation, ArtificialIntelligence and Japan's Computer Challenge to the World [26] (1983): "The machines will have reasoning power:they will automatically engineer vast amounts of knowledge to serve whatever purpose humans propose, frommedical diagnosis to product design, from management decisions to education", "The reasoning animal has, perhapsinevitably, fashioned the reasoning machine", "the reasoning power of these machines matches or exceeds thereasoning power of the humans who instructed them and, in some cases, the reasoning power of any humanperforming such tasks". Intelligence Service was in fact "Pandora" (1985),[27] a software developed for their thesisby two academic students of Jean-Louis Laurire,[28] one of the most famous and prolific French AI researcher.[29]

Unfortunately, as this software was not developed by his own IT developers, GSI-TECSI was unable to make itevolve. Sales became scarce and marketing stopped after a few years.

Software architecture

The rule base or knowledge baseIn expert system technology, the knowledge base is expressed with natural language rules IF ... THEN ... Forexamples : "IF it is living THEN it is mortal" "IF his age = known THEN his year of birth = date of today - his age in years" "IF the identity of the germ is not known with certainty AND the germ is gram-positive AND the morphology of

the organism is "rod" AND the germ is aerobic THEN there is a strong probability (0.8) that the germ is of typeenterobacteriacae"[30]

This formulation has the advantage of speaking in everyday language which is very rare in computer science (aclassic program is coded). Rules express the knowledge to be exploited by the expert system. There exist otherformulations of rules, which are not in everyday language, understandable only to computer scientists. Each rulestyle is adapted to an engine style. The whole problem of expert systems is to collect this knowledge, usuallyunconscious, from the experts. There are methods but almost all are usable only by computer scientists.

The inference engineThe inference engine is a computer program designed to produce a reasoning on rules. In order to produce areasoning, it is based on logic. There are several kinds of logic: propositional logic, predicates of order 1 or more,epistemic logic, modal logic, temporal logic, fuzzy logic, etc. Except for propositional logic, all are complex and canonly be understood by mathematicians, logicians or computer scientists. Propositional logic is the basic human logic,that is expressed in syllogisms. The expert system that uses that logic is also called a zeroth-order expert system.With logic, the engine is able to generate new information from the knowledge contained in the rule base and data tobe processed.The engine has two ways to run: batch or conversational. In batch, the expert system has all the necessary data to process from the beginning. For the user, the program works as a classical program: he provides data and receives results immediately. Reasoning is invisible. The conversational method becomes necessary when the developer knows he cannot ask the user for all the necessary data at the start, the problem being too complex. The software must "invent" the way to solve the problem, request the missing data from the user, gradually approaching the goal as quickly as possible. The result gives the impression of a dialogue led by an expert. To guide a dialogue, the engine may have several levels of sophistication: "forward chaining", "backward chaining" and "mixed chaining". Forward chaining is the questioning of an expert who has no idea of the solution and investigates progressively (e.g. fault diagnosis). In backward chaining, the engine has an idea of the target (e.g. is it okay or not? Or: there is danger but what is the level?). It starts from the goal in hopes of finding the solution as soon as possible. In mixed chaining the engine has an idea of the goal but it is not enough: it deduces in forward chaining from previous user responses all

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that is possible before asking the next question. So quite often he deduces the answer to the next question beforeasking it.A strong interest in using logic is that this kind of software is able to give to user clear explanation of what it is doing(the "Why?") and what it has deduced (the "How?" ). Better yet, thanks to logic the most sophisticated expert systemare able to detect contradictions[31] into user information or in the knowledge and can explain them clearly, revealingat the same time the expert knowledge and his way of thinking.

Advantages

ConversationalExpert systems offer many advantages for users when compared to traditional programs because they operate like ahuman brain.

Quick availability and opportunity to program itselfAs the rule base is in everyday language (the engine is untouchable), expert system can be written much faster than aconventional program, by users or experts, bypassing professional developers and avoiding the need to explain thesubject.

