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ARTIFICIAL INTELLIGENCE
Introduction.
Intelligence in general.
Quite simple human behaviour can be intelligent yet quite complex behaviour
performed by insects is unintelligent. What is the difference? Consider the behaviour of the
digger wasp, Sphex ichneumoneus. When the female wasp brings food to her burrow, she
deposits it on the threshold, goes inside the burrow to check for intruders, and then if the
coast is clear carries in the food. The unintelligent nature of the wasp's behaviour is
revealed if the watching experimenter moves the food a few inches while the wasp is inside
the burrow checking. On emerging, the wasp repeats the whole procedure: she carries the
food to the threshold once again, goes in to look around, and emerges. She can be made to
repeat this cycle of behaviour upwards of forty times in succession. Intelligence-conspicuously absent in the case of Sphex--is the ability to adapt one's behaviour to fit new
circumstances.
Mainstream thinking in psychology regards human intelligence not as a single ability
or cognitive process but rather as an array of separate components. Research in AI has
focussed chiefly on the following components of intelligence: learning, reasoning, problemsolving, perception, and language-understanding.
What is Intelligence ?
There are probably as many definitions of intelligence as there are experts who study it.
Simply put, however, intelligence is the ability to learn about, learn from, understand, and interact
with one’s environment. This general ability consists of a number of specific abilities, which include
these specific abilities:
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Adaptability to a new environment or to changes in the current environment
Capacity for knowledge and the ability to acquire it
Capacity for reason and abstract thought
Ability to comprehend relationships
Ability to evaluate and judge
Capacity for original and productive thought
Additional specific abilities might be added to the list, but they would all be abilities allowing
a person to learn about, learn from, understand, and interact with the environment. Environment in
this definition doesn’t mean the environment of the earth, such as the desert, the mountains, etc.,
although it can mean that kind of environment. It has a wider meaning that includes a person’s
immediate surroundings, including the people around him or her. Environment in this case can also
be something as small as a family, the workplace, or a classroom.
Artificial Intelligence I.A.
Artificial Intelligence (AI) is usually defined as the science of making computers do things
that require intelligence when done by humans. AI has had some success in limited, or simplified,
domains. However, the five decades since the inception of AI have brought only very slow progress,
and early optimism concerning the attainment of human-level intelligence has given way to an
appreciation of the profound difficulty of the problem
When it comes to making complex judgement calls, computers can’t replace people. But
with artificial intelligence, computers could be trained to think like humans do. Artificial intelligence
allows computers to learn from experience, recognize patterns in large amounts of complex data
and make complex decisions based on human knowledge and reasoning skills. Artificial intelligence
has become an important field of study with a wide spread of applications in fields ranging from
medicine to agriculture.
Historical.
TURING is test.
The "standard interpretation" of the Turing Test, in which player C, the interrogator, is
tasked with trying to determine which player - A or B - is a computer and which is a human. The
interrogator is limited to using the responses to written questions in order to make the
determination.
The Turing test is a proposal for a test of a machine's ability to demonstrate intelligence. It
proceeds as follows: a human judge engages in a natural language conversation with one human and
one machine, each of which tries to appear human. All participants are placed in isolated locations. If
the judge cannot reliably tell the machine from the human, the machine is said to have passed the
test. In order to test the machine's intelligence rather than its ability to render words into audio, the
conversation is limited to a text-only channel such as a computer keyboard and screen.
It was described by Alan Turing in his 1950 paper "Computing Machinery and Intelligence,"
in which Turing considers the question "can machines think?" Since "thinking" is difficult to define,
Turing chose to "replace the question by another which is closely related to it and is expressed in
relatively unambiguous words." Turing's new question is: "Are there imaginable digital computers
which would do well in the [Turing test]"? This question, Turing believed, is one that can actually be
answered. In the remainder of the paper, he argued against all the major objections to this
proposition.
In the years since 1950, the test has proven to be both highly influential and widely
criticized, and it is an essential concept in the philosophy of artificial intelligence
Expert system.
An expert system is software that attempts to reproduce the performance of one or more
human experts, most commonly in a specific problem domain, and is a traditional application and/or
subfield of artificial intelligence. A wide variety of methods can be used to simulate the performance
of the expert however common to most or all are 1) the creation of a so-called "knowledgebase"
which uses some knowledge representation formalism to capture the Subject Matter Experts (SME)
knowledge and 2) a process of gathering that knowledge from the SME and codifying it according to
the formalism, which is called knowledge engineering. Expert systems may or may not have learning
components but a third common element is that once the system is developed it is proven by being
placed in the same real world problem solving situation as the human SME, typically as an aid to
human workers or a supplement to some information system.
