Artificial Intelligence

Mrs. Lakshmi Sivasubramaniam

Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy.

AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think". In order to classify machines as "thinking", it is necessary to define intelligence. To what degree does intelligence consist of, for example, solving complex problems, or making generalizations and relationships? And what about perception and comprehension? Research into the areas of learning, of language, and of sensory perception has aided scientists in building intelligent machines. One of the most challenging approaches facing experts is building systems that mimic the behavior of the human brain, made up of billions of neurons, and arguably the most complex matter in the universe.

Artificial Intelligence has come a long way from its early roots, driven by dedicated researchers. The beginnings of AI reach back before electronics, to philosophers and mathematicians such as Boole and others theorizing on principles that were used as the foundation of AI Logic. AI really began to intrigue researchers with the invention of the computer in 1943. The technology was finally available, or so it seemed, to simulate intelligent behavior. Over the next four decades, despite many stumbling blocks, AI has grown from a dozen researchers, to thousands of engineers and specialists; and from programs capable of playing checkers, to systems designed to diagnose disease.

AI has always been on the pioneering end of computer science. Advanced-level computer languages, as well as computer interfaces and word-processors owe their existence to the research into artificial intelligence. The theory and insights brought about by AI research will set the trend in the future of computing. The products available today are only bits and pieces of what are soon to follow, but they are a movement towards the future of artificial intelligence. The advancements in the quest for artificial intelligence have, and will continue to affect our jobs, our education, and our lives.

The History of Artificial Intelligence

Timeline of major AI events

Evidence of Artificial Intelligence folklore can be traced back to ancient Egypt, but with the development of the electronic computer in 1941, the technology finally became available to create machine intelligence. The term artificial intelligence was first coined in 1956, at the Dartmouth conference, and since then Artificial Intelligence has expanded because of the theories and principles developed by its dedicated researchers. Through its short modern history, advancement in the fields of AI have been slower than first estimated, progress continues to be made. From its birth 4 decades ago, there have been a variety of AI programs, and they have impacted other technological advancements.

The first computers required large, separate air-conditioned rooms, and were programmers nightmare, involving the separate configuration of thousands of wires to even gets a program running. The 1949 innovation, the stored program computer, made the job of entering a program easier, and advancements in computer theory lead to computer science, and eventually Artificial intelligence. With the invention of an electronic means of processing data, came a medium that made AI possible. Although the computer provided the technology necessary for AI, it was not until the early 1950's that the link between human intelligence and machines was really observed.

Prehistory of AI

Humans have always speculated about the nature of mind, thought, and language, and searched for discrete representations of their knowledge. Aristotle tried to formalize this speculation by means of syllogistic logic, which remains one of the key strategies of AI. The first is-a hierarchy was created in 260 by Porphyry of Tyros. Classical and medieval grammarians explored more subtle features of language that Aristotle shortchanged. In the 13th century Ramon Llull was the first to build 'machines' that used logical means to produce knowledge. The mathematician Bernard Bolzano made the first modern attempt to formalize semantics in 1837.

Early computer design was driven mainly by the complex mathematics needed to target weapons accurately, with analog feedback devices inspiring an ideal of cybernetics. The expression "artificial intelligence" was introduced as a 'digital' replacement for the analog 'cybernetics'.

Historical Antecedents

Greek myths of Hephaestus and Pygmalion incorporate the idea of intelligent robots. In the 5th century BC, Aristotle invented syllogistic logic, the first formal deductive reasoning system. Ramon Llull, Spanish theologian, invented paper "machines" for discovering nonmathematical truths through combinations of words from lists in the 13th century.

By the 15th century and 16th century, clocks, the first modern measuring machines, were first produced using lathes. Clockmakers extended their craft to creating mechanical animals and other novelties. Rabbi Judah Loew ben Bezalel of Prague is said to have invented the Golem, a clay man brought to life (1580).

Early in the 17th century, René Descartes proposed that bodies of animals are nothing more than complex machines. Many other 17th century thinkers offered variations and elaborations of Cartesian mechanism. Thomas Hobbes published Leviathan, containing a material and combinatorial theory of thinking. Wilhelm Schickard created the first mechanical calculating machine in 1623, Blaise Pascal created the second mechanical and first digital calculating machine (1642). Gottfried Leibniz improved the earlier machines, making the Stepped Reckoner to do multiplication and division (1673). He also invented the binary system and evisioned a universal calculus of reasoning (Alphabet of human thought) by which arguments could be decided mechanically.

The 18th century saw a profusion of mechanical toys, including the celebrated mechanical duck of Jacques de Vaucanson and Wolfgang von Kempelen's phony chess-playing automaton, The Turk (1769). Mary Shelley published the story of Frankenstein; or the Modern Prometheus (1818).

19th and Early 20th Century

George Boole developed a binary algebra (Boolean algebra) representing (some) "laws of thought." Charles Babbage & Ada Lovelace worked on programmable mechanical calculating machines.

In the first years of the 20th century Bertrand Russell and Alfred North Whitehead published Principia Mathematica, which revolutionized formal logic. Russell, Ludwig Wittgenstein, and Rudolf Carnap lead philosophy into logical analysis of knowledge. Karel Čapek's play R.U.R. (Rossum's Universal Robots)) opens in London (1923). This is the first use of the word "robot" in English.

Mid 20th century and Early AI

In 1931 Kurt Gödel showed that sufficiently powerful consistent formal systems permit the formulation of true theorems that are unprovable by any theorem-proving machine deriving all possible theorems from the axioms. To do this he had to build a universal, integer-based programming language, which is the reason why he is sometimes called the "father of theoretical computer science". Since human mathematicians are able to "see" the truth of Goedel's theorems, AIs were deemed inferior by certain philosophers.

In 1941 Konrad Zuse built the first working program-controlled computers. Warren Sturgis McCulloch and Walter Pitts publish "A Logical Calculus of the Ideas Immanent in Nervous Activity" (1943), laying foundations for artificial neural networks. Arturo Rosenblueth, Norbert Wiener and Julian Bigelow coin the term "cybernetics" in a 1943 paper. Wiener's popular book by that name published in 1948.

Game theory which would prove invaluable in the progress of AI was introduced with the 1944 paper, Theory of Games and Economic Behavior by mathematician John von Neumann and economist Oskar Morgenstern. Vannevar Bush published As We May Think (The Atlantic Monthly, July 1945) a prescient vision of the future in which computers assist humans in many activities.

1950's

Date	Development
1950	Alan Turing (who introduced the universal Turing machine in 1936) published "Computing Machinery and Intelligence", which suggested the Turing test as a way of operationalizing a test of intelligent behavior.
1950	Claude Shannon published a detailed analysis of chess playing as search.
1950	Isaac Asimov published his Three Laws of Robotics.
1951	The first working AI programs were written in 1951 to run on the Ferranti Mark I machine of the University of Manchester: a checkers-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz.
1952-1962	Arthur Samuel (IBM) wrote the first game-playing program, for checkers (draughts), to achieve sufficient skill to challenge a world champion. His first checkers-playing program was written in 1952, and in 1955 he created a version that learned to play (Samuel 1959).
1956	John McCarthy coined the term "artificial intelligence" as the topic of the Dartmouth Conference, the first conference devoted to the subject.
1956	The first demonstration of the Logic Theorist (LT) written by Allen Newell, J.C. Shaw and Herbert Simon (Carnegie Institute of Technology, now Carnegie Mellon University). This is often called the first AI program, though Samuel's checkers program also has a strong claim.
1957	The General Problem Solver (GPS) demonstrated by Newell, Shaw and Simon.
1958	John McCarthy (Massachusetts Institute of Technology or MIT) invented the Lisp programming language.
1958	Herb Gelernter and Nathan Rochester (IBM) described a theorem prover in geometry that exploits a semantic model of the domain in the form of diagrams of "typical" cases.
1958	Teddington Conference on the Mechanization of Thought Processes was held in the UK and among the papers presented were John McCarthy's Programs with Common Sense, Oliver Selfridge’s Pandemonium, and Marvin Minsky's Some Methods of Heuristic Programming and Artificial Intelligence.
Late 1950s, early 1960s	Margaret Masterman and colleagues at University Cambridge design semantic nets for machine translation.

