Expert system - Knowledge

263:. 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 395: 618:

had a formal syntax where a misplaced comma or other character could cause havoc as with any other computer language. Also, as expert systems moved from prototypes in the lab to deployment in the business world, issues of integration and maintenance became far more critical. Inevitably demands to integrate with, and take advantage of, large legacy databases and systems arose. To accomplish this, integration required the same skills as any other type of system.

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is true. One of the early innovations of expert systems shells was to integrate inference engines with a user interface. This could be especially powerful with backward chaining. If the system needs to know a particular fact but does not, then it can simply generate an input screen and ask the user if the information is known. So in this example, it could use R1 to ask the user if Socrates was a Man and then use that new information accordingly.

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the later years of expert systems was focused on tools for knowledge acquisition, to help automate the process of designing, debugging, and maintaining rules defined by experts. However, when looking at the life-cycle of expert systems in actual use, other problems – essentially the same problems as those of any other large system – seem at least as critical as knowledge acquisition: integration, access to large databases, and performance.

20: 189:). The idea that "intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use" – 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 553:

rules which fired to cause the assertion and present those rules to the user as an explanation. In English, if the user asked "Why is Socrates Mortal?" the system would reply "Because all men are mortal and Socrates is a man". A significant area for research was the generation of explanations from the knowledge base in natural English rather than simply by showing the more formal but less intuitive rules.

991: 966:, that provided automated clinical diagnostic comments on endocrine reports from a pathology laboratory. It was one of the first medical expert systems to go into routine clinical use internationally and the first expert system to be used for diagnosis daily in Australia. The system was written in "C" and ran on a PDP-11 in 64K of memory. It had 661 rules that were compiled; not interpreted. 108: 94:

medicine and biology. These early diagnostic systems used patients’ symptoms and laboratory test results as inputs to generate a diagnostic outcome. These systems were often described as the early forms of expert systems. However, researchers realized that there were significant limits when using traditional methods such as flow charts, statistical pattern matching, or probability theory.

346:) 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. 236:

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."

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Hayes-Roth divides expert systems applications into 10 categories illustrated in the following table. The example applications were not in the original Hayes-Roth table, and some of them arose well afterward. Any application that is not footnoted is described in the Hayes-Roth book. Also, while these

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A claim for expert system shells that was often made was that they removed the need for trained programmers and that experts could develop systems themselves. In reality, this was seldom if ever true. While the rules for an expert system were more comprehensible than typical computer code, they still

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Hypothetical reasoning. In this, the knowledge base can be divided up into many possible views, a.k.a. worlds. This allows the inference engine to explore multiple possibilities in parallel. For example, the system may want to explore the consequences of both assertions, what will be true if Socrates

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that evaluates the current state of the knowledge-base, applies relevant rules, and then asserts new knowledge into the knowledge base. The inference engine may also include abilities for explanation, so that it can explain to a user the chain of reasoning used to arrive at a particular conclusion by

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operating system, was introduced. 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

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Another major challenge of expert systems emerges when the size of the knowledge base increases. This causes the processing complexity to increase. For instance, when an expert system with 100 million rules was envisioned as the ultimate expert system, it became obvious that such system would be too

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problem. Obtaining the time of domain experts for any software application is always difficult, but for expert systems it was especially difficult because the experts were by definition highly valued and in constant demand by the organization. As a result of this problem, a great deal of research in

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classification. With the addition of object classes to the knowledge base, a new type of reasoning was possible. Along with reasoning simply about object values, the system could also reason about object structures. In this simple example, Man can represent an object class and R1 can be redefined as

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The key challenges that expert systems in medicine (if one considers computer-aided diagnostic systems as modern expert systems), and perhaps in other application domains, include issues related to aspects such as: big data, existing regulations, healthcare practice, various algorithmic issues, and

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Performance could be especially problematic because early expert systems were built using tools (such as earlier Lisp versions) that interpreted code expressions without first compiling them. This provided a powerful development environment, but with the drawback that it was virtually impossible to

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Ease of maintenance is the most obvious benefit. This was achieved in two ways. First, by removing the need to write conventional code, many of the normal problems that can be caused by even small changes to a system could be avoided with expert systems. Essentially, the logical flow of the program

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Backward chaining is a bit less straight forward. In backward chaining the system looks at possible conclusions and works backward to see if they might be true. So if the system was trying to determine if Mortal(Socrates) is true it would find R1 and query the knowledge base to see if Man(Socrates)

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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. The other is the mirror opposite, that expert systems were

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Soon after the dawn of modern computers in the late 1940s and early 1950s, researchers started realizing the immense potential these machines had for modern society. One of the first challenges was to make such machines able to “think” like humans – in particular, making these machines able to make

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The knowledge base represents facts about the world. In early expert systems such as Mycin and Dendral, these facts were represented mainly as flat assertions about variables. In later expert systems developed with commercial shells, the knowledge base took on more structure and used concepts from

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published his breakthrough paper: “Reducibility among Combinatorial Problems” in the early 1970s. Thanks to Karp's work, together with other scholars, like Hubert L. Dreyfus, it became clear that there are certain limits and possibilities when one designs computer algorithms. His findings describe

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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

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through an expert systems approach. For the most part this category of expert systems was not all that successful. Hearsay and all interpretation systems are essentially pattern recognition systems—looking for patterns in noisy data. In the case of Hearsay recognizing phonemes in an audio stream.

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The use of rules to explicitly represent knowledge also enabled explanation abilities. In the simple example above if the system had used R1 to assert that Socrates was Mortal and a user wished to understand why Socrates was mortal they could query the system and the system would look back at the

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Thus, in the late 1950s, right after the information age had fully arrived, researchers started experimenting with the prospect of using computer technology to emulate human decision making. For example, biomedical researchers started creating computer-aided systems for diagnostic applications in

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The limits of prior type of expert systems prompted researchers to develop new types of approaches. They have developed more efficient, flexible, and powerful methods to simulate the human decision-making process. Some of the approaches that researchers have developed are based on new methods of

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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

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The goal of knowledge-based systems is to make the critical information required for the system to work explicit rather than implicit. In a traditional computer program, the logic is embedded in code that can typically only be reviewed by an IT specialist. With an expert system, the goal was to

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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

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Dendral was a tool to study hypothesis formation in the identification of organic molecules. The general problem it solved—designing a solution given a set of constraints—was one of the most successful areas for early expert systems applied to business domains such as salespeople configuring

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Another problem related to the knowledge base is how to make updates of its knowledge quickly and effectively. Also how to add a new piece of knowledge (i.e., where to add it among many rules) is challenging. Modern approaches that rely on machine learning methods are easier in this regard.

