Sierra Acai Company
Sierra Acai Company
Open 7 Days a Week
MonaVie Independent Distributor
Distributor Training
Shop Online
Enroll Now
Buy Wholesale and maintain an Active status for 2 months and we will refund your $39 Distributor Fee
Friends
Buy Wholesale and maintain an Active status for 2 months and we will refund your $39 Distributor Fee
14-September-2008 12:50:34 - Artificial intelligence Redirected from AI AI redirects here. For the other uses, see AI disambiguation. Garry Kasparov playing against Deep Blue, the first machine to win a chess match against a world champion. Garry Kasparov playing against Deep Blue, the first machine to win a chess match against a world champion. Artificial intelligence AI is the intelligence of machines and the branch of computer science which aims to create it. Major AI textbooks define artificial intelligence as the study and design of intelligent agents,1 where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.2 John McCarthy, who coined the term in 1956,3 defines it as the science and engineering of making intelligent machines.4 Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.5 General intelligence or strong AI has not yet been achieved and is a long-term goal of some AI research.6 AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, ontology, operations research, economics, control theory, probability, optimization and logic.7 AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.8 Other names for the field have been proposed, such as computational intelligence,9 synthetic intelligence,9 intelligent systems,10 or computational rationality.11 These alternative names are sometimes used to set oneself apart from the part of AI dealing with symbols considered outdated by many, see GOFAI which is often associated with the term AI itself. Artificial intelligence portal Contents 1 Perspectives on AI 1.1 AI in myth, fiction and speculation 1.2 History of AI research 1.3 Philosophy of AI 2 AI research 2.1 Problems of AI 2.1.1 Deduction, reasoning, problem solving 2.1.2 Knowledge representation 2.1.3 Planning 2.1.4 Learning 2.1.5 Natural language processing 2.1.6 Motion and manipulation 2.1.7 Perception 2.1.8 Social intelligence 2.1.9 Creativity 2.1.10 General intelligence 2.2 Approaches to AI 2.2.1 Cybernetics and brain simulation 2.2.2 Traditional symbolic AI 2.2.3 Sub-symbolic AI 2.2.4 Intelligent agent paradigm 2.2.5 Integrating the approaches 2.3 Tools of AI research 2.3.1 Search and optimization 2.3.2 Logic 2.3.3 Probabilistic methods for uncertain reasoning 2.3.4 Classifiers and statistical learning methods 2.3.5 Neural networks 2.3.6 Control theory 2.3.7 Specialized languages 2.4 Evaluating artificial intelligence 2.5 Competitions and prizes 3 Applications of artificial intelligence 4 See also 5 Notes 6 References 6.1 Major AI textbooks 6.2 History of AI 6.3 Other sources 7 Further reading 8 External links Perspectives on AI AI in myth, fiction and speculation Main articles: artificial intelligence in fiction, ethics of artificial intelligence, transhumanism, and Technological singularity Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the golden robots of Hephaestus and Pygmalion's Galatea.12 Human likenesses believed to have intelligence were built in every civilization, beginning with the sacred statues worshipped in Egypt and Greece,1314 and including the machines of Yan Shi,15 Hero of Alexandria,16 Al-Jazari17 or Wolfgang von Kempelen.18 It was widely believed that artificial beings had been created by Geber,19 Judah Loew20 and Paracelsus.21 Stories of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by artificial intelligence.22 Mary Shelley's Frankenstein,23 inspired in part by the legend of Paracelsus, considers a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human being? The idea also appears in modern science fiction: the film Artificial Intelligence: A.I. considers a machine in the form of a small boy which has been given the ability to feel human emotions, including, tragically, the capacity to suffer. This issue, now known as robot rights, is also being considered by futurists, such as California's Institute for the Future,24 although many critics believe that the discussion is premature.25 Another issue explored by both science fiction writers and futurists is the impact of artificial intelligence on society. In fiction, AI has appeared as a servant R2D2 in Star Wars, a comrade Lt. Commander Data in Star Trek, an extension to human abilities Ghost in the Shell, a conqueror The Matrix, a dictator With Folded Hands and an exterminator Terminator, Battlestar Galactica. Academic sources have considered such consequences as: a decreased demand for human labor;26 the enhancement of human ability or experience;27 and a need for redefinition of human identity and basic values.28 Several futurists argue that artificial intelligence will transcend the limits of progress and fundamentally transform humanity. Ray Kurzweil has used Moore's law which describes the relentless exponential improvement in digital technology with uncanny accuracy to calculate that desktop computers will have the same processing power as human brains by the year 2029, and that by 2045 artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything conceivable in the past, a scenario that science fiction writer Vernor Vinge named the technological singularity.27 Edward Fredkin argues that artificial intelligence is the next stage in evolution,29 an idea first proposed by Samuel Butler's