1960's

Date	Development
1960s	Ray Solomonoff lays the foundations of a mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction.
1961	James Slagle (PhD dissertation, MIT) wrote (in Lisp) the first symbolic integration program, SAINT, which solved calculus problems at the college freshman level.
1962	First industrial robot company, Unimation, founded.
1963	Thomas Evans' program, ANALOGY, written as part of his PhD work at MIT, demonstrated that computers can solve the same analogy problems as are given on IQ tests.
1963	Edward Feigenbaum and Julian Feldman published Computers and Thought, the first collection of articles about artificial intelligence.
1963	Leonard Uhr and Charles Vossler published "A Pattern Recognition Program That Generates, Evaluates, and Adjusts Its Own Operators", which described one of the first machine learning programs that could adaptively acquire and modify features and thereby overcome the limitations of simple perceptrons of Rosenblatt
1964	Danny Bobrow's dissertation at MIT (technical report #1 from MIT's AI group, Project MAC), shows that computers can understand natural language well enough to solve algebra word problems correctly.
1964	Bertram Raphael's MIT dissertation on the SIR program demonstrates the power of a logical representation of knowledge for question-answering systems.
1965	J. Alan Robinson invented a mechanical proof procedure, the Resolution Method, which allowed programs to work efficiently with formal logic as a representation language.
1965	Joseph Weizenbaum (MIT) built ELIZA (program), an interactive program that carries on a dialogue in English language on any topic. It was a popular toy at AI centers on the ARPANET when a version that "simulated" the dialogue of a psychotherapist was programmed.
1966	Ross Quillian (PhD dissertation, Carnegie Inst. of Technology, now CMU) demonstrated semantic nets.
1966	First Machine Intelligence workshop at Edinburgh: the first of an influential annual series organized by Donald Michie and others.
1966	Negative report on machine translation kills much work in Natural language processing (NLP) for many years.
1967	Dendral program (Edward Feigenbaum, Joshua Lederberg, Bruce Buchanan, Georgia Sutherland at Stanford University) demonstrated to interpret mass spectra on organic chemical compounds. First successful knowledge-based program for scientific reasoning.
1968	Joel Moses (PhD work at MIT) demonstrated the power of symbolic reasoning for integration problems in the Macsyma program. First successful knowledge-based program in mathematics.
1968	Richard Greenblatt (programmer) at MIT built a knowledge-based chess-playing program, MacHack, which was good enough to achieve a class-C rating in tournament play.
1969	Stanford Research Institute (SRI): Shakey the Robot, demonstrated combining animal locomotion, perception and problem solving.
1969	Roger Schank (Stanford) defined conceptual dependency model for natural language understanding. Later developed (in PhD dissertations at Yale University) for use in story understanding by Robert Wilensky and Wendy Lehnert, and for use in understanding memory by Janet Kolodner.
1969	Yorick Wilks (Stanford) developed the semantic coherence view of language called Preference Semantics, embodied in the first semantics-driven machine translation program, and the basis of many PhD dissertations since such as Bran Boguraev and David Carter at Cambridge.
1969	First International Joint Conference on Artificial Intelligence (IJCAI) held at Stanford.
1969	Marvin Minsky and Seymour Papert publish Perceptrons, demonstrating previously unrecognized limits of a simple form of neural nets. This may have helped trigger the AI winter of the 1970s, a failure of confidence and funding for AI. Nevertheless significant progress in the field continued (see below).

1970s

Date	Development
Early 1970s	Jane Robinson and Don Walker established an influential Natural Language Processing group at SRI.
1970	Jaime Carbonell (Sr.) developed SCHOLAR, an interactive program for computer assisted instruction based on semantic nets as the representation of knowledge.
1970	Bill Woods described Augmented Transition Networks (ATN's) as a representation for natural language understanding.
1970	Patrick Winston's PhD program, ARCH, at MIT learned concepts from examples in the world of children's blocks.
1971	Terry Winograd's PhD thesis (MIT) demonstrated the ability of computers to understand English sentences in a restricted world of children's blocks, in a coupling of his language understanding program, SHRDLU, with a robot arm that carried out instructions typed in English.
1972	Prolog programming language developed by Alain Colmerauer.
1973	The Assembly Robotics Group at University of Edinburgh builds Freddy Robot, capable of using visual perception to locate and assemble models.
1973	The Lighthill report gives a largely negative verdict on AI research in Great Britain and forms the basis for the decision by the British government to discontine support for AI research in all but two universities.
1974	Edward H. Shortliffe's PhD dissertation on the MYCIN program (Stanford) demonstrated the power of rule-based systems for knowledge representation and inference in the domain of medical diagnosis and therapy. Sometimes called the first expert system.
1974	Earl Sacerdoti developed one of the first planning programs, ABSTRIPS, and developed techniques of hierarchical planning.
1975	Marvin Minsky published his widely-read and influential article on Frames as a representation of knowledge, in which many ideas about schemas and semantic links are brought together.
1975	The Meta-Dendral learning program produced new results in chemistry (some rules of mass spectrometry) the first scientific discoveries by a computer to be published in a referreed journal.
Mid 1970s	Barbara Grosz (SRI) established limits to traditional AI approaches to discourse modeling. Subsequent work by Grosz, Bonnie Webber and Candace Sidner developed the notion of "centering", used in establishing focus of discourse and anaphoric references in Natural language processing.
Mid 1970s	David Marr and MIT colleagues describe the "primal sketch" and its role in visual perception.
1976	Douglas Lenat's AM program (Stanford PhD dissertation) demonstrated the discovery model (loosely-guided search for interesting conjectures).
1976	Randall Davis demonstrated the power of meta-level reasoning in his PhD dissertation at Stanford.
1978	Tom Mitchell, at Stanford, invented the concept of Version Spaces for describing the search space of a concept formation program.
1978	Herbert Simon wins the Nobel Prize in Economics for his theory of bounded rationality, one of the cornerstones of AI known as "satisficing".
1978	The MOLGEN program, written at Stanford by Mark Stefik and Peter Friedland, demonstrated that an object-oriented programming representation of knowledge can be used to plan gene-cloning experiments.
1979	Bill VanMelle's PhD dissertation at Stanford demonstrated the generality of MYCIN's representation of knowledge and style of reasoning in his EMYCIN program, the model for many commercial expert system "shells".
1979	Jack Myers and Harry Pople at University of Pittsburgh developed INTERNIST, a knowledge-based medical diagnosis program based on Dr. Myers' clinical knowledge.
1979	Cordell Green, David Barstow, Elaine Kant and others at Stanford demonstrated the CHI system for automatic programming.
1979	The Stanford Cart, built by Hans Moravec, becomes the first computer-controlled, autonomous vehicle when it successfully traverses a chair-filled room and circumnavigates the Stanford AI Lab.
1979	Drew McDermott and Jon Doyle at MIT, and John McCarthy at Stanford begin publishing work on non-monotonic logics and formal aspects of truth maintenance.
Late 1970s	Stanford's SUMEX-AIM resource, headed by Ed Feigenbaum and Joshua Lederberg, demonstrates the power of the ARPAnet for scientific collaboration.

