Agent CLIPS is a
CLIPS plugin that enables you to create intelligent agents that can interact
with one other on a single system or over the Internet.
CLIPS.
http://users.aimnet.com/~yilsoft/softwares/agentclips/agentclips.html
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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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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.
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Advanced Scout is a customized system built by IBM in the mid-1990s that organizes and interprets data from basketball games using Data Mining methods.
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ART stands for "Advanced Reasoning Tool."
The
Advanced Reasoning Tool (ART) is a knowledge engineering language built on the
LISP programming language.
It is a rule-based system that also allows for the modeling
of frames and procedures.
Inference Corporation was responsible for its creation.
The same term (ART) is also used to refer to Adaptive
Resonance Theory-based approaches.
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Adjacency Matrix is an acronym for "adjacency matrix."
A binary relation over a finite set may be represented using an adjacency matrix.
If the cardinality of set A is n, the adjacency matrix for a
relation on A is a nxn binary matrix, with a one for the I j-th element if the
connection holds, and a zero otherwise.
The adjacency matrix is used by a variety of route and
closure algorithms, either implicitly or explicitly.
If there are ones along the major diagonal, an adjacency
matrix is reflexive, and symmetric if the I j-th element equals the j, i-th
element for all I j pairings in the matrix.
The weighted adjacency matrix, which substitutes the zeros
and ones with and costs, respectively, and utilizes this matrix to determine
the shortest distance or minimal cost pathways between the components, is a
generalization of this.
Floyd's Shortest Distance Algorithm, path matrix
ADE Monitor is a CLIPS-based expert system that analyzes
patient data for signs of an adverse drug response.
The technology will allow doctors to make changes and will
be able to alert the proper authorities as necessary.
C Language Integrated Production System (CLIPS)
http://www-uk.hpl.hp.com/people/ewc/list-main.html.
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AVAS stands for
Additivity and Variance Stabilization and is a smooth regression model version
of the ACE approach.
It incorporates a variance stabilizing transform into the
ACE approach, removing many of ACE's problems with smooth connection
estimation.
ACE
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A modeling approach
that predicts the output variable using weighted linear sums of the presumably
altered input variables, but excludes phrases like cross-products that are
dependent on more than one predictor variable.
Boosting and Generalized Additive Models are two examples of
machine learning methods that employ additive models (GAMs).
Boosting, Generalized Additive Models.
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Adaptive Vector Quantization is a neural network technique in which the input vectors form a state space and the network quantizes those vectors into a smaller number of ideal vectors or regions.
The network adapts the placement (and quantity) of these
vectors to the data as it "learns."
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Adaptive Resonance Theory (ART) is a kind of neural network
based on neurophysiologic theories.
Stephen Grossberg came up with the idea in 1976.
For prediction, ART models use a hidden layer of ideal
instances.
If an input case is sufficiently similar to an existing
case, it "resonates" with it, and the ideal case is modified to
include it.
A new ideal scenario is introduced if this is not the case.
ARTs are sometimes shown as having two layers, known as the
F1 and F2 layers.
The matching is done by the F1 layer, and the outcome is
chosen by the F2 layer.
It's a cluster analysis technique.
http://www.wi.leidenuniv.nl/art/
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Aaron Sloman (1936–) is a renowned artificial intelligence and cognitive science philosopher.
He is a global expert in the evolution of biological
information processing, an area of study that seeks to understand how animal
species have acquired cognitive levels that surpass technology.
He's been debating if evolution was the first blind
mathematician and whether weaver birds are actually capable of recursion in
recent years (dividing a problem into parts to conquer it).
His present Meta-Morphogenesis Project is based on an idea
by Alan Turing (1912–1954), who claimed that although computers could do
mathematical brilliance, only brains could perform mathematical intuition.
According to Sloman, not every aspect of the cosmos,
including the human brain, can be represented in a sufficiently massive digital
computer because of this.
This assertion clearly contradicts digital physics, which
claims that the universe may be characterized as a simulation running on a
sufficiently big and fast general-purpose computer that calculates the cosmos's
development.
