Editing Expert system (section)

=== Formal introduction and later developments ===
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This previous situation gradually led to the development of expert systems, which used knowledge-based approaches. These expert systems in medicine were the [[MYCIN]] expert system,<ref>{{cite journal |vauthors=Shortliffe EH, and Buchanan BG |title=A model of inexact reasoning in medicine |journal=Mathematical Biosciences |date=1975 |volume=23 |issue=3–4 |pages=351–379 |doi=10.1016/0025-5564(75)90047-4 |s2cid=118063112}}</ref> the [[Internist-I]] expert system<ref>{{cite journal |vauthors=Miller RA, Pople Jr HE, and Myers JD |date=1982 |title=Internist-I, an experimental computer-based diagnostic consultant for general internal medicine |journal=New England Journal of Medicine |volume=307 |issue=8 |pages=468–476 |doi=10.1056/NEJM198208193070803 |pmid=7048091}}</ref> and later, in the middle of the 1980s, the [[CADUCEUS (expert system)|CADUCEUS]].<ref>{{cite book |last1=Feigenbaum |first1=Edward |pages=1–275 |title=The fifth generation |year=1984 |publisher=Addison-Wesley |isbn=978-0451152640 |first2=Pamela |last2=McCorduck}}</ref>

Expert systems were formally introduced around 1965 by the [[Stanford University|Stanford]] Heuristic Programming Project led by [[Edward Feigenbaum]], who is sometimes termed the "father of expert systems";<ref>{{Cite web |last=Joseph |first=Staney |date=2023-10-30 |title=The Diversity of Artificial Intelligence: How Edward Feigenbaum Developed the Expert Systems |url=https://medium.com/@staneyjoseph.in/the-diversity-of-artificial-intelligence-how-edward-feigenbaum-developed-the-expert-systems-8fe57350efb8 |access-date=2024-01-26 |website=Medium |language=en}}</ref> other key early contributors were Bruce Buchanan and Randall Davis. The Stanford researchers tried to identify domains where expertise was highly valued and complex, such as diagnosing infectious diseases ([[Mycin]]) and identifying unknown organic molecules ([[Dendral]]).<ref>{{cite book |last1=Lea |first1=Andrew S. |year=2023 |title=Digitizing Diagnosis: Medicine, Minds, and Machines in Twentieth-Century America |publisher=Johns Hopkins University Press |pages=1–256 |isbn=978-1421446813}}</ref> The idea that "intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use"<ref>Edward Feigenbaum, 1977. Paraphrased by Hayes-Roth, et al.</ref> – as Feigenbaum said – was at the time a significant step forward, since the past research had been focused on heuristic computational methods, culminating in attempts to develop very general-purpose problem solvers (foremostly the conjunct work of [[Allen Newell]] and [[Herbert A. Simon|Herbert Simon]]).<ref>{{cite book |last1=Hayes-Roth |first1=Frederick |last2=Waterman |first2=Donald |last3=Lenat |first3=Douglas |author3-link=Douglas Lenat |year=1983 |pages=[https://archive.org/details/buildingexpertsy00temd/page/6 6–7] |title=Building Expert Systems |publisher=Addison-Wesley |isbn=978-0-201-10686-2 |url=https://archive.org/details/buildingexpertsy00temd/page/6}}</ref> Expert systems became some of the first truly successful forms of [[artificial intelligence]] (AI) software.<ref name="Simon & Schuster"/>{{sfn|Luger|Stubblefield|2004|pp=227–331}}{{sfn|Nilsson|1998|loc=chpt. 17.4}}{{sfn|McCorduck|2004|pp=327–335, 434–435}}{{sfn|Crevier|1993|pp=145–62, 197−203}}