As a premiere application of computing and artificial intelligence, the topic of expert systems
has many points of contact with general systems theory, operations research, business process
reengineering and various topics in applied mathematics and management science.
The following general points about expert systems and their architecture have been
illustrated.
1. The sequence of steps taken to reach a conclusion is dynamically synthesized with each
new case. It is not explicitly programmed when the system is built.
2. Expert systems can process multiple values for any problem parameter. This permits more
than one line of reasoning to be pursued and the results of incomplete (not fully
determined) reasoning to be presented.
3. Problem solving is accomplished by applying specific knowledge rather than specific
technique. This is a key idea in expert systems technology. It reflects the belief that human
experts do not process their knowledge differently from others, but they do possess
different knowledge. With this philosophy, when one finds that their expert system does not
produce the desired results, work begins to expand the knowledge base, not to re-program
the procedures.
There are various expert systems in which a rulebase and an inference engine cooperate to
simulate the reasoning process that a human expert pursues in analyzing a problem and arriving at a
conclusion. In these systems, in order to simulate the human reasoning process, a vast amount of
knowledge needed to be stored in the knowledge base. Generally, the knowledge base of such an
expert system consisted of a relatively large number of "if then" type of statements that were
interrelated in a manner that, in theory at least, resembled the sequence of mental steps that were
involved in the human reasoning process.
Because of the need for large storage capacities and related programs to store the rulebase,
most expert systems have, in the past, been run only on large information handling systems.
Recently, the storage capacity of personal computers has increased to a point where it is becoming
possible to consider running some types of simple expert systems on personal computers.
In some applications of expert systems, the nature of the application and the amount of
stored information necessary to simulate the human reasoning process for that application is just
too vast to store in the active memory of a computer. In other applications of expert systems, the
nature of the application is such that not all of the information is always needed in the reasoning
process. An example of this latter type application would be the use of an expert system to diagnose
a data processing system comprising many separate components, some of which are optional. When
that type of expert system employs a single integrated rulebase to diagnose the minimum system
configuration of the data processing system, much of the rulebase is not required since many of the
components which are optional units of the system will not be present in the system. Nevertheless,
earlier expert systems require the entire rulebase to be stored since all the rules were, in effect,
chained or linked together by the structure of the rulebase.
When the rulebase is segmented, preferably into contextual segments or units, it is then
possible to eliminate portions of the Rulebase containing data or knowledge that is not needed in a
particular application. The segmenting of the rulebase also allows the expert system to be run with
systems or on systems having much smaller memory capacities than was possible with earlier
arrangements since each segment of the rulebase can be paged into and out of the system as
needed. The segmenting of the rulebase into contextual segments requires that the expert system
manage various intersegment relationships as segments are paged into and out of memory during
execution of the program. Since the system permits a rulebase segment to be called and executed at
any time during the processing of the first rulebase, provision must be made to store the data that
has been accumulated up to that point so that at some time later in the process, when the system
returns to the first segment, it can proceed from the last point or rule node that was processed.
Also, provision must be made so that data that has been collected by the system up to that point can
be passed to the second segment of the rulebase after it has been paged into the system and data
collected during the processing of the second segment can be passed to the first segment when the
system returns to complete processing that segment.
The user interface and the procedure interface are two important functions in the
information collection process.
Automatic Learning and Neuro-fuzzy.
In the field of artificial intelligence, neuro-fuzzy refers to combinations of artificial neural
networks and fuzzy logic. Neuro-fuzzy hybridization results in a hybrid intelligent system that
synergizes these two techniques by combining the human-like reasoning style of fuzzy systems with
the learning and connectionist structure of neural networks. Neuro-fuzzy hybridization is widely
termed as Fuzzy Neural Network (FNN) or Neuro-Fuzzy System (NFS) in the literature. Neuro-fuzzy
system (the more popular term is used henceforth) incorporates the human-like reasoning style of
fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy
rules. The main strength of neuro-fuzzy systems is that they are universal approximators with the
ability to solicit interpretable IF-THEN rules.
