An agenda or job-list controls the inference process in Agenda Based Systems.
It deconstructs the system into discrete, modular stages.
Each entry in the work list, or task, represents a
particular task to be completed throughout the problem-solving process.
AM, DENDRAL.
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He is largely regarded as one of the twentieth century's
most prominent social scientists.
His contributions at Carnegie Mellon University lasted five
decades.
Early artificial intelligence research was driven by the
idea of the computer as a symbol manipulator rather than a number cruncher.
Emil Post, who originally wrote about this sort of
computational model in 1943, is credited with inventing production systems,
which included sets of rules for symbol strings used to establish
conditions—which must exist before rules can be applied—and the actions to be
done or conclusions to be drawn.
Simon and his Carnegie Mellon colleague Allen Newell
popularized these theories regarding symbol manipulation and production systems
by praising their potential benefits for general-purpose reading, storing, and
replicating, as well as comparing and contrasting various symbols and patterns.
Theorems presented in Bertrand Russell and Alfred North
Whitehead's Principia Mathematica (1910) might be independently proved by logic
theorists.
Perhaps most notably, the Logic Theorist program uncovered a
shorter, more elegant demonstration of Theorem 2.85 in the Principia
Mathematica, which was subsequently rejected by the Journal of Symbolic Logic
since it was coauthored by a machine.
Although it was theoretically conceivable to prove the
Principia Mathematica's theorems in an exhaustively detailed and methodical
manner, it was impractical in reality due to the time required.
Newell and Simon were fascinated by the human rules of thumb
for solving difficult issues for which an extensive search for answers was
impossible due to the massive quantities of processing necessary.
They used the term "heuristics" to describe
procedures that may solve issues but do not guarantee success.
A heuristic is a "rule of thumb" used to solve a
problem that is too difficult or time consuming to address using an exhaustive
search, a formula, or a step-by-step method.
Heuristic approaches are often compared with algorithmic
methods in computer science, with the result of the method being a significant
differentiating element.
According to this contrast, a heuristic program will provide
excellent results in most cases, but not always, while an algorithmic program
is a clear technique that guarantees a solution.
This is not, however, a technical difference.
In fact, a heuristic procedure that consistently yields the
best result may no longer be deemed "heuristic"—alpha-beta pruning is
an example of this.
Simon's heuristics are still utilized by programmers who are
trying to solve issues that demand a lot of time and/or memory.
The game of chess is one such example, in which an
exhaustive search of all potential board configurations for the proper solution
is beyond the human mind's or any computer's capabilities.
Heuristics may also be used to solve issues that don't have
a precise answer, such as in medical diagnosis, when heuristics are applied to
a collection of symptoms to determine the most probable diagnosis.
Production rules are derived from a class of cognitive
science models that apply heuristic principles to productions (situations).
In practice, these rules reduce down to "IF-THEN"
statements that reflect specific preconditions or antecedents, as well as the
conclusions or consequences that these preconditions or antecedents justify.
"IF there are two X's in a row, THEN put an O to
block," is a frequent example offered for the application of production
rules to the tic-tac-toe game.
These IF-THEN statements are incorporated into expert
systems' inference mechanisms so that a rule interpreter can apply production
rules to specific situations lodged in the context data structure or short-term
working memory buffer containing information supplied about that situation and
draw conclusions or make recommendations.
Joshua Lederberg, Edward Feigenbaum, and other Stanford
University partners would later use this fundamental finding to develop
DENDRAL, an expert system for detecting molecular structure, in the 1960s.
These production guidelines were developed in DENDRAL after
discussions between the system's developers and other mass spectrometry
specialists.
Edward Shortliffe, Bruce Buchanan, and Edward Feigenbaum
used production principles to create MYCIN in the 1970s.
MYCIN has over 600 IFTHEN statements in it, each reflecting
domain-specific knowledge about microbial illness diagnosis and treatment.
PUFF, EXPERT, PROSPECTOR, R1, and CLAVIER were among the
several production rule systems that followed.
A process for solving issues in a restricted, clear sequence
of steps is known as an algorithm.
Sequential operations, conditional operations, and iterative
operations are the three kinds of fundamental instructions required to create
computable algorithms.
Sequential operations perform tasks in a step-by-step
manner.
The algorithm only moves on to the next job when each step
is completed.
Conditional operations are made up of instructions that ask
questions and then choose the next step dependent on the response.
One kind of conditional operation is the "IF-THEN"
expression.
Iterative operations run "loops" of instructions.
These statements tell the task flow to go back and repeat a
previous series of statements in order to solve an issue.