Research on expert systems was also active in Europe. In the US, the focus tended to be on the use of [[Production system (computer science)|production rule systems]], first on systems hard coded on top of [[Lisp (programming language)|Lisp]] programming environments and then on expert system shells developed by vendors such as [[IntelliCorp (software)|Intellicorp]]. In Europe, research focused more on systems and expert systems shells developed in [[Prolog]]. The advantage of Prolog systems was that they employed a form of [[rule-based system|rule-based programming]] that was based on [[logic programming|formal logic]].<ref>George F. Luger and William A. Stubblefield, Benjamin/Cummings Publishers, Rule-Based Expert System Shell: example of code using the Prolog rule-based expert system shell</ref><ref>[http://promethee.philo.ulg.ac.be/engdep1/download/prolog/htm_docs/prolog.htm A. Michiels] {{Webarchive|url=https://web.archive.org/web/20120402132354/http://promethee.philo.ulg.ac.be/engdep1/download/prolog/htm_docs/prolog.htm |date=2012-04-02}}, Université de Liège, Belgique: "PROLOG, the first declarative language</ref>

One such early expert system shell based on Prolog was APES.<ref name="APES">{{citation |url=https://www.ojp.gov/ncjrs/virtual-library/abstracts/investigating-apes-augmented-prolog-expert-system |title=Investigating with APES (Augmented Prolog Expert System) |access-date=2024-01-03}}</ref>
One of the first use cases of [[Prolog]] and APES was in the legal area namely, the encoding of a large portion of the British Nationality Act. Lance Elliot wrote: "The British Nationality Act was passed in 1981 and shortly thereafter was used as a means of showcasing the efficacy of using Artificial Intelligence (AI) techniques and technologies, doing so to explore how the at-the-time newly enacted statutory law might be encoded into a computerized logic-based formalization. A now oft-cited research paper entitled “The British Nationality Act as a Logic Program” was published in 1986 and subsequently became a hallmark for subsequent work in AI and the law."<ref name="AI & Law">
{{citation |url=https://lance-eliot.medium.com/ai-law-british-nationality-act-unexpectedly-spurred-ai-and-law-404aea03386a |title=AI & Law: British Nationality Act Unexpectedly Spurred AI And Law |date=17 April 2021 |access-date=2023-11-13}}</ref><ref name="BNA">{{cite journal |doi=10.1145/5689.5920 |author=M.J. Sergot and F. Sadri and R.A. Kowalski and F. Kriwaczek and P. Hammond and H.T. Cory |date=May 1986 |title=The British Nationality Act as a Logic Program |journal=Communications of the ACM |volume=29 |number=5 |pages=370–386}}</ref>

In the 1980s, expert systems proliferated. Universities offered expert system courses and two-thirds of the [[Fortune 500]] companies applied the technology in daily business activities.<ref name=durkinhistory/><ref>Durkin, J. Expert Systems: Catalog of Applications. Intelligent Computer Systems, Inc., Akron, OH, 1993.</ref> Interest was international with the [[Fifth generation computer|Fifth Generation Computer Systems project]] in Japan and increased research funding in Europe.