Ability to exploit a considerable amount of knowledgeThe expert system uses a rule base, unlike conventional programs, which means that the volume of knowledge toprogram is not a major concern. Whether the rule base has 10 rules or 10 000, the engine operation is the same.

ReliabilityThe reliability of an expert system is the same as the reliability of a database, i.e. good, higher than that of a classicalprogram.

ScalabilityEvolving an expert system is to add, modify or delete rules. Since the rules are written in plain language, it is easy toidentify those to be removed or modified.

PedagogyThe engines that are run by a true logic are able to explain to the user in plain language why they ask a question andhow they arrived at each deduction. In doing so, they show knowledge of the expert contained in the expert system.So, user can learn this knowledge in its context. Moreover, they can communicate their deductions step by step. So,the user has information about their problem even before the final answer of the expert system.

Preservation and improvement of knowledgeValuable knowledge can disappear with the death, resignation or retirement of an expert. Recorded in an expertsystem, it becomes eternal. To develop an expert system is to interview an expert and make the system aware of theirknowledge. In doing so, it reflects and enhances it.

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New areas neglected by conventional computingAutomating a vast knowledge, the developer may meet a classic problem: "combinatorial explosion" commonlyknown as "information overload" that greatly complicates his work and results in a complex and time consumingprogram. The reasoning expert system does not encounter that problem since the engine automatically loadscombinatorics between rules. This ability can address areas where combinatorics are enormous: highly interactive orconversational applications, fault diagnosis, decision support in complex systems, educational software, logicsimulation of machines or systems, constantly changing software.

DisadvantagesThe expert system has a major flaw which explains its low success although the principle has existed for 70 years:knowledge collection and interpretation into rules, the knowledge engineering. Most developers have no method toperform this task. Instead, they work manually, which creates a large potential for errors. Expert knowledge is notwell understood: there is a lack of rules, rules are contradictory, and some are poorly written and unusable. Worse,expert systems most often use an engine incapable of reasoning. Therefore, oftentimes the expert system workspoorly and the project is abandoned.[32] Correct development methodology can mitigate these problems. There existssoftware to interview the expert step by step which automatically write the rules and simultaneously run the expertsystem before his eyes, performing a consistency of the rules control.[33][34][35] So expert and users can check thequality of the software before it is finished.Many expert systems are also penalized by the logic used. Most of the logics operate on "variables" facts, i.e. whosevalue changes several times during one reasoning, considered as a property belonging to a more powerful logic.Exactly like in classical computing, a way of programming where developers are in fact comfortable. This is the caseof Mycin, Dendral, fuzzy logic, predicate logic (Prolog), symbolic logic, mathematical logic, etc.. Propositional logicuses only invariant facts.[36] It turns out that in human mind, the facts used must remain invariable as long as thebrain reasons on them. This makes possible detection of contradictions and production of explanations, two ways ofcontrolling consistency of the knowledge.[37][38] That is why expert systems using variable facts, moreunderstandable for IT developers so the most numerous, are less easy to develop, less clear to users, less reliable andwhy they don't produce explanation or contradiction detection.

Application fieldExpert systems address areas where combinatorics is enormous: highly interactive or conversational applications, IVR, voice server, chatterbot fault diagnosis, medical diagnosis decision support in complex systems, process control, interactive user guide educational and tutorial software logic simulation of machines or systems knowledge management constantly changing software.They can also be used in software engineering for rapid prototyping applications (RAD). Indeed, the expert systemquickly developed in front of the expert shows him if the future application should be programmed.Indeed, any program contains expert knowledge and classic programming always begins with an expert interview. Aprogram written in the form of expert system receives all the specific benefits of expert system, among others thingsit can be developed by anyone without computer training and without programming languages. But this solution hasa defect: expert system runs slower than a traditional program because he consistently "thinks" when in fact a classicsoftware just follows paths traced by the programmer.