659:). System and database integration were difficult for early expert systems because the tools were mostly in languages and platforms that were neither familiar to nor welcome in most corporate IT environments – programming languages such as Lisp and Prolog, and hardware platforms such as 560:

Truth maintenance. These systems record the dependencies in a knowledge-base so that when facts are altered, dependent knowledge can be altered accordingly. For example, if the system learns that Socrates is no longer known to be a man it will revoke the assertion that Socrates is

457:. The different approaches are dictated by whether the inference engine is being driven by the antecedent (left hand side) or the consequent (right hand side) of the rule. In forward chaining an antecedent fires and asserts the consequent. For example, consider the following rule: 667:

and large database systems, and on porting to more standard platforms. These issues were resolved mainly by the client–server paradigm shift, as PCs were gradually accepted in the IT environment as a legitimate platform for serious business system development and as affordable

181:, who is sometimes termed the "father of expert systems"; 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 ( 739:

Because of the above challenges, it became clear that new approaches to AI were required instead of rule-based technologies. These new approaches are based on the use of machine learning techniques, along with the use of feedback mechanisms.

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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.

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systems, to being one of many standard tools. 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.

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Mistral is an expert system to monitor dam safety, developed in the 1990s by Ismes (Italy). It gets data from an automatic monitoring system and performs a diagnosis of the state of the dam. Its first copy, installed in 1992 on the

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code. Expert systems were among the first truly successful forms of AI software. They were created in the 1970s and then proliferated in the 1980s, being then widely regarded as the future of AI — before the advent of successful

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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.

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Modern systems can incorporate new knowledge more easily and thus update themselves easily. Such systems can generalize from existing knowledge better and deal with vast amounts of complex data. Related is the subject of

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There are also questions on how to prioritize the use of the rules to operate more efficiently, or how to resolve ambiguities (for instance, if there are too many else-if sub-structures within one rule) and so on.

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Uncertainty systems. One of the first extensions of simply using rules to represent knowledge was also to associate a probability with each rule. So, not to assert that Socrates is mortal, but to assert Socrates

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categories provide an intuitive framework to describe the space of expert systems applications, they are not rigid categories, and in some cases an application may show traits of more than one category.

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effects when using known facts and trying to generalize to other cases not described explicitly in the knowledge base. Such problems exist with methods that employ machine learning approaches too.

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A simple example of forward chaining would be to assert Man(Socrates) to the system and then trigger the inference engine. It would match R1 and assert Mortal(Socrates) into the knowledge base.

1143: 3689: 614:. With an expert system shell it was possible to enter a few rules and have a prototype developed in days rather than the months or year typically associated with complex IT projects. 588:. Although they were not highly used in expert systems, classifiers are very powerful for unstructured volatile domains, and are a key technology for the Internet and the emerging 1114: 1711: 2355: 290:

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

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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

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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

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specify the rules in a format that was intuitive and easily understood, reviewed, and even edited by domain experts rather than IT experts. The benefits of this explicit

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important decisions the way humans do. The medical–healthcare field presented the tantalizing challenge of enabling these machines to make medical diagnostic decisions.

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and personal computers. As a result, much effort in the later stages of expert system tool development was focused on integrating with legacy environments such as

410:. Expert systems were the first commercial systems to use a knowledge-based architecture. In general view, an expert system includes the following components: a 158:

This previous situation gradually led to the development of expert systems, which used knowledge-based approaches. These expert systems in medicine were the

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complex and it would face too many computational problems. An inference engine would have to be able to process huge numbers of rules to reach a decision.

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were medical diagnosis systems. The user describes their symptoms to the computer as they would to a doctor and the computer returns a medical diagnosis.

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It is a closed world with specific knowledge, in which there is no deep perception of concepts and their interrelationships until an expert provides them.

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be mortal with some probability value. Simple probabilities were extended in some systems with sophisticated mechanisms for uncertain reasoning, such as

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M.J. Sergot and F. Sadri and R.A. Kowalski and F. Kriwaczek and P. Hammond and H.T. Cory (May 1986). "The British Nationality Act as a Logic Program".

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How to verify that decision rules are consistent with each other is also a challenge when there are too many rules. Usually such problem leads to a

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As expert systems evolved, many new techniques were incorporated into various types of inference engines. Some of the most important of these were:

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CPU logic gates. Input to the software was a set of rules created by several expert logic designers. SID expanded the rules and generated software

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Multiple expertise: Several expert systems can be run simultaneously to solve a problem. and gain a higher level of expertise than a human expert.

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in Brazil), and on landslide sites under the name of Eydenet, and on monuments under the name of Kaleidos. Mistral is a registered trade mark of

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Miller RA, Pople Jr HE, and Myers JD (1982). "Internist-I, an experimental computer-based diagnostic consultant for general internal medicine".

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Gorry GA, Kassirer JP, Essig A, and Schwartz WB (1973). "Decision analysis as the basis for computer-aided management of acute renal failure".

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Pham HN, Triantaphyllou E (2008). "Prediction of diabetes by employing a new data mining approach which balances fitting and generalization".

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Increased availability and reliability: Expertise can be accessed on any computer hardware and the system always completes responses on time.

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K. Horn; L. Lazarus; P. Compton; J.R. Quinlan (1985). "An expert system for the interpretation of thyroid assays in a clinical laboratory".

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Zhao, Kai; Ying, Shi; Zhang, Linlin; Hu, Luokai (9–10 Oct 2010). "Achieving business process and business rules integration using SPL".

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Plenary Paper Presented at: International Federation of Automatic Control (IFAC) Symposium on Compute R Aided Design in Control Systems

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Other early examples were analyzing sonar data to detect Russian submarines. These kinds of systems proved much more amenable to a

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Rosati RA, McNeer JF, Starmer CF, Mittler BS, Morris JJ, and Wallace AG (1975). "A new information system for medical practice".

