Datasets:
mathieuschanz
/
Wiki12kTextSegmentation

Dataset card Data Studio Files Community
1
text
stringlengths
3
1.81k
So in this example, it could use R1 to ask the user if Socrates was a Man and then use that new information accordingly.
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 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.
As expert systems evolved, many new techniques were incorporated into various types of inference engines.
Some of the most important of these were:
***LIST***.
========,2,Advantages.
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 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 knowledge representation were rapid development and ease of maintenance.
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 (at least at the highest level) was simply a given for the system, simply invoke the inference engine.
This also was a reason for the second benefit: rapid prototyping.
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.
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 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.
========,2,Disadvantages.
The most common disadvantage cited for expert systems in the academic literature is the knowledge acquisition 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 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.
Performance was especially problematic because early expert systems were built using tools such as Lisp, which executed interpreted (rather than compiled) code.
Interpreting provided an extremely powerful development environment but with the drawback that it was virtually impossible to match the efficiency of the fastest compiled languages, such as C. 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 Lisp machines 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 COBOL 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 minicomputer servers provided the processing power needed for AI applications.
========,2,Applications.
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 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.
Hearsay was an early attempt at solving voice recognition through an expert systems approach.
For the most part this category or 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.
Other early examples were analyzing sonar data to detect Russian submarines.
These kinds of systems proved much more amenable to a neural network AI solution than a rule-based approach.
CADUCEUS and MYCIN 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.
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 Digital Equipment Corporation (DEC) VAX computers and mortgage loan application development.
SMH.PAL is an expert system for the assessment of students with multiple disabilities.
Mistral is an expert system to monitor dam safety, developed in the 90's 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 Ridracoli Dam (Italy), is still operational 24/7/365.
It has been installed on several dams in Italy and abroad (e.g., Itaipu Dam 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 CESI.
========,3,Applications as Bayesian networks.
Bayesian networks (BNs) are probabilistic graphical models, which are typically used to model cause and effect relationships, have become the most widely accepted technique for incorporating expert knowledge along with data.
Expert knowledge can be incorporated into BNs by either constructing the causal (or dependence) graph, or by incorporating factors into the causal network which are important for inference but which data fail to capture.
The popularity of BNs as expert systems has led to the development of countless prediction and decision support systems in industry, government and academia worldwide.
These systems typically incorporate both knowledge and data, and have been applied in the areas of, but not limited to, finance, engineering, sports, sports psychology, law, project management, marketing, medicine, energy, forensics, economics, property market, and defence.
========,1,preface.
In chemistry, an alcohol is any organic compound in which the hydroxyl functional group (–OH) is bound to a saturated carbon atom.
The term alcohol originally referred to the primary alcohol ethanol (ethyl alcohol), the predominant alcohol in alcoholic beverages.
The suffix "-ol" appears in the IUPAC chemical name of all substances where the hydroxyl group is the functional group with the highest priority; in substances where a higher priority group is present the prefix "hydroxy-" will appear in the International Union of Pure and Applied Chemistry (IUPAC) name.
The suffix "-ol" in non-systematic names (such as paracetamol or cholesterol) also typically indicates that the substance includes a hydroxyl functional group and, so, can be termed an alcohol.
But many substances, particularly sugars (examples glucose and sucrose) contain hydroxyl functional groups without using the suffix.
An important class of alcohols, of which methanol and ethanol are the simplest members is the saturated straight chain alcohols, the general formula for which is CHOH.
========,2,Nomenclature.
========,3,Etymology.
Muhammad ibn Zakariya al-Razi ( "Abūbakr Mohammad-e Zakariyyā-ye Rāzī", also known by his Latinized name Rhazes or Rasis) (854 CE – 925 CE), was a Persian polymath, physician, alchemist, philosopher who discovered numerous compounds and chemicals including "alcohol" by developing several chemical instruments and methods of distillation.
The word "alcohol" is from the Arabic "kohl" (), a powder used as an eyeliner.
Al- is the Arabic definite article, equivalent to "the" in English.
"Alcohol" was originally used for the very fine powder produced by the sublimation of the natural mineral stibnite to form antimony trisulfide , hence the essence or "spirit" of this substance.
It was used as an antiseptic, eyeliner, and cosmetic.
The meaning of alcohol was extended to distilled substances in general, and then narrowed to ethanol, when "spirits" as a synonym for hard liquor.
Bartholomew Traheron, in his 1543 translation of John of Vigo, introduces the word as a term used by "barbarous" (Moorish) authors for "fine powder."
Vigo wrote: "the barbarous auctours use alcohol, or (as I fynde it sometymes wryten) alcofoll, for moost fine poudre."
The 1657 "Lexicon Chymicum", by William Johnson glosses the word as "antimonium sive stibium."
By extension, the word came to refer to any fluid obtained by distillation, including "alcohol of wine," the distilled essence of wine.
Libavius in "Alchymia" (1594) refers to "vini alcohol vel vinum alcalisatum".
Johnson (1657) glosses "alcohol vini" as "quando omnis superfluitas vini a vino separatur, ita ut accensum ardeat donec totum consumatur, nihilque fæcum aut phlegmatis in fundo remaneat."
The word's meaning became restricted to "spirit of wine" (the chemical known today as ethanol) in the 18th century and was extended to the class of substances so-called as "alcohols" in modern chemistry after 1850.
The term "ethanol" was invented 1892, based on combining the word ethane with "ol" the last part of "alcohol".
========,3,Systematic names.
IUPAC nomenclature is used in scientific publications and where precise identification of the substance is important, especially in cases where the relative complexity of the molecule does not make such a systematic name unwieldy.
In the IUPAC system, in naming simple alcohols, the name of the alkane chain loses the terminal "e" and adds "ol", "e.g.
", as in "methanol" and "ethanol".
When necessary, the position of the hydroxyl group is indicated by a number between the alkane name and the "ol": propan-1-ol for , propan-2-ol for .
If a higher priority group is present (such as an aldehyde, ketone, or carboxylic acid), then the prefix "hydroxy" is used, e.g., as in 1-hydroxy-2-propanone ().
========,3,Common names.
In other less formal contexts, an alcohol is often called with the name of the corresponding alkyl group followed by the word "alcohol", e.g., methyl alcohol, ethyl alcohol.
Propyl alcohol may be "n"-propyl alcohol or isopropyl alcohol, depending on whether the hydroxyl group is bonded to the end or middle carbon on the straight propane chain.
As described under systematic naming, if another group on the molecule takes priority, the alcohol moiety is often indicated using the "hydroxy-" prefix.
Alcohols are then classified into primary, secondary ("sec-", "s-"), and tertiary ("tert-", "t-"), based upon the number of carbon atoms connected to the carbon atom that bears the hydroxyl functional group.
(The respective numeric shorthands 1°, 2°, and 3° are also sometimes used in informal settings.)
The primary alcohols have general formulas RCHOH.
The simplest primary alcohol is methanol (CHOH), for which R=H, and the next is ethanol, for which R=CH, the methyl group.
Secondary alcohols are those of the form RR'CHOH, the simplest of which is 2-propanol (R=R'=CH).
For the tertiary alcohols the general form is RR'R"COH.