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Examples of applicationsExpert systems are designed to facilitate tasks in the fields of accounting, medicine, process control, financialservice, production, human resources, among others. Typically, the problem area is complex enough that a moresimple traditional algorithm cannot provide a proper solution. The foundation of a successful expert system dependson a series of technical procedures and development that may be designed by technicians and related experts. Assuch, expert systems do not typically provide a definitive answer, but provide probabilistic recommendations.An example of the application of expert systems in the financial field is expert systems for mortgages. Loandepartments are interested in expert systems for mortgages because of the growing cost of labour, which makes thehandling and acceptance of relatively small loans less profitable. They also see a possibility for standardized,efficient handling of mortgage loan by applying expert systems, appreciating that for the acceptance of mortgagesthere are hard and fast rules which do not always exist with other types of loans. Another common application in thefinancial area for expert systems are in trading recommendations in various marketplaces. These markets involvenumerous variables and human emotions which may be impossible to deterministically characterize, thus expertsystems based on the rules of thumb from experts and simulation data are used. Expert system of this type can rangefrom ones providing regional retail recommendations, like Wishabi, to ones used to assist monetary decisions byfinancial institutions and governments.Another 1970s and 1980s application of expert systems, which we today would simply call AI, was in computergames. For example, the computer baseball games Earl Weaver Baseball and Tony La Russa Baseball each hadhighly detailed simulations of the game strategies of those two baseball managers. When a human played the gameagainst the computer, the computer queried the Earl Weaver or Tony La Russa Expert System for a decision on whatstrategy to follow. Even those choices where some randomness was part of the natural system (such as when tothrow a surprise pitch-out to try to trick a runner trying to steal a base) were decided based on probabilities suppliedby Weaver or La Russa. Today we would simply say that "the game's AI provided the opposing manager's strategy".A new application for expert systems is automated computer program generation. Funded by a US Air Force grant,an expert system-based application (hprcARCHITECT) that generates computer programs for mixed processortechnology (FPGA/GPU/Multicore) systems without a need for technical specialists has recently been commerciallyintroducted.There is also a large body of contemporary research and development directed toward using expert systems forhuman behavior modeling and decision support systems. The former is especially important in the area ofintercultural relations and the latter in improving management operations in small businesses.

Knowledge engineeringThe building, maintaining and development of expert systems is known as knowledge engineering.[39] Knowledgeengineering is a "discipline that involves integrating knowledge into computer systems in order to solve complexproblems normally requiring a high level of human expertise".[40]

There are generally three individuals having an interaction in an expert system. Primary among these is the end-user,the individual who uses the system for its problem solving assistance. In the construction and maintenance of thesystem there are two other roles: the problem domain expert who builds the system and supplies the knowledge base,and a knowledge engineer who assists the experts in determining the representation of their knowledge, enters thisknowledge into an explanation module and who defines the inference technique required to solve the problem.Usually the knowledge engineer will represent the problem solving activity in the form of rules. When these rules arecreated from domain expertise, the knowledge base stores the rules of the expert system.

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References[1] Jackson, Peter (1998), Introduction To Expert Systems (3 ed.), Addison Wesley, p.2, ISBN978-0-201-87686-4[2] Nwigbo Stella and Agbo Okechuku Chuks (http:/ / www. hrmars. com/ admin/ pics/ 261. pdf), School of Science Education, Expert system: a

catalyst in educational development in Nigeria: "The ability of this system to explain the reasoning process through back-traces (...) providesan additional feature that conventional programming does not handle"

[3] Regina Barzilay, Daryl McCullough, Owen Rambow, Jonathan DeCristofaro, Tanya Korelsky, Benoit Lavoie (http:/ / www. cogentex. com/papers/ explanation-iwnlg98. pdf): "A new approach to expert system explanations"

[4] Conventional programming (http:/ / www. pcmag. com/ encyclopedia_term/ 0,2542,t=conventional+ programming& i=40325,00. asp)[5] Cornelius T. Leondes (2002). Expert systems: the technology of knowledge management and decision making for the 21st century. pp.122.