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Yanase J, Triantaphyllou E (2019). "A Systematic Survey of Computer-Aided Diagnosis in Medicine: Past and Present Developments".

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Hollan, J.; Hutchins, E.; Weitzman, L. (1984). "STEAMER: An interactive inspectable simulation-based training system".

1977: 1744: 1694: 334:, with significant success stories and adoption. Many of the leading major business application suite vendors (such as 307:

expectations of people of what computers can do were frequently too idealistic. This situation radically changed after

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Yanase J, Triantaphyllou E (2019). "The Seven Key Challenges for the Future of Computer-Aided Diagnosis in Medicine".

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Weiss SM, Kulikowski CA, Amarel S, Safir A (1978). "A model-based method for computer-aided medical decision-making".

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were replaced by values of object instances. The rules worked by querying and asserting values of the objects.

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Expert systems have superficial knowledge, and a simple task can potentially become computationally expensive.

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Dam (Italy), is still operational 24/7/365. It has been installed on several dams in Italy and abroad (e.g.,

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Shan N, and Ziarko W (1995). "Data-based acquisition and incremental modification of classification rules".

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In the 1980s, expert systems proliferated. Universities offered expert system courses and two-thirds of the

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Durkin, J. Expert Systems: Catalog of Applications. Intelligent Computer Systems, Inc., Akron, OH, 1993.

2512: 1911: 1064: 442: 48: 3347:"SMH.PAL: an expert system for identifying treatment procedures for students with severe disabilities" 584:

a rule that defines the class of all men. These types of special purpose inference engines are termed

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In the first decade of the 2000s, there was a "resurrection" for the technology, while using the term

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Research on expert systems was also active in Europe. In the US, the focus tended to be on the use of

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MacGregor, Robert (June 1991). "Using a description classifier to enhance knowledge representation".

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companies applied the technology in daily business activities. Interest was international with the

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often take advantage of such mechanisms. Related is the discussion on the disadvantages section.

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Szolovits P, Patil RS, and Schwartz WB (1988). "Artificial intelligence in medical diagnosis".

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Expert systems: the technology of knowledge management and decision making for the 21st century

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Kwak, S. H. (1990). "A mission planning expert system for an autonomous underwater vehicle".

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Real Time Process Control, Space Shuttle Mission Control, Smart Autoclave Cure of Composites

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The expert system may choose the most inappropriate method for solving a particular problem.

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Expert systems require knowledge engineers to input the data, data acquisition is very hard.

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P. Compton; K. Horn; R. Quinlan; L. Lazarus; K. Ho (1988). "Maintaining an Expert System".

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One such early expert system shell based on Prolog was APES. One of the first use cases of

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Please expand the section to include this information. Further details may exist on the

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The most common disadvantage cited for expert systems in the academic literature is the

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SMH.PAL is an expert system for the assessment of students with multiple disabilities.

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programming environments and then on expert system shells developed by vendors such as

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Bleich HL (1972). "Computer-based consultation: Electrolyte and acid-base disorders".

1008:"Its first copy, installed in 1992 on the Dam (Italy), is still operational 24/7/365." 4001: 3958: 3930: 3841: 3821: 3720: 3649: 3620: 3587: 3560: 3508: 3407: 3279:

Proceedings of the Fourth Australian Conference on the Applications of Expert Systems

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Schwartz WB (1970). "Medicine and the computer: the promise and problems of change".

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Buchanan, B. (1986). "Expert systems: working systems and the research literature".

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Fast response: The expert systems are fast and able to solve a problem in real-time.

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An Expert System for the Management of Hazardous Materials at a Naval Supply Center

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Shortliffe EH, and Buchanan BG (1975). "A model of inexact reasoning in medicine".

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Summing up the benefits of using expert systems, the following can be highlighted:

450: 418:, an explanation facility, a knowledge acquisition facility, and a user interface. 415: 356: 194: 72: 2973: 2061: 3981: 3750: 2863: 2646:"Expert system applications in business: a review and analysis of the literature" 1819: 308: 299: 280: 3063: 2326: 2325:

Chung, Junyoung; Gulcehre, Caglar; Cho, Kyunghyun; Bengio, Yoshua (2015-06-01).

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Digitizing Diagnosis: Medicine, Minds, and Machines in Twentieth-Century America

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Finally, the following disadvantages of using expert systems can be summarized:

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More recently, it can be argued that expert systems have moved into the area of

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Ledley RS, and Lusted LB (1959). "Reasoning foundations of medical diagnosis".

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here. Sometimes these type of expert systems are called "intelligent systems."

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Artificial Intelligence: Structures and Strategies for Complex Problem Solving

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Lancini, Stefano; Lazzari, Marco; Masera, Alberto; Salvaneschi, Paolo (1997).

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Proceedings of the 1990 Symposium on Autonomous Underwater Vehicle Technology

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Problems of ethics in the use of any form of AI are very relevant at present.

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Haddawy, P; Suebnukarn, S. (2010). "Intelligent Clinical Training Systems".

3301: 2702: 3411: 3403: 3325:"REACTOR: An Expert System for Diagnosis and Treatment of Nuclear Reactors" 3024: 2746: 2202: 1373: 669: 660: 637:

Reduced cost: The cost of expertise for each user is significantly reduced.

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Explanation: Expert systems always describe of how the problem was solved.

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Rasmussen, Arthur; Muratore, John F.; Heindel, Troy A. (February 1990).

2244: 1822:, Université de Liège, Belgique: "PROLOG, the first declarative language 3227: 2958: 2793: 1215: 1213: 975: 255: 16:

Computer system emulating the decision-making ability of a human expert

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tracing back over the firing of rules that resulted in the assertion.