In 1981, the first [[IBM PC]], with the [[IBM PC DOS|PC DOS]] operating system, was introduced.<ref>{{Cite web |title=The IBM PC - CHM Revolution |url=https://www.computerhistory.org/revolution/personal-computers/17/301 |access-date=2024-01-26 |website=www.computerhistory.org}}</ref> The imbalance between the high affordability of the relatively powerful chips in the PC, compared to the much more expensive cost of processing power in the mainframes that dominated the corporate IT world at the time, created a new type of architecture for corporate computing, termed the [[client–server model]].<ref>{{cite book |last1=Orfali |first1=Robert |year=1996 |title=The Essential Client/Server Survival Guide |publisher=Wiley Computer Publishing |location=New York |isbn=978-0-471-15325-2 |pages=[https://archive.org/details/essentialclients00orfa/page/1 1–10] |url=https://archive.org/details/essentialclients00orfa/page/1}}</ref> Calculations and reasoning could be performed at a fraction of the price of a mainframe using a PC. This model also enabled business units to bypass corporate IT departments and directly build their own applications. As a result, client-server had a tremendous impact on the expert systems market. Expert systems were already outliers in much of the business world, requiring new skills that many IT departments did not have and were not eager to develop. They were a natural fit for new PC-based shells that promised to put application development into the hands of end users and experts. Until then, the main development environment for expert systems had been high end [[Lisp machine]]s from [[Xerox]], [[Symbolics]], and [[Texas Instruments]]. With the rise of the PC and client-server computing, vendors such as Intellicorp and Inference Corporation shifted their priorities to developing PC-based tools. Also, new vendors, often financed by [[venture capital]] (such as Aion Corporation, [[Neuron Data]], Exsys, [[VP-Expert]], and many others<ref>{{cite book |last=Hurwitz |first=Judith |title=Smart or Lucky: How Technology Leaders Turn Chance into Success |year=2011 |publisher=John Wiley & Son |isbn=978-1118033784 |page=164|url=https://books.google.com/books?id=3KrTQzQHl7AC&q=expert+systems+failed+to+live+up+to+hype&pg=PA164|access-date=29 November 2013}}</ref><ref>{{cite journal |first=Robert J. |last=Dunn |date=September 30, 1985 |title=Expandable Expertise for Everyday Users |page=30 |journal=InfoWorld |volume=7 |issue=39 |url=https://books.google.com/books?id=iS8EAAAAMBAJ&pg=PA30 |access-date=2011-03-13}}</ref>), started appearing regularly.

The first expert system to be used in a design capacity for a large-scale product was the Synthesis of Integral Design (SID) software program, developed in 1982. Written in [[Lisp (programming language)|Lisp]], SID generated 93% of the [[VAX 9000]] CPU logic gates.<ref name="SIDDTJ">{{cite journal |last1=Gibson |first1=Carl S. |display-authors=etal |title=VAX 9000 Series |journal=Digital Technical Journal of Digital Equipment Corporation |volume=2 |issue=4, Fall 1990 |pages=118–129}}</ref> Input to the software was a set of rules created by several expert logic designers. SID expanded the rules and generated software [[logic synthesis]] routines many times the size of the rules themselves. Surprisingly, the combination of these rules resulted in an overall design that exceeded the capabilities of the experts themselves, and in many cases out-performed the human counterparts. While some rules contradicted others, top-level control parameters for speed and area provided the tie-breaker. The program was highly controversial but used nevertheless due to project budget constraints. It was terminated by logic designers after the VAX 9000 project completion.

During the years before the middle of the 1970s, the expectations of what expert systems can accomplish in many fields tended to be extremely optimistic. At the start of these early studies, researchers were hoping to develop entirely automatic (i.e., completely computerized) expert systems. The expectations of people of what computers can do were frequently too idealistic. This situation radically changed after [[Richard M. Karp]] published his breakthrough paper: “Reducibility among Combinatorial Problems” in the early 1970s.<ref>{{cite book |last1=Karp |first1=Richard M. |year=1972 |chapter=Reducibility Among Combinatorial Problems |chapter-url=http://www.cs.berkeley.edu/~luca/cs172/karp.pdf |title=Complexity of Computer Computations |editor1-last=Miller |editor1-first=R. E. |editor2-last=Thatcher |editor2-first=J. W. |publisher=New York: Plenum |pages=85–103 |access-date=2020-01-24 |archive-date=2011-06-29 |url-status=dead |archive-url=https://web.archive.org/web/20110629023717/http://www.cs.berkeley.edu/~luca/cs172/karp.pdf}}</ref> Thanks to Karp's work, together with other scholars, like Hubert L. Dreyfus,<ref>{{cite book |author=Hubert L. Dreyfus |title=What Computers Still Can't Do |publisher=Cambridge, Massachusetts: The MIT Press |year=1972}}</ref> it became clear that there are certain limits and possibilities when one designs computer algorithms. His findings describe what computers can do and what they cannot do. Many of the computational problems related to this type of expert systems have certain pragmatic limits. These findings laid down the groundwork that led to the next developments in the field.<ref name="CADsurvey"/>