ISBN978-0-12-443880-4.[6][6] ACM 1998, I.2.1[7] Russell & Norvig 2003, pp.2224[8] Luger & Stubblefield 2004, pp.227331[9][9] Nilsson 1998, chpt. 17.4[10] McCorduck 2004, pp.327335, 434435[11] Crevier 1993, pp.14562, 197203[12] Nwigbo Stella and Agbo Okechuku Chuks (http:/ / www. hrmars. com/ admin/ pics/ 261. pdf), School of Science Education, Expert system:

a catalyst in educational development in Nigeria: "Knowledge-based systems collect the small fragments of human know-how into aknowledge-base which is used to reason through a problem, using the knowledge that is appropriated"

[13] Koch, C. G.; Isle, B. A.; Butler, A. W. (1988). "Intelligent user interface for expert systems applied to power plant maintenance andtroubleshooting". IEEE Transactions on Energy Conversion 3: 71. doi:10.1109/60.4202.

[14] McTear, M. F. (2002). "Spoken dialogue technology: Enabling the conversational user interface". ACM Computing Surveys 34: 90.doi:10.1145/505282.505285.

[15] Lowgren, J. (1992). "The Ignatius environment: Supporting the design and development of expert-system user interfaces". IEEE Expert 7(4): 4957. doi:10.1109/64.153464.

[16][16] George F. Luger and William A. Stubblefield, Benjamin/Cummings Publishers, Rule Based Expert System Shell: example of code using theProlog rule based expert system shell

[17] A. MICHIELS (http:/ / promethee. philo. ulg. ac. be/ engdep1/ download/ prolog/ htm_docs/ prolog. htm), Universit de Lige, Belgique:"PROLOG, the first declarative language

[18] Carnegie Mellon University's AI Web Site (http:/ / www. prenhall. com/ divisions/ bp/ app/ turban/ dss/ html/ chap16. html): "Prolog wasthe most popular AI language in Japan and probably in Europe"

[19] Ivana Berkovi, Biljana Radulovi and Petar Hotomski (http:/ / www. proceedings2007. imcsit. org/ pliks/ 33. pdf), University of Novi Sad,2007, Extensions of Deductive Concept in Logic Programing and Some Applications: "the defects of PROLOG-system: the expansionconcerning Horn clauses, escaping negation treatment as definite failure"

[20][20] "Software developed in Prolog has been criticized for having a high performance penalty compared to conventional programminglanguages"

[21] Dr. Nikolai Bezroukov (http:/ / www. softpanorama. org/ Lang/ prolog. shtml), Softpanorama: "I think that most people exposed to Prologremember strongly the initial disappointment. Language was/is so hyped but all you can see initially are pretty trivial examples that are solvedby complex, obscure notation that lacks real expressive power: some of simple examples can be expressed no less concisely is many otherlanguages"

[22][22] Durkin, J. Expert Systems: Catalog of Applications. Intelligent Computer Systems, Inc., Akron, OH, 1993.[23] page 21. Giarratano & Riley, 3rd ed.[24] Flamant B. and Girard G (http:/ / cat. inist. fr/ ?aModele=afficheN& cpsidt=7001328)., GSI-TECSI, Intelligence Service: build your own

expert system : "Intelligence Service is a development environment for expert systems that requires no experience of classic programming thatoffers to everyone the opportunity to develop its own expert system"

[25] Bertrand Savatier (http:/ / www. tree-logic. com/ Articles/ 01, seul IS fait de l'IA, par un universitaire. jpg), Le Monde Informatique,November 23, 1987: "Expert systems accessible to all"

[26] http:/ / www. atarimagazines. com/ creative/ v10n8/ 103_The_fifth_generation_Jap. php[27] Jean-Philippe de Lespinay (http:/ / www. tree-logic. com/ scienceetvie. htm), Science et Vie, "From total zero to Zero Plus [logic]", May

1991[28] Death of Jean-Louis Laurire (http:/ / www. lip6. fr/ actualite/ information-fiche. php?RECORD_KEY(informations)=id&

id(informations)=18)[29] Journey " In honor of Jean-Louis Laurire (http:/ / www. lip6. fr/ Laboratoire/ 2006-03-22/ 2006-03-22-Affiche. pdf)", Universit Pierre et