The strength of neuro-fuzzy systems involves two contradictory requirements in fuzzy
modeling: interpretability versus accuracy. In practice, one of the two properties prevails. The neurofuzzy in fuzzy modeling research field is divided into two areas: linguistic fuzzy modeling that is
focused on interpretability, mainly the Mamdani model; and precise fuzzy modeling that is focused
on accuracy, mainly the Takagi-Sugeno-Kang (TSK) model.
Although generally assumed to be the realization of a fuzzy system through connectionist
networks, this term is also used to describe some other configurations including:
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Deriving fuzzy rules from trained RBF networks.
Fuzzy logic based tuning of neural network training parameters.
Fuzzy logic criteria for increasing a network size.
Realising fuzzy membership function through clustering algorithms in unsupervised learning
in SOMs and neural networks.
Representing fuzzification, fuzzy inference and defuzzification through multi-layers feedforward connectionist networks.
It must be pointed out that interpretability of the Mamdani-type neuro-fuzzy systems can be
lost. To improve the interpretability of neuro-fuzzy systems, certain measures must be taken,
wherein important aspects of interpretability of neuro-fuzzy systems are also discussed.
Intelligent system.
In order to make great games,
great tools and a great work environment are essential.
INTELLIGENT SYSTEMS began by supporting Nintendo game development from the very
beginning. At first, development tools were required in order to develop game software. From
there a strong partnership formed with a shared dream of creating great development tools for
game developers. Confidence in our development tools and a passion for making them, contributed
to the creation of our company and its environment. From our experience we believe that “with
great development tools and the expertise in using them, along with a great work environment, are
essential for creating great games.”
INTELLIGENT SYSTEMS creates it own unique game development environment.
INTELLIGENT SYSTEMS grew not only as a game developer, but as a game environment and
development tool developer as well. Resulting in our company having advance technical knowledge
and originality in game development. Along with our great partnership with Nintendo, we are a
driving force that continues to make the industry thrive
Applications of AI
Q. What are the applications of AI?
A. Here are some.
game playing
You can buy machines that can play master level chess for a few hundred dollars.
There is some AI in them, but they play well against people mainly through brute
force computation--looking at hundreds of thousands of positions. To beat a world
champion by brute force and known reliable heuristics requires being able to look at
200 million positions per second.
speech recognition
In the 1990s, computer speech recognition reached a practical level for limited
purposes. Thus United Airlines has replaced its keyboard tree for flight information
by a system using speech recognition of flight numbers and city names. It is quite
convenient. On the the other hand, while it is possible to instruct some computers
using speech, most users have gone back to the keyboard and the mouse as still
more convenient.
understanding natural language
Just getting a sequence of words into a computer is not enough. Parsing sentences is
not enough either. The computer has to be provided with an understanding of the
domain the text is about, and this is presently possible only for very limited domains.
computer vision
The world is composed of three-dimensional objects, but the inputs to the human
eye and computers' TV cameras are two dimensional. Some useful programs can
work solely in two dimensions, but full computer vision requires partial threedimensional information that is not just a set of two-dimensional views. At present
there are only limited ways of representing three-dimensional information directly,
and they are not as good as what humans evidently use.
heuristic classification
One of the most feasible kinds of expert system given the present knowledge of AI is
to put some information in one of a fixed set of categories using several sources of
information. An example is advising whether to accept a proposed credit card
purchase. Information is available about the owner of the credit card, his record of
payment and also about the item he is buying and about the establishment from
which he is buying it (e.g., about whether there have been previous credit card
frauds at this establishment).
Finance
Banks use artificial intelligence systems to organize operations, invest in stocks, and
manage properties. In August 2001, robots beat humans in a simulated financial
trading competition.
Financial institutions have long used artificial neural network systems to detect
charges or claims outside of the norm, flagging these for human investigation.
Medicine
A medical clinic can use artificial intelligence systems to organize bed schedules,
make a staff rotation, and provide medical information.
They may also be used for medical diagnosis,
Artificial neural networks are used for medical diagnosis (such as in Concept
Processing technology in EMR software), functioning as machine differential
diagnosis.
Heavy industry
Robots have become common in many industries. They are often given jobs that are
considered dangerous to humans. Robots have proven effective in jobs that are very
repetitive which may lead to mistakes or accidents due to a lapse in concentration
and other jobs which humans may find degrading. General Motors uses around
16,000 robots for tasks such as painting, welding, and assembly. Japan is the leader
in using and producing robots in the world. In 1995, 700,000 robots were in use
worldwide; over 500,000 of which were from Japan.