1980s

Date	Development
Early 1980s	The team of Ernst Dickmanns at Bundeswehr University Munich builds the first robot cars, driving up to 55 mph on empty streets.
1980s	Lisp machines developed and marketed. First expert system shells and commercial applications.
1980	First National Conference of the American Association for Artificial Intelligence (AAAI) held at Stanford.
1981	Danny Hillis designs the connection machine, which utilizes Parallel computing to bring new power to AI, and to computation in general. (Later founds Thinking Machines, Inc.)
1982	The Fifth Generation Computer Systems project (FGCS), an initiative by Japan's Ministry of International Trade and Industry, begun in 1982, to create a "fifth generation computer" (see history of computing hardware) which was supposed to perform much calculation utilizing massive parallelism.
1983	John Laird and Paul Rosenbloom, working with Allen Newell, complete CMU dissertations on Soar (program).
1983	James F. Allen invents the Interval Calculus, the first widely used formalization of temporal events.
Mid 1980s	Neural Networks become widely used with the Backpropagation algorithm (first described by Paul Werbos in 1974).
1985	The autonomous drawing program, AARON, created by Harold Cohen, is demonstrated at the AAAI National Conference (based on more than a decade of work, and with subsequent work showing major developments).
1987	Marvin Minsky published The Society of Mind, a theoretical description of the mind as a collection of cooperating agents. He had been lecturing on the idea for years before the book came out (c.f. Doyle 1983).
1987	Around the same time, Rodney Brooks introduced the subsumption architecture and behavior-based robotics as a more minimalist modular model of natural intelligence.
1989	Dean Pomerleau at CMU creates ALVINN (An Autonomous Land Vehicle in a Neural Network).

1990s

Date	Development
Early 1990s	TD-Gammon, a backgammon program written by Gerry Tesauro, demonstrates that reinforcement (learning) is powerful enough to create a championship-level game-playing program by competing favorably with world-class players.
1990s	Major advances in all areas of AI, with significant demonstrations in machine learning, intelligent tutoring, case-based reasoning, multi-agent planning, scheduling, uncertain reasoning, data mining, natural language understanding and translation, vision, virtual reality, games, and other topics.
1993	Ian Horswill extended behavior-based robotics by creating Polly, the first robot to navigate using vision and operate at animal-like speeds (1 meter/second).
1993	Rodney Brooks, Lynn Andrea Stein and Cynthia Breazeal started the widely-publicized MIT Cog project with numerous collaborators, in an attempt to build a humanoid robot child in just five years.
1993	ISX corporation wins "DARPA contractor of the year" for the Dynamic Analysis and Replanning Tool (DART) which reportedly repaid the US government's entire investment in AI research since the 1950s.
1995	ALVINN steered a car coast-to-coast under computer control for all but about 50 of the 2850 miles. Throttle and brakes, however, were controlled by a human driver.
1995	In the same year, one of Ernst Dickmann' robot cars (with robot-controlled throttle and brakes) drove more than 1000 miles from Munich to Copenhagen and back, in traffic, at up to 120 mph, occasionally executing maneuvers to pass other cars (only in a few critical situations a safety driver took over). Active vision was used to deal with rapidly changing street scenes.
1997	The Deep Blue chess program (IBM) beats the world chess champion, Garry Kasparov, in a widely followed match.
1997	First official RoboCup football (soccer) match featuring table-top matches with 40 teams of interacting robots and over 5000 spectators.
1998	Tim Berners-Lee published his Semantic Web Road map paper.
Late 1990s	Web crawlers and other AI-based information extraction programs become essential in widespread use of the World Wide Web.
Late 1990s	Demonstration of an Intelligent room and Emotional Agents at MIT's AI Lab.
Late 1990s	Initiation of work on the Oxygen architecture, which connects mobile and stationary computers in an adaptive network.

2000 and beyond

Date	Development
2000	Interactive robopets ("smart toys") become commercially available, realizing the vision of the 18th century novelty toy makers.
2000	Cynthia Breazeal at MIT publishes her dissertation on Sociable machines, describing Kismet (robot), with a face that expresses emotions.
2000	The Nomad robot explores remote regions of Antarctica looking for meteorite samples.
2004	OWL Web Ontology Language W3C Recommendation (10 February 2004).
2006	The Dartmouth Artificial Intelligence Conference: The Next 50 Years (AI@50) AI@50 (July 14-16 2006)
2006	Release 1.0 of the OpenCyc top-level ontology engine is released as open source at sourceforge.net.

The field of artificial intelligence truly dawned in the 1950s, since then there have been many achievements in the History of artificial intelligence, some of the more notable moments include:

Year	Development
1950	Alan Turing introduces the Turing test to test of a machine's capability to perform human-like conversation.
1951	The first working AI programs were written to run on the Ferranti Mark I machine of the University of Manchester: a checkers-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz.
1956	John McCarthy coined the term "artificial intelligence" as the topic of the Dartmouth Conference.
1958	John McCarthy invented the Lisp programming language.
1965	Joseph Weizenbaum built ELIZA, an interactive program that carries on a dialogue in English language on any topic.
1966	Machine Intelligence workshop at Edinburgh - the first of an influential annual series organized by Donald Michie and others.
1968	HAL 9000 made its appearance in the science fiction movie 2001: A Space Odyssey.
1972	The Prolog programming language was developed by Alain Colmerauer.
1973	Edinburgh Freddy Assembly Robot: a versatile computer-controlled assembly system.
1974	Ted Shortliffe's PhD dissertation on the MYCIN program (Stanford) demonstrated the power of rule-based systems for knowledge representation and inference in the domain of medical diagnosis and therapy. Sometimes called the first expert system.
1997	The Deep Blue chess program (IBM) beats the world chess champion, Garry Kasparov.
1999	Sony introduces the AIBO, an artificially intelligent pet.

During the 1990s and 2000s AI has become very influenced by probability theory and statistics. Bayesian networks are the focus of this movement, providing links to more rigorous topics in statistics and engineering such as Markov models and Kalman filters, and bridging the divide between 'neat' and 'scruffy' approaches. This new school of AI is sometimes called 'machine learning'. The last few years have also seen a big interest in game theory applied to AI decision making. After the September 11, 2001 attacks there has been much renewed interest and funding for threat-detection AI systems, including machine vision research and data-mining.

Development of AI theory

Much of the (original) focus of artificial intelligence research draws from an experimental approach to psychology, and emphasizes what may be called linguistic intelligence (best exemplified in the Turing test).

Approaches to artificial intelligence that do not focus on linguistic intelligence include robotics and collective intelligence approaches, which focus on active manipulation of an environment, or consensus decision making, and draw from biology and political science when seeking models of how "intelligent" behavior is organized.

AI also draws from animal studies, in particular with insects, which are easier to emulate as robots, as well as animals with more complex cognition, including apes, who resemble humans in many ways but have less developed capacities for planning and cognition. Some researchers argue that animals, which are apparently simpler than humans, ought to be considerably easier to mimic. But satisfactory computational models for animal intelligence are not available.

There were also early papers which denied the possibility of machine intelligence on logical or philosophical grounds; sufficiently powerful formal systems are either inconsistent or allow for formulating true theorems unprovable by any theorem-proving AI deriving all provable theorems from the axioms. Since humans are able to "see" the truth of such theorems, machines were deemed inferior.

With the development of practical techniques based on AI research, advocates of AI have argued that opponents of AI have repeatedly changed their position on tasks such as computer chess or speech recognition that were previously regarded as "intelligent" in order to deny the accomplishments of AI. In 1969 McCarthy and Hayes started the discussion about the frame problem with their essay, "Some Philosophical Problems from the Standpoint of Artificial Intelligence".

Experimental AI research

Historically, there are two broad styles of AI research - the "neats" and "scruffies". "Neat", classical or symbolic AI research, in general, involves symbolic manipulation of abstract concepts, and is the methodology used in most expert systems. Parallel to this are the "scruffy", or "connectionist", approaches, of which artificial neural networks are the best-known example, which try to "evolve" intelligence through building systems and then improving them through some automatic process rather than systematically designing something to complete the task. Both approaches appeared very early in AI history. Throughout the 1960s and 1970s scruffy approaches were pushed to the background, but interest was regained in the 1980s when the limitations of the "neat" approaches of the time became clearer. However, it has become clear that contemporary methods using both broad approaches have severe limitations.