Marie Curie in Paris (March 22, 2006)[30] Mycin rule[31] Nabil Arman (http:/ / www. ccis2k. org/ iajit/ PDF/ vol. 4,no. 1/ 9-Nabil. pdf), Polytechnic University of Palestine, January 2007, Fault

Detection in Dynamic Rule Bases Using Spanning Trees and Disjoin Sets: ""

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[32] Kenneth Laudon, Jane Laudon, Eric Fimbel, "Management Information Systems: Managing the Digital Firm", Business & Economics, 2010edition, chapter 11-3.5: The implementation of a large number of expert systems requires the deployment of considerable development efforts,lengthy and expensive. Hiring and training a larger number of experts may be less expensive than building an expert system .(...) Some expertsystems, particularly the largest, are so complex that over years, the costs of curative and adaptive maintenance become as high as the cost ofdevelopment.

[33] Systmes Experts, April 15, 1990, Miao, authentic expert system generator of fault diagnosis: "MIAO can explain, again in [plain]language, all of his logical approach: why he is asking such a question and how it came to such a conclusion. And that because he isconstantly reasoning and not because an IT developer programmed in advance all the possible explanations."

[34] Olivier Rafal (http:/ / www. tree-logic. com/ Articles/ LMI 2001 cf Maieutique. jpg), Le Monde Informatique, Programming for all (T.Rexgenerator): "This software allows to develop a conversational application (...) leading to a self-learning" (i.e. thanks to the automaticexplanations)

[35] French Technology Survey (http:/ / www. tree-logic. com/ Articles/ Maeutica par FTS (91). jpg), MAIEUTICA, An Expert SystemGenerator which writes its own rules, July 1991: "checking the coherence of the knowledge", "it can detect contradictions", "it reactappropriately to changes of minds"

[36] RGU: School of Computing (http:/ / www. comp. rgu. ac. uk/ docs/ info/ index. php), More Complex Inference: "propositional logic, wherevariables are not allowed".

[37] Ong K. and Lee R.M, Texas University-Austin, A logic model for maintaining consistency of bureaucratic policies, 1993: "Inconsistenciescan be detected if any of the integrity constraints is proven false, and an explanation can be provided based on the proof tree. A more generalinference mechanism is presented based on the theory of abduction for checking potential inconsistency of policies"

[38] Carl G. Hempel and Paul Oppenheim (http:/ / people. cohums. ohio-state. edu/ tennant9/ hempel_oppenheim_PS1948. pdf), Philosophy ofScience, Studies in the Logic of Explanation, 1948: "The sentences constituting the explanans must be true"

[39] Kendal, S.L.; Creen, M. (2007), An introduction to knowledge engineering, London: Springer, ISBN978-1-84628-475-5, OCLC70987401[40] Feigenbaum, Edward A.; McCorduck, Pamela (1983), The fifth generation (1st ed.), Reading, MA: Addison-Wesley,

ISBN978-0-201-11519-2, OCLC9324691

Bibliography

Textbooks Darlington, Keith (2000). The Essence of Expert Systems. Pearson Education. ISBN978-0-13-022774-4. Ignizio, James (1991). Introduction to Expert Systems. McGraw-Hill Companies. ISBN978-0-07-909785-9. Giarratano, Joseph C. and Riley, Gary; Gary Riley (2005). Expert Systems, Principles and Programming. Course

Technology Ptr. ISBN978-0-534-38447-0. Jackson, Peter (1998). Introduction to Expert Systems. Addison Wesley. ISBN978-0-201-87686-4. Walker, Adrian et al. (1990). Knowledge Systems and Prolog. Addison-Wesley. ISBN978-0-201-52424-6. Naylor, Chris. (1983). Build your own Expert System. Sigma Technical Press. ISBN978-0-905-10441-6.