For more information, see surveyabout artificial intelligence in business.
Transportation
Fuzzy logic controllers have been developed for automatic gearboxes in automobiles
(the 2006 Audi TT, VW Toureg and VW Caravell feature the DSP transmission which
utilizes Fuzzy logic, a number of Škoda variants (Škoda Fabia) also currently include a
Fuzzy Logic based controller).
Telecommunications
Many telecommunications companies make use of heuristic search in the
management of their workforces, for example BT Group has deployed heuristic
searchin a scheduling application that provides the work schedules of 20000
engineers.
Toys and games
The 1990s saw some of the first attempts to mass-produce domestically aimed types
of basic Artificial Intelligence for education, or leisure. This prospered greatly with
the Digital Revolution, and helped introduce people, especially children, to a life of
dealing with various types of AI, specifically in the form of Tamagotchis and Giga
Pets, the Internet (example: basic search engine interfaces are one simple form), and
the first widely released robot, Furby. A mere year later an improved type of
domestic robot was released in the form of Aibo, a robotic dog with intelligent
features and autonomy. AI has also been applied to video games.
Music
The evolution of music has always been affected by technology. With AI, scientists
are trying to make the computer emulate the activities of the skillful musician.
Composition, performance, music theory, sound processing are some of the major
areas on which research in Music and Artificial Intelligence are focusing on.
Aviation
The Air Operations Division, AOD, uses for the rule based expert systems. The AOD
has use for artificial intelligence for surrogate operators for combat and training
simulators, mission management aids, support systems for tactical decision making,
and post processing of the simulator data into symbolic summaries.
The use of artificial intelligence in simulators is proving to be very useful for the AOD.
Airplane simulators are using artificial intelligence in order to process the data taken
from simulated flights. Other than simulated flying, there is also simulated aircraft
warfare. The computers are able to come up with the best success scenarios in these
situations. The computers can also create strategies based on the placement, size,
speed, and strength of the forces and counter forces. Pilots may be given assistance
in the air during combat by computers. The artificial intelligent programs can sort the
information and provide the pilot with the best possible maneuvers, not to mention
getting rid of certain maneuvers that would be impossible for a sentient being to
perform. Multiple aircraft are needed to get good approximations for some
calculations so computer simulated pilots are used to gather data. These computer
simulated pilots are also used to train future air traffic controllers.
The system used by the AOD in order to measure performance was the Interactive
Fault Diagnosis and Isolation System, or IFDIS. It is a rule based expert system put
together by collecting information from TF-30 documents and the expert advice
from mechanics that work on the TF-30. This system was designed to be used to for
the development of the TF-30 for the RAAF F-111C. The performance system was
also used to replace specialized workers. The system allowed the regular workers to
communicate with the system and avoid mistakes, miscalculations, or having to
speak to one of the specialized workers.
The AOD also uses artificial intelligence in speech recognition software. The air traffic
controllers are giving directions to the artificial pilots and the AOD wants to the
pilots to respond to the ATC’s with simple responses. The programs that incorporate
the speech software must be trained, which means they use neural networks. The
program used, the Verbex 7000, is still a very early program that has plenty of room
for improvement. The improvements are imperative because ATCs use very specific
dialog and the software needs to be able to communicate correctly and promptly
every time.
The Artificial Intelligence supported Design of Aircraft [2], or AIDA, is used to help
designers in the process of creating conceptual designs of aircraft. This program
allows the designers to focus more on the design itself and less on the design
process. The software also allows the user to focus less on the software tools. The
AIDA uses rule based systems to compute its data. This is a diagram of the
arrangement of the AIDA modules. Although simple, the program is proving
effective.
In 2003, NASA’s Dryden Flight Research Center, and many other companies, created
software that could enable a damaged aircraft to continue flight until a safe landing
zone can be reached. The Intelligent Flight Control System was tested on an F-15 ,
which was heavily modified by NASA. The software compensates for all the damaged
components by relying on the undamaged components. The neural network used in
the software proved to be effective and marked a triumph for artificial intelligence.
The Integrated Vehicle Health Management system, also used by NASA, on board an
aircraft must process and interpret data taken from the various sensors on the
aircraft. The system needs to be able to determine the structural integrity of the
aircraft. The system also needs to implement protocols in case of any damage taken
the vehicle.
Other
Neural networks are also being widely deployed in homeland security, speech and
text recognition, data mining, and e-mail spam filtering.