Algorithms are often compared to cookbook recipes, in which
a certain order and execution of actions in the manufacture of a product—in
this example, food—are dictated by a specific sequence of set instructions.
List processing is a programming technique for allocating
dynamic storage.
It's mostly utilized in symbol manipulation computer
applications like compiler development, visual or linguistic data processing,
and artificial intelligence, among others.
Allen Newell, J. Clifford Shaw, and Herbert A. Simon are credited with creating the first list processing software with enormous, sophisticated, and flexible memory structures that were not reliant on subsequent computer/machine memory.
List processing techniques are used in a number of
higher-order languages.
IPL and LISP, two artificial intelligence languages, are the
most well-known.
Simon and Newell's Generic Problem Solver (GPS) was
published in the early 1960s, and it thoroughly explains the essential
properties of symbol manipulation as a general process that underpins all types
of intelligent problem-solving behavior.
GPS formed the foundation for decades of early AI research.
To arrive at a solution, General Problem Solver is a
software for a problem-solving method that employs means-ends analysis and
planning.
GPS was created with the goal of separating the
problem-solving process from information particular to the situation at hand,
allowing it to be used to a wide range of issues.
Simon is an economist, a political scientist, and a
cognitive psychologist.
Simon is known for the notions of limited rationality,
satisficing, and power law distributions in complex systems, in addition to his
important contributions to organizational theory, decision-making, and
problem-solving.
Computer and data scientists are interested in all three
themes.
Human reasoning is inherently constrained, according to
bounded rationality.
Humans lack the time or knowledge required to make ideal
judgments; problems are difficult, and the mind has cognitive limitations.
Satisficing is a term used to describe a decision-making
process that produces a solution that "satisfies" and
"suffices," rather than the most ideal answer.
Customers use satisficing in market conditions when they
choose things that are "good enough," meaning sufficient or
acceptable.
When a relative change in one variable induces a
proportionate change in another, power laws, also known as scaling laws, come
into play.
A square is a simple illustration; when the length of a side
doubles, the square's area quadruples.
Power laws may be found in biological systems, fractal
patterns, and wealth distributions, among other things.
Preferential attachment processes explain why the affluent
grow wealthier in income/wealth distributions: Individuals' wealth is dispersed
according on their current level of wealth; those with more wealth get
proportionately more income, and hence greater overall wealth, than those with
less.
When graphed, such distributions often create so-called long
tails.
These long-tailed distributions are being employed to
explain crowdsourcing, microfinance, and online marketing, among other things.
His mother was a musical prodigy. Simon grew interested in the social sciences after reading books on psychology and economics written by an uncle.
He has said that two works inspired his early thinking on
the subjects: Norman Angell's The Great Illusion (1909) and Henry George's
Progress and Poverty (1879).
Rudolf Carnap, Harold Lasswell, Edward Merriam, Nicolas
Rashevsky, and Henry Schultz were among his instructors.
He started his career as a political science professor at
the Illinois Institute of Technology, where he taught and conducted research.
In 1949, he transferred to Carnegie Mellon University, where
he stayed until 2001.
He progressed through the ranks of the Department of
Industrial Management to become its chair.
He has written twenty-seven books and several articles that
have been published.
In 1959, he was elected a member of the American Academy of
Arts and Sciences.
In 1975, Simon was awarded the coveted Turing Award, and in
1978, he was awarded the Nobel Prize in Economics.
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Dartmouth AI Conference; Expert Systems; General Problem Solver; Newell, Allen.
Crowther-Heyck, Hunter. 2005. Herbert A. Simon: The Bounds of Reason in Modern America. Baltimore: Johns Hopkins Press.
Newell, Allen, and Herbert A. Simon. 1956. The Logic Theory Machine: A Complex Information Processing System. Santa Monica, CA: The RAND Corporation.
Newell, Allen, and Herbert A. Simon. 1976. “Computer Science as Empirical Inquiry: Symbols and Search.” Communications of the ACM 19, no. 3: 113–26.
Simon, Herbert A. 1996. Models of My Life. Cambridge, MA: MIT Press.
MYCIN is an
interactive expert system for infectious illness diagnosis and treatment
developed by computer scientists Edward Feigenbaum (1936–) and Bruce Buchanan
at Stanford University in the 1970s.
MYCIN was Feigenbaum's second expert system (after DENDRAL),
but it was the first to be commercially accessible as a standalone software
package.
TeKnowledge, the software business cofounded by Feigenbaum
and other partners, offered EMYCIN as the most successful expert shell for this
purpose by the 1980s.
MYCIN was developed by Feigenbaum's Heuristic Programming
Project (HPP) in collaboration with Stanford Medical School's Infectious
Diseases Group (IDG).