Darwin Among the Machines 1863, and expanded upon by George Dyson in his book of the same name in 1998. Several futurists and science fiction writers have predicted that human beings and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, which has roots in Aldous Huxley and Robert Ettinger, is now associated with robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil.27 Transhumanism has been illustrated in fiction as well, for example on the manga Ghost in the Shell. Pamela McCorduck believes that these scenarios are expressions of an ancient human desire to, as she calls it, forge the gods.22 History of AI research Main articles: history of artificial intelligence and timeline of artificial intelligence In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries in neurology, a new mathematical theory of information, an understanding of control and stability called cybernetics, and above all, by the invention of the digital computer, a machine based on the abstract essence of mathematical reasoning.30 The field of modern AI research was founded at conference on the campus of Dartmouth College in the summer of 1956.31 Those who attended would become the leaders of AI research for many decades, especially John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, who founded AI laboratories at MIT, CMU and Stanford. They and their students wrote programs that were, to most people, simply astonishing:32 computers were solving word problems in algebra, proving logical theorems and speaking English.33 By the middle 60s their research was heavily funded by the U.S. Department of Defense34 and they were optimistic about the future of the new field: 1965, H. A. Simon: Machines will be capable, within twenty years, of doing any work a man can do35 1967, Marvin Minsky: Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved.36 These predictions, and many like them, would not come true. They had failed to recognize the difficulty of some of the problems they faced.37 In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. This was the first AI Winter.38 In the early 80s, AI research was revived by the commercial success of expert systems39 a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached more than a billion dollars and governments around the world poured money back into the field.40 However, just a few years later, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, more lasting AI Winter began.41 In the 90s and early 21st century AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for logistics, data mining, medical diagnosis and many other areas.42 The success was due to several factors: the incredible power of computers today see Moore's law, a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.43 Philosophy of AI Mind and Brain portal Main article: philosophy of artificial intelligence Artificial intelligence, by claiming to be able to recreate the capabilities of the human mind, is a both challenge and an insipiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine have a mind and consciousness? A few of the most influential answers to these questions are given below.44 Turing's polite convention: If a machine acts as intelligently as a human being, then it is as intelligent as a human being. Alan Turing theorized that, ultimately, we can only judge the intelligence of machine based on its behavior. This theory forms the basis of the Turing test.45 The Dartmouth proposal: Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it. This assertion was printed in the proposal for the Dartmouth Conference of 1956, and represents the position of most working AI researchers.46 Newell and Simon's physical symbol system hypothesis: A physical symbol system has the necessary and sufficient means of general intelligent action. This statement claims that the essence of intelligence is symbol manipulation.47 Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a feel for the situation rather than explicit symbolic knowledge.4849 Gödel's incompleteness theorem: A formal system such as a computer program can not prove all true statements. Roger Penrose is among those who claim that Gödel's theorem limits what machines can do.5051 Searle's strong AI hypothesis: The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.52 Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the mind might be.53 The artificial brain argument: The brain can be simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially identical to the original. This argument combines the idea that a suitably powerful machine can simulate any process, with the materialist idea that the mind is the result of physical processes in the brain.54 AI research Problems of AI While there is no universally accepted definition of intelligence,55 AI researchers have studied several traits that are considered essential.5 Deduction, reasoning, problem solving Early AI researchers developed algorithms that imitated the process of conscious, step-by-step reasoning that human beings use when they solve puzzles, play board games, or make logical deductions.56 By the late 80s and 90s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.57 For difficult problems, most of these algorithms can require enormous computational resources - most experience a combinatorial explosion: the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem solving algorithms is a high priority for AI research.58 It is not clear, however, that conscious human reasoning is any more efficient when faced