Artificial intelligence research was very heavily funded in the 1980s by the Defense Advanced Research Projects Agency in the United States and by the fifth generation computer systems project in Japan. The failure of the work funded at the time to produce immediate results, despite the grandiose promises of some AI practitioners, led to correspondingly large cutbacks in funding by government agencies in the late 1980s, leading to a general downturn in activity in the field known as AI winter. Over the following decade, many AI researchers moved into related areas with more modest goals such as machine learning, robotics, and computer vision, though research in pure AI continued at reduced levels.

Micro-World AI

The real world is full of distracting and obscuring detail: generally science progresses by focusing on artificially simple models of reality (in physics, frictionless planes and perfectly rigid bodies, for example). In 1970 Marvin Minsky and Seymour Papert, of the MIT AI Laboratory, proposed that AI research should likewise focus on developing programs capable of intelligent behaviour in artificially simple situations known as micro-worlds. Much research has focused on the so-called blocks world, which consists of coloured blocks of various shapes and sizes arrayed on a flat surface.

Spinoffs

Whilst progress towards the ultimate goal of human-like intelligence has been slow, many spinoffs have come in the process. Notable examples include the languages LISP and Prolog, which were invented for AI research but are now used for non-AI tasks.

AI languages and programming styles

AI research has led to many advances in programming languages including the first list processing language, Lisp dialects, Planner, Actors, the Scientific Community Metaphor, production systems, and rule-based languages.

GOFAI TEST research is often done in programming languages such as Prolog or Lisp. Bayesian work often uses Matlab or Lush (a numerical dialect of Lisp). These languages include many specialist probabilistic libraries. Real-life and especially real-time systems are likely to use C++. AI programmers are often academics and emphasise rapid development and prototyping rather than bulletproof software engineering practices, hence the use of interpreted languages to empower rapid command-line testing and experimentation.

The most basic AI program is a single If-Then statement, such as "If A, then B." If you type an 'A' letter, the computer will show you a 'B' letter. Basically, you are teaching a computer to do a task. You input one thing, and the computer responds with something you told it to do or say. All programs have If-Then logic. A more complex example is if you type in "Hello.", and the computer responds "How are you today?" This response is not the computer's own thought, but rather a line you wrote into the program before. Whenever you type in "Hello.", the computer always responds "How are you today?". It seems as if the computer is alive and thinking to the casual observer, but actually it is an automated response. AI is often a long series of If-Then (or Cause and Effect) statements.

A randomizer can be added to this. The randomizer creates two or more response paths. For example, if you type "Hello", the computer may respond with "How are you today?" or "Nice weather" or "Would you like to play a game?" Three responses (or 'thens') are now possible instead of one. There is an equal chance that any one of the three responses will show. This is similar to a pull-cord talking doll that can respond with a number of sayings. A computer AI program can have thousands of responses to the same input. This makes it less predictable and closer to how a real person would respond, arguably because living people respond somewhat unpredictably. When thousands of input ("if") are written in (not just "Hello.") and thousands of responses ("then") are written into the AI program, then the computer can talk (or type) with most people, if those people know the If statement input lines to type.

Many games, like chess and strategy games, use action responses instead of typed responses, so that players can play against the computer. Robots with AI brains would use If-Then statements and randomizers to make decisions and speak. However, the input may be a sensed object in front of the robot instead of a "Hello." line, and the response may be to pick up the object instead of a response line.

Challenge & Prize

The DARPA Grand Challenge is a race for a $2 million prize where cars drive themselves across several hundred miles of challenging desert terrain without any communication with humans, using GPS, computers and a sophisticated array of sensors. In 2005 the winning vehicles completed all 132 miles of the course in just under 7 hours.

In the post-dot com boom era, some search engine websites have sprung using a simple form of AI to provide answers to questions entered by the visitor. Questions such as "What is the tallest building?" can be entered into the search engine's input form and a list of answers will be returned.

Conditions for intelligence

The Turing test suggests a sufficient condition for intelligence is the ability to converse with a human in such a way that the human is fooled into thinking the conversation is with another human. (In order to remove biases based on how the AI looks, the conversation is normally imagined to take place through a medium like modern-day instant messaging chats.)

Such a test is not a necessary condition; it seems for example that ET was intelligent even if it couldn't convince anyone of this fact due to language barriers and the like. Others doubt that it is even a sufficient condition. Chatbots, for example, are learning more and more sophisticated algorithms for sounding intelligent without any actual understanding of the conversations.

John Searle argues that AI is impossible in his famous thought experiment, the Chinese room. Searle argues that syntax is not sufficient for semantics—that mere symbol manipulation, no matter how complicated, cannot provide genuine meaning or understanding. Most professional philosophers in the area believe that Searle failed to establish that AI is impossible, but there is disagreement about exactly what is wrong with his argument, with the Systems Reply, Robot Reply, and Brain Simulator Reply being among the objections.

Ethical issues

There are many ethical problems associated with working to create intelligent creatures.

• AI rights: if an AI is comparable in intelligence to humans, then should it have comparable moral status?
• Would it be wrong to engineer robots that want to perform tasks unpleasant to humans?
• Would a technological singularity be a good result or a bad one? If bad, what safeguards can be put in place, and how effective could any such safeguards be?
• Could a computer simulate an animal or human brain in a way that the simulation should receive the same animal rights or human rights as the actual creature?
• Under what preconditions could such a simulation be allowed to happen at all?

A major influence in the AI ethics dialogue was Isaac Asimov who created the Three Laws of Robotics to govern artificial intelligent systems. Much of his work was then spent testing the boundaries of his three laws to see where they would break down, or where they would create paradoxical or unanticipated behavior. Ultimately, a reading of his work concludes that no set of fixed laws can sufficiently match the possible behavior of AI agents and human society. A criticism of Asimov's robot laws is that the installation of unalterable laws into a sentient consciousness would be a limitation of free will and therefore unethical. Consequently, Asimov's robot laws would be restricted to explicitly non-sentient machines, which possibly could not be made to reliably understand them under all possible circumstances.

Over time, debates have tended to focus less and less on possibility and more on desirability, as emphasized in the "Cosmist" and "Terran" debates initiated by Hugo de Garis and Kevin Warwick. A Cosmist, according to Hugo de Garis, is actually seeking to build more intelligent successors to the human species.

Expectations of AI

AI methods are often employed in cognitive science research, which tries to model subsystems of human cognition. Historically, AI researchers aimed for the loftier goal of so-called strong AI—of simulating complete, human-like intelligence. This goal is epitomised by the fictional strong AI computer HAL 9000 in the film 2001: A Space Odyssey. This goal is unlikely to be met in the near future and is no longer the subject of most serious AI research. The label "AI" has something of a bad name due to the failure of these early expectations, and aggravation by various popular science writers and media personalities such as Professor Kevin Warwick whose work has raised the expectations of AI research far beyond its current capabilities. For this reason, many AI researchers say they work in cognitive science, informatics, statistical inference or information engineering. Recent research areas include Bayesian networks and artificial life.

The vision of artificial intelligence replacing human professional judgment has arisen many times in the history of the field, and today in some specialized areas where "expert systems" are routinely used to augment or to replace professional judgment in some areas of engineering and of medicine.

AI in business

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 (BBC News, 2001). A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and to provide medical information. Many practical applications are dependent on artificial neural networks ; networks that pattern their organization in mimicry of a brain's neurons, which have been found to excel in pattern recognition. Financial institutions have long used such systems to detect charges or claims outside of the norm, flagging these for human investigation. Neural networks are also being widely deployed in homeland security, speech and text recognition, medical diagnosis (such as in Concept Processing technology in EMR software), data mining, and e-mail spam filtering.