In the 1990s and beyond, the term ''expert system'' and the idea of a standalone AI system mostly dropped from the IT lexicon. There are two interpretations of this. One is that "expert systems failed": the IT world moved on because expert systems did not deliver on their over hyped promise.<ref>{{Cite web |url=http://www.ainewsletter.com/newsletters/aix_0501.htm#w |title=AI Expert Newsletter: W is for Winter |access-date=2013-11-29 |archive-url=https://web.archive.org/web/20131109201636/http://www.ainewsletter.com/newsletters/aix_0501.htm#w |archive-date=2013-11-09 |url-status=dead}}</ref><ref>{{cite journal |last1=Leith |first1=P. |date=2010 |url=http://ejlt.org//article/view/14/1 |title=The rise and fall of the legal expert system |journal=European Journal of Law and Technology |volume=1 |issue=1 |access-date=2020-01-24 |url-status=dead |archive-date=2016-03-04 |archive-url=https://web.archive.org/web/20160304124042/http://ejlt.org//article/view/14/1}}</ref> The other is the mirror opposite, that expert systems were simply victims of their success: as IT professionals grasped concepts such as rule engines, such tools migrated from being standalone tools for developing special purpose ''expert'' systems, to being one of many standard tools.<ref>{{cite journal |last1=Haskin |first1=David |date=January 16, 2003 |title=Years After Hype, 'Expert Systems' Paying Off for Some |journal=Datamation |url=http://www.datamation.com/netsys/article.php/1570851/Years-After-Hype-Expert-Systems-Paying-Off-For-Some.htm|access-date=29 November 2013}}</ref> Other researchers suggest that Expert Systems caused inter-company power struggles when the IT organization lost its exclusivity in software modifications to users or Knowledge Engineers.<ref>{{cite journal |last1=Romem |first1=Yoram |title=The Social Construction of Expert Systems |journal=Human Systems Management |date=2007 |volume=26 |issue=4 |pages=291–309 |doi=10.3233/HSM-2007-26406 |url=https://www.researchgate.net/publication/228630533}}</ref>

In the first decade of the 2000s, there was a "resurrection" for the technology, while using the term ''[[rule-based system]]s'', with significant success stories and adoption.<ref>{{cite news |last1=Voelker |first1=Michael P. |date=October 18, 2005 |url=https://www.informationweek.com/information-management/business-makes-the-rules |title=Business Makes the Rules |work=Information Week}}</ref> Many of the leading major business application suite vendors (such as [[SAP (software)|SAP]], [[Siebel Systems|Siebel]], and [[Oracle Corporation|Oracle]]) integrated expert system abilities into their suite of products as a way to specify business logic. Rule engines are no longer simply for defining the rules an expert would use but for any type of complex, volatile, and critical business logic; they often go hand in hand with business process automation and integration environments.<ref>{{cite web |last=SAP News Desk |title=SAP News Desk IntelliCorp Announces Participation in SAP EcoHub |url=http://laszlo.sys-con.com/node/946452 |work=laszlo.sys-con.com |publisher=LaszloTrack |access-date=29 November 2013 |archive-date=3 December 2013 |archive-url=https://web.archive.org/web/20131203002523/http://laszlo.sys-con.com/node/946452 |url-status=dead}}</ref><ref>{{cite web |last=Pegasystems |title=Smart BPM Requires Smart Business Rules |url=http://www.pega.com/business-rules |work=pega.com |access-date=29 November 2013}}</ref><ref>{{cite conference |last1=Zhao |first1=Kai |first2=Shi |last2=Ying |first3=Linlin |last3=Zhang |first4=Luokai |last4=Hu |date=9–10 Oct 2010 |title=Achieving business process and business rules integration using SPL |book-title=Future Information Technology and Management Engineering (FITME) |volume=2 |pages=329–332 |location=Changzhou, China |publisher=IEEE |doi=10.1109/fitme.2010.5656297 |isbn=978-1-4244-9087-5}}</ref>