Sloman proposes that the universe has developed its own
biological building kits for creating and deriving other—different and more
sophisticated—construction kits, similar to how scientists have evolved,
accumulated, and applied increasingly complex mathematical knowledge via
mathematics.
He refers to this concept as the Self-Informing Universe,
and suggests that scientists build a multi-membrane Super-Turing machine that
runs on subneural biological chemistry.
Sloman was born to Jewish Lithuanian immigrants in Southern
Rhodesia (now Zimbabwe).
At the University of Cape Town, he got a bachelor's degree
in Mathematics and Physics.
He was awarded a Rhodes Scholarship and earned his PhD in
philosophy from Oxford University, where he defended Immanuel Kant's
mathematical concepts.
He saw that artificial intelligence had promise as the way
forward in philosophical understanding of the mind as a visiting scholar at
Edinburgh University in the early 1970s.
He said that using Kant's recommendations as a starting
point, a workable robotic toy baby could be created, which would eventually
develop in intellect and become a mathematician on par with Archimedes or Zeno.
He was one of the first scholars to refute John McCarthy's
claim that a computer program capable of operating intelligently in the real
world must use structured, logic-based ideas.
Sloman was one of the founding members of the University of
Sussex School of Cognitive and Computer Sciences.
There, he collaborated with Margaret Boden and Max Clowes to
advance artificial intelligence instruction and research.
This effort resulted in the commercialization of the widely
used Poplog AI teaching system.
Sloman's The Computer Revolution in Philosophy (1978) is
famous for being one of the first to recognize that metaphors from the realm of
computers (for example, the brain as a data storage device and thinking as a
collection of tools) will dramatically alter how we think about ourselves.
The epilogue of the book contains observations on the near
impossibility of AI sparking the Singularity and the likelihood of a human
Society for the Liberation of Robots to address possible future brutal
treatment of intelligent machines.
Sloman held the Artificial Intelligence and Cognitive
Science chair in the School of Computer Science at the University of Birmingham
until his formal retirement in 2002.
He is a member of the Alan Turing Institute and the Association for the Advancement of Artificial Intelligence.
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Superintelligence; Turing, Alan.
Sloman, Aaron. 1962. “Knowing and Understanding: Relations Between Meaning and Truth, Meaning and Necessary Truth, Meaning and Synthetic Necessary Truth.” D. Phil., Oxford University.
Sloman, Aaron. 1971. “Interactions between Philosophy and AI: The Role of Intuition and Non-Logical Reasoning in Intelligence.” Artificial Intelligence 2: 209–25.
Sloman, Aaron. 1978. The Computer Revolution in Philosophy: Philosophy, Science, and Models of Mind. Terrace, Hassocks, Sussex, UK: Harvester Press.
Sloman, Aaron. 1990. “Notes on Consciousness.” AISB Quarterly 72: 8–14.
Sloman, Aaron. 2018. “Can Digital Computers Support Ancient Mathematical Consciousness?” Information 9, no. 5: 111.
A fuzzy associative
memory that can adapt to changing input over time.
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A term used to
describe systems that can learn or adjust from data in use, such as neural
networks or other dynamic control systems.
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ADABOOST.MH is a
multi-class and multi-label data extension of the ADABOOST algorithm.
See Also:
multi-class, multi-label.
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ADABOOST is a way for
enhancing machine learning methods that was recently created.
It has the potential to greatly enhance the performance of
classification methods (e.g., decision trees).
It works by repeatedly applying the procedure to the data,
analyzing the findings, and then reweighting the observations to provide more
weight to the misclassified instances.
By a majority vote of the individual classifiers, the final
classifier employs all of the intermediate classifiers to categorize an
observation.
It also has the intriguing virtue of continuing to lower the
generalization error (i.e., the error in a test set) long after the training
set error has stopped dropping or hit 0.
See Also:
arcing, Bootstrap AGGregation (bagging)
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APACHE is a system
that predicts a person's likelihood of dying in a hospital.
The algorithm employs 27 factors to predict a patient's fate
and is based on a big collection of case data.
It may also be used to assess the impact of a suggested or
implemented treatment strategy.
Further Reading:
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Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the ...