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is a computer system emulating the decision-making ability of a human

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AI & Law: British Nationality Act Unexpectedly Spurred AI And Law

1039: 971: 691:, say n of them, and then the corresponding search space is of size 2 272: 197:). Expert systems became some of the first truly successful forms of 24: 2974:"Knowledge-based systems and knowledge management: friends or foes?" 1230: 1228: 1210: 919:

Interpreting, predicting, repairing, and monitoring system behaviors

2501:"An Assessment of Tools for Building Large Knowledge-Based Systems" 2339: 1180: 848: 536:{\displaystyle R1:{\mathit {Man}}(x)\implies {\mathit {Mortal}}(x)} 372: 295: 220:. The advantage of Prolog systems was that they employed a form of 3479:"The INCO Expert System Project: CLIPS in Shuttle mission control" 672:

servers provided the processing power needed for AI applications.

3915: 3859: 2763:(1992). "On the thresholds of knowledge". In Kirsh, David (ed.). 2381:"Hybrid categorical expert system for use in content aggregation" 2237:"SAP News Desk IntelliCorp Announces Participation in SAP EcoHub" 1225: 836: 186: 2294:

Future Information Technology and Management Engineering (FITME)

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match the efficiency of the fastest compiled languages (such as

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Berners-Lee, Tim; Hendler, James; Lassila, Ora (May 17, 2001).

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Smart or Lucky: How Technology Leaders Turn Chance into Success

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Catlett, J. (1990). "Expert systems, the risks and rewards".

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refers to, all the sources cited seem to be from before 2000.

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Expert systems were formally introduced around 1965 by the

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Digital Technical Journal of Digital Equipment Corporation

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Edward Feigenbaum, 1977. Paraphrased by Hayes-Roth, et al.

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Salvaneschi, Paolo; Cadei, Mauro; Lazzari, Marco (1996).

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GARVAN-ES1 was a medical expert system, developed at the

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expert system and later, in the middle of the 1980s, the

3476: 2426:"Knowledge-Based Systems Concepts, Techniques, Examples" 2161:"Years After Hype, 'Expert Systems' Paying Off for Some" 1834:

Investigating with APES (Augmented Prolog Expert System)

287:, Exsys, and many others), started appearing regularly. 64:. An expert system is divided into two subsystems: 1) a 3097: 3052:

Journal of the American Medical Informatics Association

2643:

Wong, Bo K.; Monaco, John A.; Monaco (September 1995).

2604: 2455: 1768: 1649: 908:

Diagnosing, assessing, and correcting student behaviour

349: 3432: 2856:

Soft Computing for Knowledge Discovery and Data Mining

2816: 2324: 1614: 1137: 1135: 3557:

AI: The Tumultuous Search for Artificial Intelligence

3045: 956:

computers and mortgage loan application development.

697: 465: 3002: 2638: 2636: 1308: 886:

Providing incremental solutions for complex problems

449:

There are mainly two modes for an inference engine:

3188:"Diagnosing Ancient Monuments with Expert Software" 2786:

9th International Conference on Automated Deduction

2715: 2060:. New York: Plenum. pp. 85–103. Archived from 1684: 1132: 867:Mission Planning for Autonomous Underwater Vehicle 355:artificial intelligence (AI), and in particular in 247:in Japan and increased research funding in Europe. 51:through bodies of knowledge, represented mainly as 3612: 3579: 3140: 3099: 2971: 2894: 2849: 2644: 2589: 2451: 2449: 1739:. Johns Hopkins University Press. pp. 1–256. 897:Executing a plan to administer a prescribed remedy 709: 535: 3573: 3379: 2633: 2084: 1189: 804:Inferring likely consequences of given situations 793:Inferring situation descriptions from sensor data 717:. Thus, the search space can grow exponentially. 4019: 2642: 2030:Gibson, Carl S.; et al. "VAX 9000 Series". 1941:. New York: Wiley Computer Publishing. pp. 1343: 911:SMH.PAL, Intelligent Clinical Training, STEAMER 606:were rapid development and ease of maintenance. 2446: 2291: 1245: 1243: 825:, PUFF, Mistral, Eydenet, Kaleidos, GARVAN-ES1 2909: 2709: 2127:"The rise and fall of the legal expert system" 1685:Feigenbaum, Edward; McCorduck, Pamela (1984). 875:Comparing observations to plan vulnerabilities 815:Inferring system malfunctions from observables 70:, which represents facts and rules; and 2) an 3690: 2379:Kiryanov, Denis Aleksandrovich (2021-12-21). 1141: 185:) and identifying unknown organic molecules ( 137:. Please discuss this issue on the article's 31:: an early (1984) platform for expert systems 3249: 3005:International Journal of Medical Informatics 2945:Coats PK (1988). "Why expert systems fail". 2331:International Conference on Machine Learning 2056:. In Miller, R. E.; Thatcher, J. W. 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Simon & Schuster. pp. 22–23. 937:AI solution than a rule-based approach. 928:Hearsay was an early attempt at solving 393: 389: 208:, first on systems hard coded on top of 18: 3607: 3551: 3521: 3447: 3131: 3088: 2498: 2353: 2265: 2215: 2101:"AI Expert Newsletter: W is for Winter" 1965: 1249: 1234: 1204: 1142:Russell, Stuart; Norvig, Peter (1995). 1088: 4020: 3670:Expert System tutorial on Code Project 3335: 3322: 3221: 2765:Foundations Of Artificial Intelligence 2158: 2131:European Journal of Law and Technology 2023: 2000:Dunn, Robert J. (September 30, 1985). 1934: 1458: 363:approaches with a feedback mechanism. 3678: 2944: 2777: 2759: 2423: 2374: 2372: 2370: 2184: 2124: 1972:. 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New York, NY: BasicBooks. 2978:Information & Management 2864:10.1007/978-0-387-69935-6_16 2823:Information & Management 2663:10.1016/0378-7206(95)00023-p 1629:10.1016/0025-5564(75)90047-4 1543:10.1016/0002-9343(73)90204-0 1473:10.1016/0002-9343(72)90170-2 1411:10.1016/0004-3702(78)90015-2 1222:, pp. 327–335, 434–435. 1050:Constraint logic programming 7: 3850:Attempto Controlled English 3064:10.1136/jamia.1994.95153433 2897:Computer and Inf. Science G 2424:Smith, Reid (May 8, 1985). 1779:. Addison-Wesley. pp. 1664:10.1056/NEJM198208193070803 1574:Annals of Internal Medicine 1438:10.1056/NEJM197012032832305 1237:, pp. 145–62, 197−203. 1117:. Pcmag.com. Archived from 1070:Rule-based machine learning 1033: 441:The inference engine is an 424:object-oriented programming 402:from a 1990 Master's Thesis 294:, SID generated 93% of the 283:(such as Aion Corporation, 129:content into sub-articles, 10: 4054: 3530:Hub Information Technology 3207:10.2749/101686697780494392 2912:Computational Intelligence 2651:Information and Management 2615:. Addison-Wesley. p. 2302:10.1109/fitme.2010.5656297 2218:"Business Makes the Rules" 2085:Hubert L. Dreyfus (1972). 1586:10.7326/0003-4819-108-1-80 1366:10.1126/science.130.3366.9 1323:10.1016/j.eswa.2019.112821 1115:"Conventional programming" 1065:Learning classifier system 443:automated reasoning system 79: 62:artificial neural networks 3997:Preference-based planning 3972: 3939: 3883: 3840: 3797: 3764: 3716: 3345:Hofmeister, Alan (1994). 2499:Mettrey, William (1987). 2049:Karp, Richard M. 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Archived from 1358:1959Sci...130....9L 1192:, pp. 227–331. 744:system assessment. 428:classes, subclasses 261:client–server model 250:In 1981, the first 162:expert system, the 3974:Automated planning 3842:Ontology languages 3812:Constraint solvers 3646:Machines Who Think 3361:on 3 December 2013 2794:10.1007/BFb0012830 2594:on April 24, 2013. 2466:. Addison-Wesley. 2247:on 3 December 2013 2241:laszlo.sys-con.com 2125:Leith, P. (2010). 1818:2012-04-02 at the 730:overgeneralization 707: 533: 404: 331:rule-based systems 224:that was based on 33: 4015: 4014: 4002:Reactive planning 3959:Local consistency 3799:Reasoning systems 3746:Inference engines 3721:Backward chaining 3642:McCorduck, Pamela 3626:978-1-55860-467-4 3593:978-0-8053-4780-7 3118:10.1109/64.511774 2873:978-0-387-69934-9 2738:978-0-201-11519-2 2721:McCorduck, Pamela 2694:978-1-84628-475-5 2626:978-0-201-10686-2 2473:978-0-201-10686-2 2311:978-1-4244-9087-5 1952:978-0-471-15325-2 1889:10.1145/5689.5920 1790:978-0-201-10686-2 1432:(23): 1257–1264. 