History of AI Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN

0-465-02997-3 McCorduck, Pamela (2004), Machines Who Think (http:/ / www. pamelamc. com/ html/ machines_who_think.

html) (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN1-56881-205-1 Luger, George; William A. Stubblefield (2004). Artificial Intelligence: Structures and Strategies for Complex

Problem Solving (http:/ / www. cs. unm. edu/ ~luger/ ai-final/ tocfull. html) (5th ed.). The Benjamin/CummingsPublishing Company, Inc.. ISBN978-0-8053-4780-7.

Nilsson, Nils (1998-04-01). Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers.ISBN978-1-55860-467-4.

Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (http:/ / aima. cs. berkeley.edu/ ) (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN0-13-790395-2

Winston, Patrick Henry (1984-04). Artificial Intelligence. Addison-Wesley. ISBN978-0-201-08259-3.

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Other "ACM Computing Classification System: Artificial intelligence" (http:/ / www. acm. org/ class/ 1998/ I. 2. html).

ACM. 1998. Retrieved 2007-08-30. Jean-Philippe de Lespinay, Admiroutes, December 2008: Reasoning AI (http:/ / www. admiroutes. asso. fr/

larevue/ 2008/ 93/ lespinay. htm) Automates Intelligents, 2009: Conversational and Call centers (http:/ / www. automatesintelligents. com/

interviews/ 2009/ lespinay. html) US patent 4763277 (http:/ / worldwide. espacenet. com/ textdoc?DB=EPODOC& IDX=US4763277), Ashford,

Thomas J. et al., "Method for obtaining information in an expert system", published 1988-08-09, issued1988-08-09

External links Artificial Intelligence (http:/ / www. dmoz. org/ Computers/ Artificial_Intelligence/ / ) at the Open Directory

Project Expert System tutorial on Code Project (http:/ / www. codeproject. com/ KB/ recipes/ ArtificialAdvice-1. aspx)