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 robots in the world. In 1995, 700,000 robots were in use worldwide; over 500,000 of which were from Japan (Encarta, 2006).

AI in fiction

In science fiction AI — almost always strong AI — is commonly portrayed as an upcoming power trying to overthrow human authority as in HAL 9000, Skynet, Colossus and The Matrix or as service humanoids like C-3PO, Marvin, Data, KITT and KARR, the Bicentennial Man, the Mechas in A.I., Cortana from the Halo series or Sonny in I, Robot.

Some writers, such as Vernor Vinge and Ray Kurzweil, have also speculated that the advent of strong AI is likely to cause abrupt and dramatic societal change. The period of abrupt change is sometimes referred to as "the Singularity".

Author Frank Herbert explored the idea of a time when mankind might ban clever machines entirely. His Dune series makes mention of a rebellion called the Butleria Jihad in which mankind defeats the smart machines of the future and then imposes a death penalty against any who would again create thinking machines. Often quoted from the fictional Orange Catholic Bible, "Thou shalt not make a machine in the likeness of a human mind." A similar idea is also explored in Battlestar Galactica, where the use of networked computers is seen as controversial due to mankinds previous mistake of creating the Cylons.

Approaches

In the quest to create intelligent machines, the field of Artificial Intelligence has split into several different approaches based on the opinions about the most promising methods and theories. These rivaling theories have lead researchers in one of two basic approaches; bottom-up and top-down. Bottom-up theorists believe the best way to achieve artificial intelligence is to build electronic replicas of the human brain's complex network of neurons, while the top-down approach attempts to mimic the brain's behavior with computer programs.

 

Applications of AI

• 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.

• 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 three-dimensional 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.

• Expert systems

A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.

• 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.

Typical problems to which AI methods are applied

• Pattern recognition is a sub-topic of machine learning. It can be defined as "the act of taking in raw data and taking an action based on the category of the data". Most research in pattern recognition is about methods for supervised learning and unsupervised learning. Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space.

A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features.

An intriguing problem in pattern recognition yet to be solved is the relationship between the problem to be solved (data to be classified) and the performance of various pattern recognition algorithms (classifiers).

Holographic associative memory is another type of pattern matching scheme where a target small patterns can be searched from a large set of learned patterns based on cognitive meta-weight.

Typical applications are automatic speech recognition, classification of text into several categories (e.g. spam/non-spam email messages), the automatic recognition of handwritten postal codes on postal envelopes, or the automatic recognition of images of human faces. The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems.

Pattern recognition is more complex when templates are used to generate variants. For example, in English, sentences often follow the "N-VP" (noun - verb phrase) pattern, but some knowledge of the English language is required to detect the pattern. Pattern recognition is studied in many fields, including psychology, ethology, and computer science.

• Optical character recognition, usually abbreviated to OCR, is a type of computer software designed to translate images of handwritten or typewritten text (usually captured by a scanner) into machine-editable text, or to translate pictures of characters into a standard encoding scheme representing them (e.g. ASCII or Unicode). OCR began as a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the focus on OCR has shifted to implementation of proven techniques.

Optical character recognition (using optical techniques such as mirrors and lenses) and digital character recognition (using scanners and computer algorithms) were originally considered separate fields. Because very few applications survive that use true optical techniques, the optical character recognition term has now been broadened to cover digital character recognition as well.

Early systems required training (the provision of known samples of each character) to read a specific font. "Intelligent" systems with a high degree of recognition accuracy for most fonts are now common. Some systems are even capable of reproducing formatted output that closely approximates the original scanned page including images, columns and other non-textual components.

• Handwriting recognition is the ability of a computer to receive intelligible handwritten input. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition). Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface.

Handwriting recognition principally entails optical character recognition. However, a complete handwriting recognition system also handles formatting, performs correct segmentation into characters and finds the most plausible words.

• Speech recognition (in many contexts also known as 'automatic speech recognition', computer speech recognition or erroneously as Voice Recognition) is the process of converting a speech signal to a sequence of words, by means of an algorithm implemented as a computer program. Speech recognition applications that have emerged over the last few years include voice dialing (e.g., Call home), call routing (e.g., I would like to make a collect call), simple data entry (e.g., entering a credit card number), and preparation of structured documents (e.g., a radiology report).

Voice recognition or speaker recognition is a related process that attempts to identify the person speaking, as opposed to what is being said.

• Facial recognition system is a computer-driven application for automatically identifying a person from a digital image. It does that by comparing selected facial features in the live image and a facial database.

It is typically used for security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems.

Popular recognition algorithms include eigenface, fisherface, the Hidden Markov model, and the neuronal motivated Dynamic Link Matching. A newly emerging trend, claimed to achieve previously unseen accuracies, is three-dimensional face recognition. Another emerging trend uses the visual details of the skin, as captured in standard digital or scanned images. Tests on the FERET database, the widely used industry benchmark, showed that this approach is substantially more reliable than previous algorithms.^[

• Artificial Creativity is a branch of Artificial Intelligence based on trying to make computers creative or on trying to understand human creativity by doing research in making computers creative.
• Computer vision is the science and technology of machines that see. As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images or multi-dimensional data. Information, as defined by Shannon, is that which enables a decision. Since perception can be seen as the extraction of information from sensory signals, computer vision can be seen as the scientific investigation of artificial systems for perception from images or multi-dimensional data. As a technological discipline, computer vision seeks to apply the theories and models of computer vision to the construction of computer vision systems. Examples of applications of computer vision systems include systems for

•  
  • Controlling processes (e.g. an industrial robot or an autonomous vehicle).
  • Detecting events (e.g. for visual surveillance)
  • Organizing information (e.g. for indexing databases of images and image sequences),
  • Modeling objects or environments (e.g. industrial inspection, medical image analysis or topographical modeling),
  • Interaction (e.g. as the input to a device for computer-human interaction).

Computer vision can also be described as a complement (but not necessarily the opposite) of biological vision. In biological vision, the visual perception of humans and various animals are studied, resulting in models of how these systems operate in terms of physiological processes. Computer vision, on the other hand, studies and describes artificial vision system that are implemented in software and/or hardware. Interdisciplinary exchange between biological and computer vision has proven increasingly fruitful for both fields. Sub-domains of computer vision include scene reconstruction, event detection, tracking, object recognition, learning, indexing, ego-motion and image restoration.

• Virtual reality (VR) is a technology which allows a user to interact with a computer-simulated environment, be it a real or imagined one. Most current virtual reality environments are primarily visual experiences, displayed either on a computer screen or through special stereoscopic displays, but some simulations include additional sensory information, such as sound through speakers or headphones. Some advanced, haptic systems now include tactile information, generally known as force feedback, in medical and gaming applications. Users can interact with a virtual environment or a virtual artifact (VA) either through the use of standard input devices such as a keyboard and mouse, or through multimodal devices such as a wired glove, the Polhemus boom arm, and omnidirectional treadmill. The simulated environment can be similar to the real world, for example, simulations for pilot or combat training, or it can differ significantly from reality, as in VR games. In practice, it is currently very difficult to create a high-fidelity virtual reality experience, due largely to technical limitations on processing power, image resolution and communication bandwidth. However, those limitations are expected to eventually be overcome as processor, imaging and data communication technologies become more powerful and cost-effective over time.
• Image processing is any form of information processing for which the input is an image, such as photographs or frames of video; the output is not necessarily an image, but can be for instance a set of features of the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.

As a subfield in artificial intelligence, Diagnosis is concerned with the development of algorithms and techniques that are able to determine whether the behaviour of a system is correct. If the system is not functioning correctly, the algorithm should be able to determine, as accurately as possible, which part of the system is failing, and which kind of fault it is facing. The computation is based on observations, which provide information on the current behaviour.