1261:978-0-12-443880-4 1254:. pp. 1–22. 1158:978-0-13-103805-9 1100:978-0-201-87686-4 1031: 1030: 1002:about which date 930:voice recognition 926: 925: 864:Designing actions 782:Problem addressed 612:rapid prototyping 455:backward chaining 400:backward chaining 277:Texas Instruments 179:Edward Feigenbaum 156: 155: 85:Early development 4045: 3751:Proof assistants 3736:Forward chaining 3699: 3692: 3685: 3676: 3675: 3658: 3637: 3635: 3633: 3618: 3604: 3602: 3600: 3585: 3570: 3538: 3537: 3525: 3519: 3518: 3500: 3494: 3493: 3491: 3489: 3474: 3468: 3467: 3465: 3463: 3454: 3445: 3439: 3438: 3430: 3424: 3423: 3397: 3377: 3371: 3370: 3368: 3366: 3342: 3333: 3332: 3320: 3314: 3313: 3289: 3283: 3282: 3274: 3268: 3267: 3247: 3238: 3237: 3225: 3219: 3218: 3192: 3183: 3174: 3173: 3156:(2–3): 185–195. 3147: 3138: 3129: 3128: 3126: 3124: 3103: 3095: 3086: 3085: 3075: 3043: 3037: 3036: 3000: 2994: 2993: 2969: 2963: 2962: 2942: 2936: 2935: 2907: 2901: 2900: 2892: 2886: 2885: 2847: 2841: 2840: 2838: 2814: 2808: 2807: 2778:Bezem M (1988). 2775: 2769: 2768: 2757: 2751: 2750: 2713: 2707: 2706: 2680: 2674: 2673: 2671: 2669: 2648: 2640: 2631: 2630: 2602: 2596: 2595: 2593: 2588:. 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Karp 300:logic synthesis 281:venture capital 201:(AI) software. 152: 146: 143: 124: 111: 107: 100: 87: 82: 17: 12: 11: 5: 4051: 4041: 4040: 4035: 4033:Expert systems 4030: 4013: 4012: 4010: 4009: 4004: 3999: 3994: 3989: 3984: 3978: 3976: 3970: 3969: 3967: 3966: 3961: 3956: 3951: 3945: 3943: 3937: 3936: 3934: 3933: 3928: 3923: 3918: 3913: 3908: 3903: 3898: 3893: 3887: 3885: 3881: 3880: 3878: 3877: 3872: 3867: 3862: 3857: 3852: 3846: 3844: 3838: 3837: 3835: 3834: 3829: 3824: 3822:Logic programs 3819: 3814: 3809: 3803: 3801: 3795: 3794: 3792: 3791: 3786: 3781: 3776: 3770: 3768: 3766:Expert systems 3762: 3761: 3759: 3758: 3753: 3748: 3743: 3738: 3733: 3728: 3723: 3717: 3714: 3713: 3702: 3701: 3694: 3687: 3679: 3673: 3672: 3665: 3664:External links 3662: 3660: 3659: 3654: 3638: 3625: 3605: 3592: 3571: 3565: 3548: 3546: 3543: 3540: 3539: 3520: 3513: 3495: 3469: 3440: 3425: 3372: 3334: 3315: 3284: 3269: 3252:Expert Systems 3239: 3220: 3201:(4): 288–291. 3175: 3130: 3087: 3058:(6): 439–446. 3038: 2995: 2984:(2): 113–125. 2964: 2937: 2918:(2): 357–370. 2902: 2887: 2872: 2842: 2809: 2802: 2770: 2761:Lenat, Douglas 2752: 2737: 2729:Addison-Wesley 2708: 2693: 2675: 2657:(3): 141–152. 2632: 2625: 2607:Lenat, Douglas 2597: 2560: 2525: 2491: 2479: 2472: 2458:Lenat, Douglas 2445: 2416: 2366: 2346: 2317: 2310: 2284: 2258: 2227: 2208: 2197:(4): 291–309. 2177: 2151: 2117: 2092: 2077: 2041: 2022: 1992: 1979:978-1118033784 1978: 1958: 1951: 1927: 1903: 1894: 1883:(5): 370–386. 1867: 1845: 1824: 1805: 1796: 1789: 1771:Lenat, Douglas 1761: 1752: 1746:978-1421446813 1745: 1727: 1702: 1696:978-0451152640 1695: 1677: 1658:(8): 468–476. 1642: 1607: 1564: 1537:(4): 473–484. 1521: 1486: 1467:(3): 285–291. 1451: 1416: 1379: 1352:(3366): 9–21. 1336: 1294: 1267: 1260: 1239: 1224: 1220:McCorduck 2004 1209: 1194: 1179: 1157: 1131: 1106: 1099: 1080: 1079: 1077: 1074: 1073: 1072: 1067: 1062: 1057: 1052: 1047: 1042: 1035: 1032: 1029: 1028: 997: 995: 988: 935:neural network 924: 923: 920: 917: 913: 912: 909: 906: 902: 901: 898: 895: 891: 890: 887: 884: 880: 879: 876: 873: 869: 868: 865: 862: 856: 855: 834: 831: 827: 826: 816: 813: 809: 808: 805: 802: 798: 797: 794: 791: 790:Interpretation 787: 786: 783: 780: 769: 766: 765: 764: 761: 758: 755: 752: 704: 700: 681:satisfiability 643: 640: 639: 638: 635: 632: 629: 626: 598: 595: 594: 593: 578: 566: 562: 532: 529: 526: 521: 518: 515: 512: 509: 506: 500: 496: 493: 490: 485: 482: 479: 474: 471: 468: 412:knowledge base 391: 388: 380:business rules 351: 348: 154: 153: 133:it, or adding 114: 112: 105: 99: 96: 86: 83: 81: 78: 67:knowledge base 15: 9: 6: 4: 3: 2: 4050: 4039: 4036: 4034: 4031: 4029: 4026: 4025: 4023: 4008: 4005: 4003: 4000: 3998: 3995: 3993: 3990: 3988: 3985: 3983: 3980: 3979: 3977: 3975: 3971: 3965: 3962: 3960: 3957: 3955: 3952: 3950: 3947: 3946: 3944: 3942: 3938: 3932: 3929: 3927: 3924: 3922: 3919: 3917: 3914: 3912: 3909: 3907: 3904: 3902: 3899: 3897: 3894: 3892: 3889: 3888: 3886: 3882: 3876: 3873: 3871: 3868: 3866: 3863: 3861: 3858: 3856: 3853: 3851: 3848: 3847: 3845: 3843: 3839: 3833: 3830: 3828: 3825: 3823: 3820: 3818: 3815: 3813: 3810: 3808: 3805: 3804: 3802: 3800: 3796: 3790: 3787: 3785: 3782: 3780: 3777: 3775: 3772: 3771: 3769: 3767: 3763: 3757: 3754: 3752: 3749: 3747: 3744: 3742: 3739: 3737: 3734: 3732: 3729: 3727: 3724: 3722: 3719: 3718: 3715: 3711: 3707: 3700: 3695: 3693: 3688: 3686: 