The expert clinical physician was IDG's Stanley Cohen.
Feigenbaum and Buchanan had read stories of antibiotics
being prescribed wrongly owing to misdiagnoses in the early 1970s.
MYCIN was created to assist a human expert in making the
best judgment possible.
MYCIN started out as a consultation tool.
MYCIN supplied a diagnosis that included the necessary
antibiotics and dose after inputting the results of a patient's blood test,
bacterial cultures, and other data.
MYCIN also served as an explanation system.
In simple English, the physician-user may ask MYCIN to
expound on a certain inference.
Finally, MYCIN had a knowledge-acquisition software that was
used to keep the system's knowledge base up to date.
Feigenbaum and his collaborators introduced two additional
features to MYCIN after gaining experience with DENDRAL.
MYCIN's inference engine comes with a rule interpreter to
begin with.
This enabled "goal-directed backward chaining" to
be used to achieve diagnostic findings (Cendrowska and Bramer 1984, 229).
MYCIN set itself the objective of discovering a useful
clinical parameter that matched the patient data submitted at each phase in the
procedure.
The inference engine looked for a set of rules that applied
to the parameter in question.
MYCIN typically required more information when evaluating
the premise of one of the rules in this parameter set.
The system's next subgoal was to get that data.
MYCIN might test out new regulations or ask the physician
for further information.
This process was repeated until MYCIN had enough data on
numerous factors to make a diagnosis.
The certainty factor was MYCIN's second unique feature.
These elements should not be seen "as conditional
probabilities, [though] they are loosely grounded on probability theory,"
according to William van Melle (then a doctoral student working on MYCIN for
his thesis project) (van Melle 1978, 314).
The execution of production rules was assigned a value
between –1 and +1 by MYCIN (dependent on how strongly the system felt about
their correctness).
MYCIN's diagnosis also included these certainty elements,
allowing the physician-user to make their own final decision.
The software package, known as EMYCIN, was released in 1976
and comprised an inference engine, user interface, and short-term memory.
It didn't have any information.
("E" stood for "Empty" at first, then
"Essential.") Customers of EMYCIN were required to link their own
knowledge base to the system.
Faced with high demand for EMYCIN packages and high interest
in MOLGEN (Feigenbaum's third expert system), HPP decided to form IntelliCorp
and TeKnowledge, the first two expert system firms.
TeKnowledge was eventually founded by a group of roughly
twenty individuals, including all of the previous HPP students who had
developed expert systems.
EMYCIN was and continues to be their most popular product.
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Expert Systems; Knowledge Engineering
Cendrowska, J., and M. A. Bramer. 1984. “A Rational Reconstruction of the MYCIN Consultation System.” International Journal of Man-Machine Studies 20 (March): 229–317.
Crevier, Daniel. 1993. AI: Tumultuous History of the Search for Artificial Intelligence. Princeton, NJ: Princeton University Press.
Feigenbaum, Edward. 2000. “Oral History.” Charles Babbage Institute, October 13, 2000. van Melle, William. 1978. “MYCIN: A Knowledge-based Consultation Program for Infectious Disease Diagnosis.” International Journal of Man-Machine Studies 10 (May): 313–22.
MOLGEN is an expert
system that helped molecular scientists and geneticists plan studies between
1975 and 1980.
It was Edward Feigenbaum's Heuristic Programming Project
(HPP) at Stanford University's third expert system (after DENDRAL and MYCIN).
MOLGEN, like MYCIN before it, attracted hundreds of users
outside of Stanford.
MOLGEN was originally made accessible to artificial
intelligence researchers, molecular biologists, and geneticists via
time-sharing on the GENET network in the 1980s.
Feigenbaum founded IntelliCorp in the late 1980s to offer a
stand-alone software version of MOLGEN.
Scientific advancements in chromosomes and genes sparked an
information boom in the early 1970s.
In 1971, Stanford University scientist Paul Berg performed
the first gene splicing studies.
Stanford geneticist Stanley Cohen and University of
California at San Francisco biochemist Herbert Boyer succeeded in inserting
recombinant DNA into an organ ism two years later; the host organism (a
bacterium) subsequently spontaneously replicated the foreign rDNA structure in
its progeny.
Because of these developments, Stanford molecular researcher
Joshua Lederberg told Feigenbaum that now was the right time to construct an
expert system in Lederberg's expertise of molecular biology.
(Lederberg and Feigenbaum previously collaborated on DENDRAL,
the first expert system.) MOLGEN could accomplish for recombinant DNA research
and genetic engineering what DENDRAL had done for mass spectrometry, the two
agreed.