with a difficult abstract problem. Cognitive scientists have demonstrated that human beings solve most of their problems using unconscious reasoning, rather than the conscious, step-by-step deduction that early AI research was able to model.59 Embodied cognitive science argues that unconscious sensorimotor skills are essential to our problem solving abilities. It is hoped that sub-symbolic methods, like computational intelligence and situated AI, will be able to model these instinctive skills. The problem of unconscious problem solving, which forms part of our commonsense reasoning, is largely unsolveddubious - discuss. Knowledge representation Main articles: knowledge representation and commonsense knowledge Knowledge representation60 and knowledge engineering61 are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects;62 situations, events, states and time;63 causes and effects;64 knowledge about knowledge what we know about what other people know;65 and many other, less well researched domains. A complete representation of what exists is an ontology66 borrowing a word from traditional philosophy, of which the most general are called upper ontologies. Among the most difficult problems in knowledge representation are: Default reasoning and the qualification problem: Many of the things people know take the form of working assumptions. For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about birds in general. John McCarthy identified this problem in 196967 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.68 Unconscious knowledge: Much of what people know isn't represented as facts or statements that they could actually say out loud. They take the form of intuitions or tendencies and are represented in the brain unconsciously and sub-symbolically. This unconscious knowledge informs, supports and provides a context for our conscious knowledge. As with the related problem of unconscious reasoning, it is hoped that situated AI or computational intelligence will provide ways to represent this kind of knowledge. The breadth of common sense knowledge: The number of atomic facts that the average person knows is astronomical. Research projects that attempt to build a complete knowledge base of commonsense knowledge, such as Cyc, require enormous amounts of tedious step-by-step ontological engineering - they must be built, by hand, one complicated concept at a time.69 Planning Main article: automated planning and scheduling Intelligent agents must be able to set goals and achieve them.70 They need a way to visualize the future: they must have a representation of the state of the world and be able to make predictions about how their actions will change it. They must also attempt to determine the utility or value of the choices available to it.71 In some planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be.72 However, if this is not true, it must periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.73 Multi-agent planning tries to determine the best plan for a community of agents, using cooperation and competition to achieve a given goal. Emergent behavior such as this is used by both evolutionary algorithms and swarm intelligence.74 Learning Main article: machine learning Important machine learning75 problems are: Unsupervised learning: find a model that matches a stream of input experiences, and be able to predict what new experiences to expect. Supervised learning, such as classification be able to determine what category something belongs in, after seeing a number of examples of things from each category, or regression given a set of numerical input/output examples, discover a continuous function that would generate the outputs from the inputs. Reinforcement learning:76the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Natural language processing Main article: natural language processing Natural language processing77 gives machines the ability to read and understand the languages human beings speak. Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing include information retrieval or text mining and machine translation.78 Motion and manipulation ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs. ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs. Main article: robotics The field of robotics79 is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation80 and navigation, with sub-problems of localization knowing where you are, mapping learning what is around you and motion planning figuring out how to get there.81 Perception Main articles: machine perception, computer vision, and speech recognition Machine perception82 is the ability to use input from sensors such as cameras, microphones, sonar and others more exotic to deduce aspects of the world. Computer vision83 is the ability to analyze visual input. A few selected subproblems are speech recognition,84 facial recognition and object recognition.85 Social intelligence Main article: affective computing Kismet, a robot with rudimentary social skills. Kismet, a robot with rudimentary social skills. Emotion and social skills play two roles for an intelligent agent:86 It must be able to predict the actions of others, by understanding their motives and emotional states. This involves elements of game theory, decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions. For good human-computer interaction, an intelligent machine also needs to display emotions - at the very least it must appear polite and sensitive to the humans it interacts with. At