The expression diagnosis also refers to the answer of the question of whether the system is malfunctioning or not, and to the process of computing the answer. This word comes from the medical context where a diagnosis is the process of identifying a disease by its symptoms.

• Game theory is often described as a branch of applied mathematics and economics that studies situations where players choose different actions in an attempt to maximize their returns. The essential feature, however, is that it provides a formal modelling approach to social situations in which decision makers interact with other minds. Game theory extends the simpler optimization approach developed in neoclassical economics.
• Strategic planning is an organization's process of defining its strategy and making decisions on allocating its resources to pursue this strategy, including its capital and people. The outcome is normally a strategic plan which is used as guidance to define functional and divisional plans, including Technology, Marketing, etc.
• Game artificial intelligence refers to techniques used in computer and video games to produce the illusion of intelligence in the behavior of non-player characters (NPCs). The techniques used typically draw upon existing methods from the academic field of artificial intelligence (AI). However, the term game AI is often used to refer to a broad set of algorithms that also include techniques from control theory, robotics, computer graphics and computer science in general.

Since game AI is centered on appearance of intelligence and good gameplay, its approach is very different from that of traditional AI; hacks and cheats are acceptable and, in many cases, the computer abilities must be toned down to give human players a sense of fairness. This, for example, is true in first-person shooter games, where their perfect movement and aiming is beyond human skill.

• A bot, most prominently in the first person shooter PC game types (FPS), is a roBOTic computer controlled entity that simulates an online or LAN multiplayer human deathmatch opponent, team deathmatch opponent or a cooperative human player. Computer game bots work via artificial intelligence routines pre-programmed to suit the game map, game rules, game type and other parameters unique to each game. Bots are not only found in FPS PC games; they are also featured in several console games.
• Natural language processing (NLP) is a subfield of artificial intelligence and linguistics. It studies the problems of automated generation and understanding of natural human languages. Natural language generation systems convert information from computer databases into normal-sounding human language, and natural language understanding systems convert samples of human language into more formal representations that are easier for computer programs to manipulate.
• Translation is the interpretation of the meaning of a text in one language (the "source text") and the production, in another language, of an equivalent text (the "target text," or "translation") that communicates the same message. Translation must take into account a number of constraints, including context, the rules of grammar of the two languages, their writing conventions, their idioms and the like. Consequently, as has been recognized at least since the time of the translator Martin Luther, one translates best into the language that one knows best. Traditionally translation has been a human activity, though attempts have been made to computerize or otherwise automate the translation of natural-language texts (machine translation) or to use computers as an aid to translation (computer-assisted translation).
• A chatterbot is a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods. Though many appear to be intelligently interpreting the human input prior to providing a response, most chatterbots simply scan for keywords within the input and pull a reply with the most matching keywords or the most similar wording pattern from a local database. Chatterbots may also be referred to as talk bots, chat bots, or chatterboxes.
• Non-linear control is a sub-division of control engineering which deals with the control of non-linear systems. The behaviour of a non-linear system cannot be described as a linear function of the state of that system or the input variables to that system. For linear systems, there are many well-established control techniques, for example root-locus, Bode plot, Nyquist criterion, state-feedback, pole-placement etc.
• A Robot is a mechanical or virtual, artificial agent. A Robot is usually an electro-mechanical system, which, by its appearance or movements, conveys a sense that it has intent or agency of its own. The word robot can refer to both physical robots and virtual software agents, but the latter are often referred to as bots.

While there is some discussion as to precisely which machines qualify as robots, a typical robot must have several, but not all of the following properties:

o Is not 'natural' / has been artificially created.

o Can sense its environment.

o Can manipulate things in its environment.

o Has some degree of intelligence, or ability to make choices based on the environment, or automatic control / preprogrammed sequence.

o Can move with one or more axes of rotation or translation.

o Can make dexterous coordinated movements.

o Appears to have intent or agency

ASIMO, a humanoid robot manufactured by Honda.

Other fields in which AI methods are implemented

• Artificial Life, (commonly Alife or alife) is a field of study and art form that examines systems related to life, its processes and its evolution through simulations using computer models, robotics, and biochemistry (called "soft", "hard", and "wet" approaches respectively). Artificial life compliments traditional Biology by trying to recreate biological phenomena rather than take them apart. Because of its predominance within the field, the term "Artificial Life" is often used to specifically refer to soft alife.
• Automated reasoning is an area of computer science dedicated to understanding different aspects of reasoning in a way that allows the creation of software which allows computers to reason completely or nearly completely automatically. As such, it is usually considered a subfield of artificial intelligence, but it also has strong connections to theoretical computer science and even philosophy.

The most developed subareas of automated reasoning probably are automated theorem proving (and the less automated but more pragmatic subfield of interactive theorem proving) and automated proof checking (viewed as guaranteed correct reasoning under fixed assumptions), but extensive work has also been done in reasoning by analogy, induction and abduction. Other important topics are reasoning under uncertainty and non-monotonic reasoning. An important part of the uncertainty field is that of argumentation, where further constraints of minimality and consistency are applied on top of the more standard automated deduction. John Pollock's Oscar system is an example of an automated argumentation system that is more specific than being just an automated theorem prover. Formal argumentation is subfield of artificial intelligence.

Tools and techniques include the classical logics and calculi from automated theorem proving, but also fuzzy logic, Bayesian inference, reasoning with maximal entropy and a large number of less formal ad-hoc techniques.

• Automation (ancient Greek: = self dictated), roboticization or industrial automation or numerical control is the use of control systems such as computers to control industrial machinery and processes, replacing human operators. In the scope of industrialization, it is a step beyond mechanization. Whereas mechanization provided human operators with machinery to assist them with the physical requirements of work, automation greatly reduces the need for human sensory and mental requirements as well.

There are still many jobs which are in no immediate danger of automation. No device has been invented which can match the human eye for accuracy and precision in many tasks; nor the human ear. Even the admittedly handicapped human is able to identify and distinguish among far more scents than any automated device. Human pattern recognition, language recognition, and language production ability is well beyond anything currently envisioned by automation engineers.

Specialised hardened computers, referred to as programmable logic controllers (PLCs), are frequently used to synchronize the flow of inputs from (physical) sensors and events with the flow of outputs to actuators and events. This leads to precisely controlled actions that permit a tight control of almost any industrial process. (It was these devices that were feared to be vulnerable to the "Y2K bug", with such potentially dire consequences, since they are now so ubiquitous throughout the industrial world.)

Human-machine interfaces (HMI) or computer human interfaces (CHI), formerly known as man-machine interfaces, are usually employed to communicate with PLCs and other computers, such as entering and monitoring temperatures or pressures for further automated control or emergency response. Service personnel who monitor and control these interfaces are often referred to as stationary engineers.

Another form of automation involving computers is test automation, where computer-controlled automated test equipment is programmed to simulate human testers in manually testing an application. This is often accomplished by using test automation tools to generate special scripts (written as computer programs) that direct the automated test equipment in exactly what to do in order to accomplish the tests

Finally, the last form of automation is software-automation, where a computer by means of macro recorder software records the sequence of user actions (mouse and keyboard) as a macro for playback at a later time.

• Behavior-based robotics or behavioral robotics or behavioural robotics is the branch of robotics that incorporates modular or behavior based AI (BBAI). Most behavior-based systems are also reactive, which means they use relatively little internal variable state to model the environment. For instance, there is no programming in the robot of what a chair looks like, or what kind of surface the robot is moving on - all the information is gleaned from the input of the robot's sensors. The robot uses that information to react to the changes in its environment.