3681: 3680: 3677: 3671: 3668: 3667: 3657: 3655:1-5688-1205-1 3651: 3647: 3643: 3639: 3628: 3622: 3617: 3616: 3610: 3609:Nilsson, Nils 3606: 3595: 3589: 3584: 3583: 3577: 3572: 3568: 3566:0-465-02997-3 3562: 3558: 3554: 3550: 3549: 3535: 3531: 3524: 3516: 3514:9781003209010 3510: 3506: 3499: 3484: 3480: 3473: 3458: 3451: 3444: 3436: 3429: 3421: 3417: 3413: 3409: 3405: 3401: 3396: 3395:10.1.1.172.60 3391: 3387: 3383: 3376: 3360: 3356: 3352: 3348: 3341: 3339: 3330: 3326: 3319: 3311: 3307: 3303: 3299: 3295: 3288: 3280: 3273: 3265: 3261: 3257: 3253: 3246: 3244: 3235: 3231: 3224: 3216: 3212: 3208: 3204: 3200: 3196: 3189: 3182: 3180: 3171: 3167: 3163: 3159: 3155: 3151: 3144: 3137: 3135: 3119: 3115: 3111: 3107: 3102: 3094: 3092: 3083: 3079: 3074: 3069: 3065: 3061: 3057: 3053: 3049: 3042: 3034: 3030: 3026: 3022: 3018: 3014: 3010: 3006: 2999: 2991: 2987: 2983: 2979: 2975: 2968: 2960: 2956: 2952: 2948: 2941: 2933: 2929: 2925: 2921: 2917: 2913: 2906: 2898: 2891: 2883: 2879: 2875: 2869: 2865: 2861: 2857: 2853: 2846: 2837: 2832: 2828: 2824: 2820: 2813: 2805: 2803:3-540-19343-X 2799: 2795: 2791: 2787: 2783: 2782: 2774: 2766: 2762: 2756: 2748: 2744: 2740: 2734: 2730: 2726: 2722: 2718: 2712: 2704: 2700: 2696: 2690: 2686: 2679: 2664: 2660: 2656: 2652: 2647: 2639: 2637: 2628: 2622: 2618: 2614: 2613: 2608: 2601: 2592: 2587: 2583: 2579: 2575: 2571: 2564: 2556: 2552: 2548: 2544: 2540: 2536: 2529: 2515:on 2013-11-10 2514: 2510: 2506: 2502: 2495: 2488: 2483: 2475: 2469: 2465: 2464: 2459: 2452: 2450: 2434: 2433:Reid G. Smith 2427: 2420: 2412: 2408: 2404: 2400: 2395: 2390: 2386: 2382: 2375: 2373: 2371: 2359: 2358: 2350: 2341: 2336: 2332: 2328: 2321: 2313: 2307: 2303: 2299: 2295: 2288: 2273: 2269: 2266:Pegasystems. 2262: 2246: 2242: 2238: 2231: 2223: 2219: 2212: 2204: 2200: 2196: 2192: 2188: 2181: 2166: 2162: 2155: 2141:on 2016-03-04 2140: 2136: 2132: 2128: 2121: 2107:on 2013-11-09 2106: 2102: 2096: 2088: 2081: 2067:on 2011-06-29 2063: 2059: 2052: 2045: 2037: 2033: 2026: 2011: 2007: 2003: 1996: 1981: 1975: 1971: 1970: 1962: 1954: 1948: 1944: 1940: 1939: 1931: 1917: 1913: 1907: 1898: 1890: 1886: 1882: 1878: 1871: 1857: 1856: 1849: 1836: 1835: 1828: 1821: 1817: 1814: 1809: 1800: 1792: 1786: 1782: 1778: 1777: 1772: 1765: 1756: 1748: 1742: 1738: 1731: 1717: 1713: 1706: 1698: 1692: 1688: 1681: 1673: 1669: 1665: 1661: 1657: 1653: 1646: 1638: 1634: 1630: 1626: 1622: 1618: 1611: 1603: 1599: 1595: 1591: 1587: 1583: 1579: 1575: 1568: 1560: 1556: 1552: 1548: 1544: 1540: 1536: 1532: 1525: 1517: 1513: 1509: 1505: 1501: 1497: 1490: 1482: 1478: 1474: 1470: 1466: 1462: 1455: 1447: 1443: 1439: 1435: 1431: 1427: 1420: 1412: 1408: 1403: 1398: 1394: 1390: 1383: 1375: 1371: 1367: 1363: 1359: 1355: 1351: 1347: 1340: 1332: 1328: 1324: 1320: 1316: 1312: 1305: 1303: 1301: 1299: 1283: 1282: 1281:The Economist 1277: 1271: 1263: 1257: 1253: 1246: 1244: 1236: 1231: 1229: 1221: 1216: 1214: 1207:, chpt. 17.4. 1206: 1201: 1199: 1191: 1186: 1184: 1168:on 5 May 2014 1164: 1160: 1154: 1147: 1146: 1138: 1136: 1121:on 2012-10-14 1120: 1116: 1110: 1102: 1096: 1092: 1085: 1081: 1071: 1068: 1066: 1063: 1061: 1058: 1056: 1053: 1051: 1048: 1046: 1043: 1041: 1038: 1037: 1025: 1015: 1009: 1005: 1001: 998:This section 996: 992: 987: 986: 983: 981: 977: 973: 967: 965: 960: 957: 955: 951: 945: 943: 940:CADUCEUS and 938: 936: 931: 921: 918: 915: 914: 910: 907: 904: 903: 899: 896: 893: 892: 888: 885: 882: 881: 877: 874: 871: 870: 866: 863: 861: 858: 857: 853: 850: 846: 842: 838: 835: 832: 829: 828: 824: 820: 817: 814: 811: 810: 806: 803: 800: 799: 795: 792: 789: 788: 784: 781: 778: 777: 774: 762: 759: 756: 753: 750: 749: 748: 745: 741: 737: 733: 731: 727: 722: 718: 702: 699: 690: 686: 682: 677: 673: 671: 666: 662: 661:Lisp machines 658: 652: 649: 642:Disadvantages 636: 633: 630: 627: 624: 623: 622: 619: 615: 613: 607: 605: 591: 587: 582: 579: 576: 572: 567: 563: 559: 558: 557: 554: 550: 546: 543: 527: 491: 472: 469: 466: 458: 456: 452: 447: 444: 439: 437: 433: 429: 425: 419: 417: 413: 409: 401: 396: 387: 385: 381: 376: 374: 368: 366: 362: 358: 347: 345: 341: 337: 333: 332: 326: 323: 318: 317:expert system 313: 310: 304: 301: 297: 293: 288: 286: 282: 278: 274: 270: 266: 265:Lisp machines 262: 257: 253: 248: 246: 242: 237: 234: 229: 227: 223: 219: 215: 211: 207: 202: 200: 196: 195:Herbert Simon 192: 188: 184: 180: 176: 171: 169: 165: 161: 150: 147:February 2024 140: 136: 132: 128: 122: 120: 115:This section 113: 104: 103: 95: 91: 77: 75: 74: 69: 68: 63: 58: 54: 53:if–then rules 50: 46: 42: 41:expert system 38: 30: 26: 21: 3832:Rule engines 3765: 3645: 3630:. Retrieved 3614: 3597:. Retrieved 3581: 3556: 3533: 3529: 3523: 3504: 3498: 3486:. Retrieved 3482: 3472: 3460:. Retrieved 3456: 3443: 3434: 3428: 3388:(4): 388–9. 3385: 3381: 3375: 3363:. Retrieved 3359:the original 3354: 3350: 3328: 3318: 3293: 3287: 3278: 3272: 3258:(1): 32–51. 