Both expert systems were created with developing scientific
topics in mind.
This enabled MOL GEN (and DENRAL) to absorb the most
up-to-date scientific information and contribute to the advancement of their
respective fields.
Mark Stefik and Peter Friedland developed programs for
MOLGEN as their thesis project at HPP, and Feigenbaum was the primary
investigator.
MOLGEN was supposed to follow a "skeletal
blueprint" (Friedland and Iwasaki 1985, 161).
MOLGEN prepared a new experiment in the manner of a human
expert, beginning with a design approach that had previously proven effective
for a comparable issue.
MOLGEN then made hierarchical, step-by-step changes to the
plan.
The algorithm was able to choose the most promising new
experiments because to the combination of skeleton blueprints and MOLGEN's
enormous knowledge base in molecular biology.
MOLGEN contained 300 lab procedures and strategies, as well
as current data on forty genes, phages, plasmids, and nucleic acid structures,
by 1980.
Fried reich and Stefik presented MOLGEN with a set of
algorithms based on the molecular biology knowledge of Stanford University's
Douglas Brutlag, Larry Kedes, John Sninsky, and Rosalind Grymes.
SEQ (for nucleic acid sequence analysis), GA1 (for
generating enzyme maps of DNA structures), and SAFE were among them (for
selecting enzymes most suit able for gene excision).
Beginning in February 1980, MOLGEN was made available to the
molecular biology community outside of Stanford.
Under an account named GENET, the system was linked to SUMEX
AIM (Stanford University Medical Experimental Computer for Artificial
Intelligence in Medicine).
GENET was able to swiftly locate hundreds of users around
the United States.
Academic scholars, experts from commercial giants like
Monsanto, and researchers from modest start-ups like Genentech were among the
frequent visitors.
The National Institutes of Health (NIH), which was SUMEX
AIM's primary supporter, finally concluded that business customers could not
have unfettered access to cutting-edge technology produced with public funds.
Instead, the National Institutes of Health encouraged
Feigenbaum, Brutlag, Kedes, and Friedland to form IntelliGenetics, a company
that caters to business biotech customers.
IntelliGenetics created BIONET with the support of a $5.6
million NIH grant over five years to sell or rent MOLGEN and other GENET
applications.
For a $400 yearly charge, 900 labs throughout the globe had
access to BIONET by the end of the 1980s.
Companies who did not wish to put their data on BIONET might
purchase a software package from IntelliGenetics.
Until the mid-1980s, when IntelliGenetics withdrew its
genetics material and maintained solely its underlying Knowledge Engineering
Environment, MOLGEN's software did not sell well as a stand-alone product
(KEE).
IntelliGenetics' AI division, which marketed the new KEE
shell, changed its name to IntelliCorp.
Two more public offerings followed, but growth finally
slowed.
MOLGEN's shell's commercial success, according to
Feigenbaum, was hampered by its LISP-language; although LISP was chosen by
pioneering computer scientists working on mainframe computers, it did not
inspire the same level of interest in the corporate minicomputer sector.
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DENDRAL; Expert Systems; Knowledge Engineering.
Feigenbaum, Edward. 2000. Oral History. Minneapolis, MN: Charles Babbage Institute.
Friedland, Peter E., and Yumi Iwasaki. 1985. “The Concept and Implementation of Skeletal Plans.” Journal of Automated Reasoning 1: 161–208.
Friedland, Peter E., and Laurence H. Kedes. 1985. “Discovering the Secrets of DNA.” Communications of the ACM 28 (November): 1164–85.
Lenoir, Timothy. 1998. “Shaping Biomedicine as an Information Science.” In Proceedings of the 1998 Conference on the History and Heritage of Science Information Systems, edited by Mary Ellen Bowden, Trudi Bellardo Hahn, and Robert V. Williams, 27–46. Pittsburgh, PA: Conference on the History and Heritage of Science Information Systems.
Watt, Peggy. 1984. “Biologists Map Genes On-Line.” InfoWorld 6, no. 19 (May 7): 43–45.
Expert systems are used to solve issues that would normally be addressed by humans.
In the early decades of artificial intelligence research,
they emerged as one of the most promising application strategies.
The core concept is to convert an expert's knowledge into a
computer-based knowledge system.
• They make decisions based on knowledge rather than facts.
• The task of representing heuristic knowledge in expert systems is daunting.
• Knowledge and the program are generally separated so that the same program can operate on different knowledge bases.
• Expert systems should be able to explain their decisions, represent knowledge symbolically, and have and use meta knowledge, that is, knowledge about knowledge.
The subject of medical research was a frequent test
application for expert systems.
Expert systems were created as a tool to assist medical doctors
in their work.
