Showing posts with label Expert System. Show all posts
Showing posts with label Expert System. Show all posts

AI Glossary - What Is ARTMAP?


    What Is ARTMAP AI Algorithm?

    The supervised learning variant of the ART-1 model is ARTMAP.

    It learns binary input patterns that are given to it.

    The suffix "MAP" is used in the names of numerous supervised ART algorithms, such as Fuzzy ARTMAP.

    Both the inputs and the targets are clustered in these algorithms, and the two sets of clusters are linked.

    The ARTMAP algorithms' fundamental flaw is that they lack a way to prevent overfitting, hence they should not be utilized with noisy data.

    How Does The ARTMAP Neural Network Work?

    A novel neural network architecture called ARTMAP automatically picks out recognition categories for any numbers of arbitrarily ordered vectors depending on the accuracy of predictions. 

    A pair of Adaptive Resonance Theory modules (ARTa and ARTb) that may self-organize stable recognition categories in response to random input pattern sequences make up this supervised learning system. 

    The ARTa module gets a stream of input patterns ([a(p)]) and the ARTb module receives a stream of input patterns ([b(p)]), where b(p) is the right prediction given a (p). 

    An internal controller and an associative learning network connect these ART components to provide real-time autonomous system functioning. 

    The remaining patterns a(p) are shown during test trials without b(p), and their predictions at ARTb are contrasted with b. (p). 

    The ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms when tested on a benchmark machine learning database in both on-line and off-line simulations, and achieves 100% accuracy after training on less than half the input patterns in the database. 

    It accomplishes these features by using an internal controller that, on a trial-by-trial basis, links predictive success to category size and simultaneously optimizes predictive generalization and reduces predictive error, using only local operations. 

    By the smallest amount required to rectify a predicted inaccuracy at ARTb, this calculation raises the alertness parameter an of ARTa. 

    To accept a category or hypothesis triggered by an input a(p), rather than seeking a better one via an autonomously controlled process of hypothesis testing, ARTa must have a minimal level of confidence, which is calibrated by the parameter a. 

    The degree of agreement between parameter a and the top-down learnt expectation, or prototype, which is read out after activating an ARTa category, is compared. 

    If the degree of match is less than a, search is initiated. 

    The self-organizing expert system known as ARTMAP adjusts the selectivity of its hypotheses depending on the accuracy of its predictions. 

    As a result, even if they are identical to frequent occurrences with distinct outcomes, unusual but significant events may be promptly and clearly differentiated. 

    In the intervals between input trials, a returns to baseline alertness. 

    When is big, the system operates in a cautious mode and only makes predictions when it is certain of the result. 

    At no step of learning, therefore, do many false-alarm mistakes happen, yet the system nonetheless achieves asymptote quickly. 

    Due to the self-stabilizing nature of ARTMAP learning, it may continue to learn one or more databases without deteriorating its corpus of memories until all available memory has been used.

    What Is Fuzzy ARTMAP?

    For incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analogue or binary input vectors, which may represent fuzzily or crisply defined sets of characteristics, a neural network architecture is developed. 

    By taking advantage of a close formal resemblance between the computations of fuzzy subsethood and ART category choosing, resonance, and learning, the architecture, dubbed fuzzy ARTMAP, accomplishes a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks. 

    In comparison to benchmark backpropagation and general algorithm systems, fuzzy ARTMAP performance was shown using four simulation classes. 

    A letter recognition database, learning to distinguish between two spirals, identifying locations inside and outside of a circle, and incremental approximation of a piecewise-continuous function are some of the simulations included in this list. 

    Additionally, the fuzzy ARTMAP system is contrasted with Simpson's FMMC system and Salzberg's NGE systems.

    ~ Jai Krishna Ponnappan

    Find Jai on Twitter | LinkedIn | Instagram

    References And Further Reading:

    • Moreira-Júnior, J.R., Abreu, T., Minussi, C.R. and Lopes, M.L., 2022. Using Aggregated Electrical Loads for the Multinodal Load Forecasting. Journal of Control, Automation and Electrical Systems, pp.1-9.
    • Ferreira, W.D.A.P., Grout, I. and da Silva, A.C.R., 2022, March. Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction. In 2022 International Electrical Engineering Congress (iEECON) (pp. 1-4). IEEE.
    • La Marca, A.F., Lopes, R.D.S., Lotufo, A.D.P., Bartholomeu, D.C. and Minussi, C.R., 2022. BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network. Sensors22(11), p.4027.
    • Santos-Junior, C.R., Abreu, T., Lopes, M.L. and Lotufo, A.D., 2021. A new approach to online training for the Fuzzy ARTMAP artificial neural network. Applied Soft Computing113, p.107936.
    • Ferreira, W.D.A.P., 2021. Rede neural ARTMAP fuzzy implementada em hardware aplicada na previsão da qualidade do ar em ambiente interno.

    AI Glossary - AI-QUIC.


    The underwriting division of American International Groups uses AI-QUIC, a rule-based program.

    It automates underwriting activities and is designed to respond fast to changes in underwriting regulations.

    Related Terms:

    Expert System.

    ~ Jai Krishna Ponnappan

    Find Jai on Twitter | LinkedIn | Instagram

    Be sure to refer to the complete & active AI Terms Glossary here.

    You may also want to read more about Artificial Intelligence here.

    AI Glossary - Advice Taker


    J. McCarthy suggested a software called Advice Taker to demonstrate common sense and improveable behavior.

    Declarative and imperative sentences were used to express the software.

    It reasoned through deductive reasoning.

    This method was a predecessor to McCarthy and Hayes' Situational Calculus, which they proposed in a 1969 paper in Machine Intelligence.

    ~ Jai Krishna Ponnappan

    Find Jai on Twitter | LinkedIn | Instagram

    Be sure to refer to the complete & active AI Terms Glossary here.

    You may also want to read more about Artificial Intelligence here.

    Artificial Intelligence - What Is The MOLGEN Expert System?


    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.

    ~ Jai Krishna Ponnappan

    Find Jai on Twitter | LinkedIn | Instagram

    You may also want to read more about Artificial Intelligence here.

    ~ Jai Krishna Ponnappan

    Find Jai on Twitter | LinkedIn | Instagram

    You may also want to read more about Artificial Intelligence here.

    See also: 

    DENDRAL; Expert Systems; Knowledge Engineering.

    References & Further Reading:

    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.

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