3255: 3251: 3233: 3229: 3223: 3198: 3194: 3153: 3149: 3121:. Retrieved 3112:(4): 24–34. 3109: 3105: 3055: 3051: 3041: 3008: 3004: 2998: 2981: 2977: 2967: 2953:(3): 77–86. 2950: 2946: 2940: 2915: 2911: 2905: 2896: 2890: 2855: 2845: 2829:(2): 55–63. 2826: 2822: 2812: 2785: 2780: 2773: 2764: 2755: 2724: 2711: 2684: 2678: 2666:. Retrieved 2654: 2650: 2611: 2600: 2591:the original 2580:(5): 34–43. 2577: 2573: 2563: 2541:(3): 41–46. 2538: 2534: 2528: 2517:. Retrieved 2513:the original 2508: 2504: 2494: 2482: 2462: 2436:. Retrieved 2432: 2419: 2384: 2356: 2349: 2330: 2320: 2293: 2287: 2275:. Retrieved 2271: 2261: 2249:. Retrieved 2245:the original 2240: 2230: 2221: 2211: 2194: 2190: 2180: 2168:. 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Michiels 905:Instruction 726:overfitting 586:classifiers 575:fuzzy logic 361:data mining 285:Neuron Data 254:, with the 241:Fortune 500 214:Intellicorp 164:Internist-I 135:subheadings 4022:Categories 3462:3 December 2519:2013-11-29 2438:9 November 2340:1502.02367 2165:Datamation 2145:2020-01-24 2111:2013-11-29 2071:2020-01-24 2016:2011-03-13 1921:2024-01-26 1862:2023-11-13 1840:2024-01-03 1721:2024-01-26 1317:: 112821. 1288:2024-08-14 1125:2013-09-15 1076:References 1004:"is still" 976:Itaipu Dam 872:Monitoring 801:Prediction 597:Advantages 436:assertions 131:condensing 3710:reasoning 3390:CiteSeerX 3033:198287435 2411:245498498 2403:2454-0714 2006:InfoWorld 1637:118063112 1397:CiteSeerX 1331:199019309 1040:AI winter 1014:talk page 972:Ridracoli 883:Debugging 812:Diagnosis 785:Examples 499:⟹ 432:instances 273:Symbolics 139:talk page 127:splitting 125:Consider 49:reasoning 39:(AI), an 25:Symbolics 3644:(2004), 3611:(1998). 3578:(2004). 3555:(1993). 3420:11903941 3412:20686730 3310:60476847 3025:31445285 2932:38974914 2899:: 11–26. 2882:12628921 2723:(1983). 2703:70987401 2609:(1983). 2555:29575443 2460:(1983). 2272:pega.com 2012:(39): 30 1816:Archived 1773:(1983). 1602:46410202 1559:17448496 1374:13668531 1034:See also 878:REACTOR 860:Planning 849:VAX 9000 819:CADUCEUS 779:Category 581:Ontology 373:big data 296:VAX 9000 175:Stanford 168:CADUCEUS 119:too long 3916:Prover9 3911:Paradox 3860:F-logic 3236:: 7–11. 3215:8113173 3170:1746570 3123:5 March 3082:7850569 2959:3666074 2747:9324691 1672:7048091 1594:3276267 1551:4582702 1516:1156062 1481:4559984 1446:4920342 1354:Bibcode 1346:Science 1172:14 June 916:Control 837:Dendral 561:mortal. 187:Dendral 117:may be 80:History 3891:CARINE 3652: 3623: 3590: 3563: 3511: 3418: 3410: 3392: 3308: 3213: 3168: 3080: 3073:116227 3070: 3031: 3023: 2957: 2930: 2880: 2870: 2800: 2745: 2735: 2701: 2691: 2623: 2553: 2470: 2409: 2401: 2308: 1976: 1949: 1787: 1743: 1716:Medium 1693: 1670: 1635: 1600: 1592: 1557: 1549: 1514: 1479: 1444: 1399: 1372: 1329: 1258: 1155: 1097: 952:(DEC) 894:Repair 830:Design 430:, and 344:Oracle 342:, and 340:Siebel 322:expert 275:, and 256:PC DOS 252:IBM PC 233:Prolog 218:Prolog 45:expert 3921:SPASS 3906:Otter 3901:Nqthm 3865:FO(.) 3774:CLIPS 3453:(PDF) 3416:S2CID 3306:S2CID 3211:S2CID 3191:(PDF) 3166:S2CID 3146:(PDF) 3029:S2CID 2955:JSTOR 2928:S2CID 2878:S2CID 2551:S2CID 2429:(PDF) 2407:S2CID 2361:(PDF) 2335:arXiv 2065:(PDF) 2054:(PDF) 1633:S2CID 1598:S2CID 1555:S2CID 1327:S2CID 1166:(PDF) 1149:(PDF) 1045:CLIPS 942:MYCIN 823:MYCIN 665:COBOL 414:, an 269:Xerox 267:from 183:Mycin 160:MYCIN 27:3640 3855:CycL 3708:and 3650:ISBN 3634:2019 3621:ISBN 3601:2019 3588:ISBN 3561:ISBN 3509:ISBN 3490:2013 3483:NTRS 3464:2013 3408:PMID 3367:2013 3329:AAAI 3125:2014 3078:PMID 3021:PMID 2868:ISBN 2798:ISBN 2743:OCLC 2733:ISBN 2699:OCLC 2689:ISBN 2670:2013 2621:ISBN 2468:ISBN 2440:2013 2399:ISSN 2306:ISBN 2279:2013 2253:2013 2172:2013 1987:2013 1974:ISBN 1947:ISBN 1943:1–10 1785:ISBN 1741:ISBN 1691:ISBN 1668:PMID 1590:PMID 1547:PMID 1512:PMID 1477:PMID 1442:PMID 1370:PMID 1256:ISBN 1174:2014 1153:ISBN 1095:ISBN 980:CESI 728:and 453:and 434:and 382:and 359:and 292:Lisp 210:Lisp 193:and 3926:TPS 3400:doi 3298:doi 3260:doi 3203:doi 3158:doi 3114:doi 3068:PMC 3060:doi 3013:doi 3009:129 2986:doi 2920:doi 2860:doi 2831:doi 2790:doi 2659:doi 2582:doi 2578:284 2543:doi 2389:doi 2298:doi 2199:doi 1885:doi 1781:6–7 1660:doi 1656:307 1625:doi 1582:doi 1578:108 1539:doi 1504:doi 1500:135 1469:doi 1434:doi 1430:283 1407:doi 1362:doi 1350:130 1319:doi 1315:138 1006:in 954:VAX 852:CPU 571:may 336:SAP 35:In 4024:: 3931:Z3 3532:. 3507:. 3481:. 3455:. 3414:. 3406:. 3398:. 3386:49 3384:. 3355:61 3353:. 3349:. 3337:^ 3327:. 3304:. 3254:. 3242:^ 3234:17 3232:. 3209:. 3197:. 3193:. 3178:^ 3164:. 3154:20 3152:. 3148:. 3133:^ 3110:11 3108:. 3104:. 3090:^ 3076:. 3066:. 3054:. 3050:. 3027:. 3019:. 3007:. 2982:35 2980:. 2976:. 2951:17 2949:. 2926:. 2916:11 2914:. 2876:. 2866:. 2854:. 2827:32 2825:. 2821:. 2796:. 2784:. 2741:. 2731:. 2719:; 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