Behavior-based robots (BBR) usually show more biological-appearing actions than their computing-intensive counterparts, which are very deliberate in their actions. A BBR often makes mistakes, repeats actions, and appears confused, but can also show the anthropomorphic quality of tenacity. Comparisons between BBRs and insects are frequent because of these actions. BBRs are sometimes considered examples of Weak artificial intelligence, although some have claimed they are models of all intelligence

• Biologically-inspired computing (also bio-inspired computing) is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. It is often closely related to the field of artificial intelligence, as many of its pursuits can be linked to machine learning. It relies heavily on the fields of biology, computer science and mathematics. Briefly put, it is the use of computers to model nature, and simultaneously the study of nature to improve the usage of computers. Biologically-inspired computing is a major subset of natural computation.

Some areas of study encompassed under the canon of biologically-inspired computing, and their biological counterparts:

o genetic algorithms ↔ evolution

o biodegradability prediction ↔ biodegradation

o cellular automata ↔ life

o emergent systems ↔ ants, termites, bees, etc

o neural networks ↔ the brain

o artificial life ↔ life

o artificial immune systems ↔ immune system

o rendering (computer graphics) ↔ patterning and rendering of animal skins, bird feathers, mollusk shells and bacterial colonies

o lindenmayer systems ↔ plant structures

o membrane computers ↔ intra-membrane molecular processes in the living cell

o excitable media ↔ forest fires, the Mexican wave, heart conditions, etc

• Cognitive robotics (CR) is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments using limited computational resources. Robotic cognitive capabilities include perception processing, attention allocation, anticipation, planning, reasoning about other agents, and reasoning about their own mental states. Robotic cognition embodies the behaviour of intelligent agents in the physical world (or a virtual world, in the case of simulated CR).

A cognitive robot should exhibit:

o knowledge

o beliefs

o preferences

o goals

o informational attitudes

o motivational attitudes (observing, communicating, revising beliefs, planning)

Cognitive robotics involves the application and integration of various artificial intelligence disciplines, such as knowledge representation, automated reasoning and planning. It also involves the use of agent programming languages for defining transitions between mental states.

A number of different methodologies can be adopted within cognitive robotics, including not only the approach of classical symbolic AI - emphasizing symbolic reasoning and representation - but also more biologically-inspired approaches that draw on neuroscience and studies of animal behaviour.

• Colloquis, previously called ActiveBuddy, is a company that creates conversation-based interactive agents originally distributed via instant messaging platforms. The company has offices in New York, NY and Sunnyvale, CA.

Founded in 2000, the company was the brainchild of Robert Hoffer and Timothy Kay. The idea for interactive agents (also known as Internet bots) came from Hoffer’s vision to add functionality to increasingly popular instant messaging services. The original implementation took shape as a word-based adventure game but quickly grew to include a wide range of database applications including access to news, weather, stock information, movie times, yellow pages listings, and detailed sports data, as well as a variety of tools (calculators, translator, etc.). These various applications were bundled into a single entity and launched as SmarterChild in 2001. SmarterChild acted as a showcase for the quick data access and possibilities for fun conversation that the company planned to turn into customized, niche specific products.

The rapid success of SmarterChild led to targeted promotional products for Radiohead Austin Powers, The Sporting News, and others. ActiveBuddy sought to strengthen its hold on the interactive agent market for the future by filing for, and receiving, a controversial patent on their creation in 2002. The company also released the BuddyScript SDK, a free developer kit that allow programmers to design and launch their own interactive agents using ActiveBuddy’s proprietary scripting language, in 2002. Ultimately, however, the decline in ad spending in 2001 and 2002 led to a shift in corporate strategy towards business focused Automated Service Agents, building products for clients including Cingular, Comcast and Cox Communications. The company subsequently changed its name from ActiveBuddy to Conversagent in 2003, and then again to Colloquis in 2006. Colloquis was later purchased by Microsoft in October of 2006.

Concept mining is a discipline at the nexus of data mining, text mining, and linguistics, drawing on artificial intelligence and statistics. It aims to extract concepts from documents. Since documents consists of words and other symbols, not concepts, the problem is nontrivial, but it can provide powerful insights into the meaning, provenance and similarity of documents.

 

• Cybernetics is the study of feedback and derived concepts such as communication and control in living organisms, machines and organisations. The term cybernetics stems from the Greek Κυβερνήτης (kybernetes, steersman, governor, pilot, or rudder — the same root as government). It is an earlier but still-used generic term for many of the subject matters that are increasingly subject to specialization under the headings of adaptive systems, artificial intelligence, complex systems, complexity theory, control systems, decision support systems, dynamical systems, information theory, learning organizations, mathematical systems theory, operations research, simulation, and systems engineering.

A more philosophical definition, suggested in 1956 by Louis Couffignal, one of the pioneers of cybernetics, characterizes cybernetics as "the art of ensuring the efficacy of action".

• Developmental Robotics (DevRob), sometimes called epigenetic robotics, is a methodology that uses metaphors from developmental psychology to develop controllers for autonomous robots. The focus is on a single robot going through stages of autonomous mental development. Researchers in this field study artificial emotions, self-motivation, and other methods of self-organization.

DevRob is related to, but differs from, evolutionary robotics (ER). ER uses populations of robots that evolve over time, whereas DevRob is interested in the organization of a single robot's control system develops through experience, over time.

DevRob is also related to work done in the domains of Robotics, Artificial Life.

• Epigenetic robotics is an interdisciplinary research area with the goal of understanding biological systems by the integration between neuroscience, developmental psychology and engineering sciences. Epigenetic systems are characterized by a prolonged developmental process through which varied and complex cognitive and perceptual structures emerge as a result of the interaction of an embodied system with a physical and social environment. An additional goal is to enable robots to autonomously develop skills for any particular environment instead of programming them for a specific environment. Epigenetic robotics is closely related to developmental robotics.
• E-mail spam is a subset of spam that involves sending nearly identical messages to numerous recipients by e-mail.

Most definitions of spam are based on the e-mail being Unsolicited Bulk E-mail (UBE). That is, spam is e-mail that is both unsolicited by the recipients and there are many substantively similar e-mails being sent. Spam is usually also unwanted, commercial and sent by automated means and some definitions include those aspects

• Evolutionary Robotics (ER) is a methodology that uses evolutionary computation to develop controllers for autonomous robots. Algorithms in ER frequently operate on populations of candidate controllers, initially selected from some distribution. This population is then repeatedly modified according to a fitness function. In the case of genetic algorithms (or "GAs"), a common method in evolutionary computation, the population of candidate controllers is repeatedly grown according to crossover, mutation and other GA operators and then culled according to the fitness function. The candidate controllers used in ER applications may be drawn from some subset of the set of artificial neural networks, although some applications (including SAMUEL, developed at the Naval Center for Applied Research in Artificial Intelligence) use collections of "IF THEN ELSE" rules as the constituent parts of an individual controller. It is theoretically possible to use any set of symbolic formulations of a control laws (sometimes called a policies in the machine learning community) as the space of possible candidate controllers. It is worth noting that artificial neural networks can also be used for robot learning outside of the context of evolutionary robotics. In particular, other forms of reinforcement learning can be used for learning robot controllers.
• In software programming, hybrid intelligent system denotes a software system which employs, in parallel, a combination of AI models, methods and techniques from such artificial intelligence subfields as:

o Neuro-fuzzy programming

o Fuzzy expert systems

o Connectionist expert systems

o Evolutionary neural networks

o Genetic-Fuzzy-Neural Systems

o Genetic fuzzy systems (Michigan, Pitsburg, Incremental)

o Rough fuzzy and fuzzy Rough systems, also known as rough fuzzy hybridization

o Temporal difference genetic algorithm reinforcement (TDGAR) learning

o Genetic algorithm fuzzy reinforcement learning (GAFRL)

o Symbolic and knowledge/rule-based programming.