Symptoms were usually communicated by the patient in the
form of replies to inquiries.
Based on its knowledge base, the system would next attempt
to identify the ailment and, in certain cases, recommend relevant remedies.
MYCIN, a Stanford University-developed expert system for
detecting bacterial infections and blood disorders, is one example.
Another well-known application in the realm of engineering
and engineering design tries to capture the heuristic knowledge of the design
process in the design of motors and generators.
The expert system assists in the initial design phase, when
choices like as the number of poles, whether to use AC or DC, and so on are
made (Hoole et al. 2003).
The knowledge base and the inference engine are the two
components that make up the core framework of expert systems.
The inference engine utilizes the knowledge base to make
choices, whereas the knowledge base holds the expert's expertise.
In this way, the knowledge is isolated from the software that
manipulates it.
Knowledge must first be gathered, then comprehended, categorized,
and stored in order to create expert systems.
It is retrieved to answer issues depending on predetermined
criteria.
The four main processes in the design of an expert system,
according to Thomson Reuters chief scientist Peter Jackson, are obtaining
information, representing that knowledge, directing reasoning via an inference
engine, and explaining the expert system's answer (Jackson 1999).
The expert system's largest issue was acquiring domain
knowledge.
Human specialists may be challenging to obtain information
from.
The knowledge acquisition process is divided into five
phases, according to Hayes-Roth et al. (1983).
Identification, or recognizing the problem and the data that
must be used to arrive at a solution; conceptualization, or comprehending the
key concepts and relationships between the data; formalization, or
comprehending the relevant search space; implementation, or converting
formalized knowledge into a software program; and testing the rules for
completeness and accuracy are among them.
Nodes make form an associative/semantic network, which may be used to represent hierarchical knowledge.
Frames are structured sets of closely related knowledge in
frame architectures.
Top-down knowledge is represented via decision tree
structures.
A blackboard system architecture may be seen in DARPA's
HEARSAY domain independent expert system.
There are three options for manually constructing the
knowledge base.
DENDRAL was created in order to study the molecular
structure of organic molecules.
A shell provides a platform for knowledge to be programmed
into the system.
Many efforts have been made to blend disparate paradigms to
create hybrid systems.
Although other languages, such as Visual Prolog, have merged
object-oriented programming, OI-KSL takes a different approach.
Backtracking in Visual Prolog occurs inside the objects;
that is, the methods backtracked.
Backtracking is taken to a whole new level in OI KSL, with
the item itself being backtracked.
To cope with uncertainties in the given data, probability
theory, heuristics, and fuzzy logic are sometimes utilized.
A fuzzy electric lighting system was one example of a Prolog
implementation of fuzzy logic, in which the quantity of natural light
influenced the voltage that flowed to the electric bulb (Mascrenghe 2002).
This allowed the system to reason in the face of uncertainty
and with little data.
Interest in expert systems started to wane in the late
1990s, owing in part to unrealistic expectations for the technology and the
expensive cost of upkeep.
Expert systems were unable to deliver on their promises.
Even today, technology generated in expert systems research
is used in various fields like data science, chatbots, and machine intelligence.
You may also want to read more about Artificial Intelligence here.
Clinical Decision Support Systems; Computer-Assisted Diagnosis; DENDRAL; Expert Systems.
Hayes-Roth, Frederick, Donald A. Waterman, and Douglas B. Lenat, eds. 1983. Building Expert Systems. Teknowledge Series in Knowledge Engineering, vol. 1. Reading, MA: Addison Wesley.
Hoole, S. R. H., A. Mascrenghe, K. Navukkarasu, and K. Sivasubramaniam. 2003. “An Expert Design Environment for Electrical Devices and Its Engineering Assistant.” IEEE Transactions on Magnetics 39, no. 3 (May): 1693–96.
Jackson, Peter. 1999. Introduction to Expert Systems. Third edition. Reading, MA: Addison-Wesley.
Mascrenghe, A. 2002. “The Fuzzy Electric Bulb: An Introduction to Fuzzy Logic with Sample Implementation.” PC AI 16, no. 4 (July–August): 33–37.
Mascrenghe, A., S. R. H. Hoole, and K. Navukkarasu. 2002. “Prototype for a New Electromagnetic Knowledge Specification Language.” In CEFC Digest. Perugia, Italy: IEEE.
Patterson, Dan W. 2008. Introduction to Artificial Intelligence and Expert Systems. New Delhi, India: PHI Learning.
Rich, Elaine, Kevin Knight, and Shivashankar B. Nair. 2009. Artificial Intelligence. New Delhi, India: Tata McGraw-Hill.
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