Article Sources and Contributors 9

Article Sources and ContributorsExpert system Source: http://en.wikipedia.org/w/index.php?oldid=477594576 Contributors: .mdk., 2help, 4twenty42o, 767Mobile, A5b, Abaddon314159, Abdull, AbsoluteFlatness, Acroterion,Adrian.walker, Agutie, Ahoerstemeier, Alansohn, Aldarsior, Alksub, Alphachimp, Aluion, Amypro, Andrew Parke Yang Yu, AndyTheGrump, Angela, Anonymous Dissident, Anselmobattisti,Arnehans, Arpabr, Arthur Rubin, Arved, Asheeshgoja, Barneca, Bdonlan, Beetstra, Beland, Benjaminb, Bentogoa, BertSeghers, Bevan.coleman, BlackHatMentor, BlueNovember, Boing! saidZebedee, Bookandcoffee, Boothy443, Brandonm0720, Brat32, CaliforniaDreaming, Calltech, Canley, Carlosliu, CatherineMunro, Caulde, CharlesGillingham, Chocolateboy, Chris 73, Chris thespeller, Christian75, Clemwang, Coll7, Control.optimization, Conversion script, Courcelles, Criffin, Crysb, Cse.buddhika, DARTH SIDIOUS 2, DXBari, Dan Gan, Davehi1, Dbfirs, DieSkurk,Discospinster, Doc Tropics, Dottydotdot, Download, Dreadstar, Drwu82, Dspradau, Dullfig, Dupz, EAi, EagleFan, Econrad, Ee79, Eh7, Ehheh, Elphington, Epbr123, Erkan Yilmaz, Eternaldragon, Excirial, Favonian, Floris V, Frecklefoot, Freek Verkerk, Furrykef, Fvasconcellos, Fyyer, F, Gadfium, Gh23, Gilliam, Gmstanley, GoingBatty, Graham king 3, Graymornings,Greenchilefrog, Greenrd, Gtg204y, Gurch, Gwernol, Gkhan, H-ko, Harvey the rabbit, Hetar, Hingram, Hirzel, History2007, Hobartimus, Hoziron, Hubyrod, Hut 8.5, Ike9898, Iluvcapra, Imran,Informavores, Ingolfson, IvanLanin, Ixfd64, JMSwtlk, JTN, Jahiegel, JamesBWatson, Jeff G., Jerome Charles Potts, Jez9999, Jim McKeeth, Jim15936, Jmaio2, Jmchauvet, Joachim.baumeister,Joanna Bryson, JonHarder, Joydurgin, Joyous!, Jpbowen, Jtp15, Kaimiddleton, Karthikanandraj, Kelovy, Kenyon, Khalid hassani, Kingpin13, Komandobb, Kpjas, Kristensson, Kuru, Kvng,Kyle1278, Lastorset, Lbhales, Lear's Fool, Lights, LittleOldMe old, Looxix, Louharris, Lradrama, Luciandrei, Luk, Luna Santin, Lupo, Lycurgus, MC MasterChef, MCrawford, MER-C,Madelinefelkins, Manop, Mariannehgv, MarkJFAllen, Marudubshinki, Massic80, Materialscientist, MattBan, Maurreen, Mav, Mdd, Merbabu, Mi ai guy, Michael Hardy, MichaelBillington,Mikael Hggstrm, Mlaffs, Mmernex, Mononomic, MonsterSRM, Mormegil, Mr. Lefty, Mrholybrain, Mrmatiko, Mrseibert, Muchness, Murdomck, Mwi33, Mydogategodshat, Mzucherman,Mslimix, Nabeth, Neilc, Nick Number, NonNobisSolum, Noodlez84, Oddbodz, OingoBoingo2, OldManInACoffeeCan, Oliver202, Ooz dot ie, OrgasGirl, Paddles, Pakaran, Pat grenier, PavelVozenilek, Pd75, Pearle, Perfectblue97, Pgr94, Pharaoh of the Wizards, Philip Trueman, Piano non troppo, Pinethicket, Prakash Nadkarni, Predictor, Programmar, Prokopenya Viktor,Proofreader77, PseudoSudo, Quintote, Qwertyus, Qxz, R Calvete, R'n'B, RDBury, Rails, RapidR, Reach Out to the Truth, Reconsider the static, Rich Farmbrough, Richardhenricks, RickK,Rmosler2100, Robert Kowalski, Ronz, Rspeer, Rursus, Ryulong, SNowwis, Sam Korn, Sam42, Sander Sde, SarekOfVulcan, Sarnholm, Saswat012, SatuSuro, Savant13, Scott Dial, Semperf,Serdar XoXo, Sesu Prime, Shadowjams, Shaggyjacobs, Shepard, Shizhao, SimonArlott, SimonLyall, Skinneal, Skyezx, Skysmith, SlackerMom, Snowolf, Solitude, Sortior, SpeedyGonsales,Spellmaster, Sprocc, Squidfish, Srleffler, Stephen Bain, Stevag, Storkk, Supten, Swollib, Sylvain Mielot, Talk2xpert, TerrorTowers, TexMurphy, The Epopt, Thistheman, Tide rolls, Tim1357,Titov Anton, Tmg1165, Tobias Bergemann, Tom Pippens, Topiarydan, Treyt021, Ulric1313, Useight, Van helsing, Vangelis12, VictorAnyakin, Vrenator, WaltBusterkeys, Wayiran, West Brom4ever, WikiLaurent, Wikilibrarian, WillWare, Woohookitty, Wrathchild, X96lee15, Yonkie, Youblend2, Youssefsan, ZENDELBACH, Zoicon5, Zzuuzz, , 670 anonymous edits

LicenseCreative Commons Attribution-Share Alike 3.0 Unported//creativecommons.org/licenses/by-sa/3.0/

Expert systemHistorySoftware architecture The rule base or knowledge base The inference engine

Advantages Conversational Quick availability and opportunity to program itself Ability to exploit a considerable amount of knowledge Reliability Scalability Pedagogy Preservation and improvement of knowledge New areas neglected by conventional computing

Disadvantages Application field Examples of applications Knowledge engineering ReferencesBibliography TextbooksHistory of AIOther

External links

License