From the cognitive science perspective, every natural intelligent system is hybrid because it performs mental operations on both the symbolic and subsymbolic levels. For the past few years there has been an increasing discussion of the importance of systems integration in artificial intelligence. Based on notions that there have already been created simple and specific AI systems (such as systems for computer vision, speech synthesis, etc., or software that employs some of the models mentioned above) and now is the time for integration to create broad AI systems. The Constructionist design methodology (CDM) is a software development philosophy designed specifically for creating large A.I. systems. CDM is based on iterative design steps that lead to the creation of a network of named interacting modules, communicating via explicitly typed streams and discrete messages. The Mindmakers organization is an online consortium, or portal, for people that are working on integration and increased collaboration in the field of A.I.

• In computer science, an intelligent agent (IA) is a software agent that exhibits some form of artificial intelligence that assists the user and will act on their behalf, in performing non-repetitive computer-related tasks. While the working of software agents used for operator assistance or data mining (sometimes referred to as bots) is often based on fixed pre-programmed rules, "intelligent" here implies the ability to adapt and learn.

There are multiple types of agents and sub-agents. For example:

•  
  • Physical Agents

A physical agent is an entity which percepts through sensors and acts through actuators.

•  
  • Temporal Agents

A temporal agent may use time based stored information to offer instructions or data acts to a computer program or human being and takes program inputs percepts to adjust its next behaviors.

A simple agent program can be defined mathematically as an agent function which maps every possible percepts sequence to a possible action the agent can perform or to a coefficient, feedback element, function or constant that affects eventual actions:

The program agent, instead, maps every possible percept to an action. It is possible to group agents into four classes based on their degree of perceived intelligence and capability:

•  
  • simple reflex agents;
  • model-based reflex agents;
  • goal-based agents;
  • utility-based agents.

Simple reflex agent

Learning agent

• The Semantic Web is an evolving extension of the World Wide Web in which web content can be expressed not only in natural language, but also in a form that can be understood, interpreted and used by software agents, thus permitting them to find, share and integrate information more easily. It derives from W3C director Tim Berners-Lee's vision of the Web as a universal medium for data, information, and knowledge exchange.

At its core, the Semantic Web comprises a philosophy a set of design principles,collaborative working groups, and a variety of enabling technologies. Some elements of the Semantic Web are expressed as prospective future possibilities that have yet to be implemented or realized. Other elements of the Semantic Web are expressed in formal specifications. Some of these include Resource Description Framework (RDF), a variety of data interchange formats (e.g RDF/XML, N3, Turtle, N-Triples), and notations such as RDF Schema (RDFS) and the Web Ontology Language (OWL). All of which are intended to formally describe concepts, terms, and relationships within a given problem domain. All control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms can be put into the class of intelligent control.

So we can subdivide intelligent control into following major sub-domains:

o Neural network control

o Bayesian control

o Fuzzy (logic) control

o Neuro-fuzzy control

o Expert Systems

o Genetic control

o Intelligent agents (Cognitive/Conscious control)

New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them.

• Knowledge representation is an issue that arises in both cognitive science and artificial intelligence. In cognitive science it is concerned with how people store and process information. In artificial intelligence (AI) the primary aim is to store knowledge so that programs can process it and achieve the verisimilitude of human intelligence. AI researchers have borrowed representation theories from cognitive science. Thus there are representation techniques such as frames, rules and semantic networks which have originated from theories of human information processing. Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to represent knowledge in a manner as to facilitate inferencing i.e. drawing conclusions from knowledge.

Some issues that arise in knowledge representation from an AI perspective are:

o How do people represent knowledge?

o What is the nature of knowledge and how do we represent it?

o Should a representation scheme deal with a particular domain or should it be general purpose?

o How expressive is a representation scheme?

o Should the scheme be declarative or procedural?

There has been very little top-down discussion of the KR issues and research in this area is a well aged quiltwork. There are well known problems such as "spreading activation," (this is a problem in navigating a network of nodes) "subsumption" (this is concerned with selective inheritance an ATV can be thought of as a specialization of a car but it inherits only particular characteristics) and "classification." For example a banana could be classified both as a fruit and a vegetable.

In the field of artificial intelligence, problem solving can be simplified by an appropriate choice of knowledge representation. Representing the knowledge using a given technique may enable the domain to be represented. For example Mycin, a diagnostic expert system used a rule based representation scheme. An incorrect choice would defeat the representation endeavor; the analogy is to make computations in Hindu-Arabic numerals or in Roman numerals; long division is simpler in one and harder in the other. Likewise, there is no representation that can serve all purposes or make every problem equally approachable.

References

1. WHAT IS ARTIFICIAL INTELLIGENCE? by John McCarthy
2. HammondJ, Kristian. Case-based planning: viewing planning as a memory task. Academic Press Perspectives In Artificial Intelligence; Vol 1. Pages: 277. 1989. ISBN 0-12-322060-2
3. C.M. van der Walt and E. Barnard,“Data characteristics that determine classifier performance”, in Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp.160-165, 2006
4. C.M. van der Walt and E. Barnard,“Data characteristics that determine classifier perfromance”, in Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp.160-165, 2006.
5. Dictionary.com definition. Retrieved on 2007-01-19.
6. Mark A. Bedau (November 2003). Artificial life: organization, adaptation and complexity from the bottom up. TRENDS in Cognitive Sciences. Retrieved on 2007-0
7. Christopher Langston. What is Artificial Life?. Retrieved on 2007-01-19.
8. IEEE Transactions on Automation Science and Engineering
9. Brooks, Rodney A. (1991) "Intelligence Without Representation", Artificial Intelligence 47:139-159.
10. Jones, Joseph L. (2004) "Robot Programming: A practical guide to Behavior-Based Robotics", ISBN 0-07-142778-3
11. Retrieved from http://en.wikipedia.org/wiki/Cognitive_robotics
12. Ashby, W.R. (1956), Introduction to Cybernetics. Methuen, LondonUK. PDF text.
13. Bluma, Lars (2005), Norbert Wiener und die Entstehung der Kybernetik im Zweiten Weltkrieg, Münster.
14. Terminator 2: Judgement Day (1991) Cybernetic Organism, Living Matter over cybernetic tissue
15. W. Frawley and G. Piatetsky-Shapiro and C. Matheus (Fall 1992). "Knowledge Discovery in Databases: An Overview". AI Magazine: pp. 213-228. ISSN 0738-4602. 
16. D. Hand, H. Mannila, P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge, MA,. ISBN 0-262-08290-X. 
17. James John Farmer (27 December 2003). 3.4 Specific Types of Spam (FAQ). An FAQ for news.admin.net-abuse.email; Part 3: Understanding NANAE. spamfaq.net. Retrieved on 2007-01-05.
18. International Journal of Hybrid Intelligent Systems http://ijhis.hybridsystem.com/
19. http://www.iospress.nl/html/14485869.php
20. Steffen Staab, Rudi Studer (January 2004). Handbook on Ontologies. Heidelberg: Springer Verlag. ISBN 3-540-40834-7. 
21. Amaravadi, C. S., “Knowledge Management for Administrative Knowledge,” Expert Systems, 25(2), pp 53-61, May 2005.
22. Ronald J. Brachman; What IS-A is and isn't. An Analysis of Taxonomic Links in Semantic Networks; IEEE Computer, 16 (10); October 1983

About Authors:

Mrs. Lakshmi Sivasubramaniam

Lecturer, Department of Pharmaceutical Analysis, College of Pharmacy, SRM Institute of Science and Technology

Madhumathi Seshadri

Department of Chemistry, Pharmaceutical Chemistry unit, Vellore Institute of Technology, Vellore-632 014, India

Saibal Roy

Masters of Computer Applications, Department of Computer sciences, Vellore Institute of Technology, Vellore-632 014, India

Jai Janani

Masters of Computer Applications, Cognizant Technology Solutions, TCO, Okiampet, Chennai – 600 096