best, it should appear to have normal emotions itself. Creativity Main article: computational creativity A sub-field of AI addresses creativity both theoretically from a philosophical and psychological perspective and practically via specific implementations of systems that generate outputs that can be considered creative. General intelligence Main articles: strong AI and AI-complete Most researchers hope that their work will eventually be incorporated into a machine with general intelligence known as strong AI, combining all the skills above and exceeding human abilities at most or all of them.6 A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project. Many of the problems above are considered AI-complete: to solve one problem, you must solve them all. For example, even a straightforward, specific task like machine translation requires that the machine follow the author's argument reason, know what it's talking about knowledge, and faithfully reproduce the author's intention social intelligence. Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well as humans can do it.87 Approaches to AI Artificial intelligence is a young science and there is still no established unifying theory. The field is fragmented88 and research communities have grown around different approaches. Cybernetics and brain simulation The human brain provides inspiration for artificial intelligence researchers, however there is no consensus on how closely it should be simulated. The human brain provides inspiration for artificial intelligence researchers, however there is no consensus on how closely it should be simulated. In the 40s and 50s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton and the Ratio Club in England.30 Traditional symbolic AI When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: CMU, Stanford and MIT, and each one developed its own style of research. John Haugeland named these approaches to AI good old fashioned AI or GOFAI.89 Cognitive simulation Economist Herbert Simon and Alan Newell studied human problem solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team performed psychological experiments to demonstrate the similarities between human problem solving and the programs such as their General Problem Solver they were developing. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 80s.9091 Logical AI Unlike Newell and Simon, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms.92 His laboratory at Stanford SAIL focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.93 Logic was also focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.94 Scruffy symbolic AI Researchers at MIT such as Marvin Minsky and Seymour Papert found that solving difficult problems in vision and natural language processing required ad-hoc solutions - they argued that there was no simple and general principle like logic that would capture all the aspects of intelligent behavior. Roger Schank described their anti-logic approaches as scruffy as opposed to the neat paradigms at CMU and Stanford,9596 and this still forms the basis of research into commonsense knowledge bases such as Doug Lenat's Cyc which must be built one complicated concept at a time.97 Knowledge based AI When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.98 This knowledge revolution led to the development and deployment of expert systems introduced by Edward Feigenbaum, the first truly successful form of AI software.39 The knowledge revolution was also driven by the realization that truly enormous amounts of knowledge would be required by many simple AI applications. Sub-symbolic AI During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or neural networks were abandoned or pushed into the background.99 By the 1980s, however, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into sub-symbolic approaches to specific AI problems.100 Bottom-up, situated, behavior based or nouvelle AI Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focussed on the basic engineering problems that would allow robots to move and survive.101 Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 50s and reintroduced the use of control theory in AI. These approaches are also conceptually related to the embodied mind thesis. Computational Intelligence Interest in neural networks and connectionism was revived by David Rumelhart and others in the middle 1980s.102 These and other sub-symbolic approaches, such as fuzzy systems and evolutionary computation, are now studied collectively by the emerging discipline of computational intelligence.103 Formalisation In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields like mathematics, economics or operations research. Russell Norvig 2003 describe this movement as nothing less than a revolution and the victory of the neats.43 Intelligent agent paradigm The intelligent agent paradigm became widely accepted during the 1990s.104 An intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success. The simplest intelligent agents are programs that solve specific problems. The most complicated intelligent agents are rational, thinking human beings.105 The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works - some agents are symbolic and logical, some are sub-symbolic neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields-such as decision theory and economics-that also use concepts of abstract agents. Integrating the approaches An agent architecture or cognitive architecture allows researchers to build more versatile and intelligent systems out of interacting intelligent agents in a multi-agent system.106 A system with both symbolic and sub-symbolic components is a hybrid intelligent system, and the study of such systems is artificial intelligence systems integration. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling.107 Rodney Brooks' subsumption architecture was an early proposal for such a hierarchical system. Tools of AI research In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below. Search and optimization Main articles: search algorithm, optimization mathematics, and evolutionary computation Many problems in AI can be solved in theory by intelligently searching through many possible solutions:108 Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.109 Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.110 Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.80 Many learning algorithms use search algorithms based on optimization. Simple exhaustive searches111 are rarely sufficient for most real world problems: the search space the number of places to search quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use heuristics or rules of thumb that eliminate choices that are unlikely to lead to the goal called pruning the search tree. Heuristics supply the program with a best guess for what path the solution lies on.112 A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.113 Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms the guesses and then allow them to mutate and recombine, selecting only the fittest to survive each generation refining the guesses. Forms of evolutionary computation include swarm intelligence algorithms such as ant colony or particle swarm optimization114 and evolutionary algorithms such as genetic algorithms115 and genetic programming116117. Logic Main articles: logic programming and automated reasoning Logic118 was introduced into AI research by John McCarthy in his 1958 Advice Taker proposal. The most important technical development was J. Alan Robinson's discovery of the resolution and unification algorithm for logical deduction in 1963. This procedure is simple, complete and entirely algorithmic, and can easily be performed by digital computers.119 However, a naive implementation of the algorithm quickly leads to a combinatorial explosion or an infinite loop. In 1974, Robert Kowalski suggested representing logical expressions as Horn clauses statements in the form of rules: if p then q, which reduced logical deduction to backward chaining or forward chaining. This greatly alleviated but did not eliminate the problem.109120 Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning,121 and inductive logic programming is a method for learning.122 There are several different forms of logic used in AI research. Propositional or sentential logic123 is the logic of statements which can be true or false. First-order logic124 also allows the use of quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy logic, a version of first-order logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True 1 or False 0. Fuzzy systems can be used for uncertain reasoning and have been widely used in modern industrial and consumer product control systems.125 Default logics, non-monotonic logics and circumscription are forms of logic designed to help with default reasoning and the qualification problem.68 Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;62 situation calculus, event calculus and fluent calculus for representing events and time;63 causal calculus;64 belief calculus; and modal logics.65 Probabilistic methods for uncertain reasoning Main articles: Bayesian network, hidden Markov model, Kalman filter, decision theory, and utility theory Many problems in AI in reasoning, planning, learning, perception and robotics require the agent to operate with incomplete or uncertain information. Starting in the late 80s and early 90s, Judea Pearl and others championed the use of methods drawn from probability theory and economics to devise a number of powerful tools to solve these problems.126127 Bayesian networks128 are very general tool that can be used for a large number of problems: reasoning using the Bayesian inference algorithm,129 learning using the expectation-maximization algorithm,130 planning using decision networks131 and perception using dynamic Bayesian networks.132 Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time133 e.g., hidden Markov models134 and Kalman filters135. A key concept from the science of economics is utility: a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,136 information value theory.71 These tools include models such as Markov decision processes,137 dynamic decision networks,137 game theory and mechanism design138 Classifiers and statistical learning methods Main articles: classifier mathematics, statistical classification, and machine learning The simplest AI applications can be divided into two types: classifiers if shiny then diamond and controllers if shiny then pick up. Controllers do however also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers139 are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience. A classifier can be trained in various ways; there are many statistical and machine learning approaches. A wide range of classifiers are available, each with its strengths and weaknesses. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the no free lunch theorem. Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than science. The most widely used classifiers are the neural network,140 kernel methods such as the support vector machine,141 k-nearest neighbor algorithm,142 Gaussian mixture model,143 naive Bayes classifier,144 and decision tree.145 The performance of these classifiers have been compared over a wide range of classification tasks146 in order to find data characteristics that determine classifier performance. Neural networks Main articles: neural networks and connectionism A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain. A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain. The study of artificial neural networks140 began in the decade before the field AI research was founded. In the 1960s Frank Rosenblatt developed an important early version, the perceptron.147 Paul Werbos developed the backpropagation algorithm for multilayer perceptrons in 1974,148 which led to a renaissance in neural network research and connectionism in general in the middle 1980s. The Hopfield net, a form of attractor network, was first described by John Hopfield in 1982. Common network architectures which have been developed include the feedforward neural network, the radial basis network, the Kohonen self-organizing map and various recurrent neural networks.citation needed Neural networks are applied to the problem of learning, using such techniques as Hebbian learning, competitive learning149 and the relatively new field of Hierarchical Temporal Memory which simulates the architecture of the neocortex.150 Control theory Main article: intelligent control Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.151 Specialized languages AI researchers have developed several specialized languages for AI research: IPL152, includes features intended to support programs that could perform general problem solving, including lists, associations, schemas frames, dynamic memory allocation, data types, recursion, associative retrieval, functions as arguments, generators streams, and cooperative multitasking. Lisp153154 is a practical mathematical notation for computer programs based on lambda calculus. Linked lists are one of Lisp languages' major data structures, and Lisp source code is itself made up of lists. As a result, Lisp programs can manipulate source code as a data structure, giving rise to the macro systems that allow programmers to create new syntax or even new domain-specific programming languages embedded in Lisp. There are many dialects of Lisp in use today. Prolog,155120 is a declarative language where programs are expressed in terms of relations, and execution occurs by running queries over these relations. Prolog is particularly useful for symbolic reasoning, database and language parsing applications. Prolog is widely used in AI today. STRIPS, a language for expressing automated planning problem instances. It expresses an initial state, the goal states, and a set of actions. For each action preconditions what must be established before the action is performed and postconditions what is established after the action is performed are specified. Planner is a hybrid between procedural and logical languages. It gives a procedural interpretation to logical sentences where implications are interpreted with pattern-directed inference. AI applications are also often written in standard languages like C++ and languages designed for mathematics, such as Matlab and Lush. Evaluating artificial intelligence Main article: Progress in artificial intelligence How can one determine if an agent is intelligent? In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail. Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results. The broad classes of outcome for an AI test are: optimal: it is not possible to perform better strong super-human: performs better than all humans super-human: performs better than most humans sub-human: performs worse than most humans For example, performance at checkers draughts is optimal,156 performance at chess is super-human and nearing strong super-human,157 and performance at many everyday tasks performed by humans is sub-human. Competitions and prizes Main article: Competitions and prizes in artificial intelligence There are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machine intelligence, conversational behaviour, data-mining, driverless cars, robot soccer and games. Applications of artificial intelligence Main article: Applications of artificial intelligence Artificial intelligence has successfully been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery and toys. Frequently, when a technique reaches mainstream use it is no longer considered artificial intelligence, sometimes described as the AI effect.158 It may also become integrated into artificial life. See also List of basic artificial intelligence topics List of AI researchers List of AI projects List of important AI publications List of emerging technologies Notes ^ Poole, Mackworth Goebel 1998, p. 1 who use the term computational intelligence as a synonym for artificial intelligence. Other textbooks that define AI this way include Nilsson 1998, and Russell Norvig 2003 who prefer the term rational agent and write The whole-agent view is now widely accepted in the field Russell Norvig 2003, p. 55 ^ This definition, in terms of goals, actions, perception and environment, is due to Russell Norvig 2003. Other definitions also include knowledge and learning as additional criteria. See also Abstract Intelligent Agents: Paradigms, Foundations and Conceptualization Problems, A.M. Gadomski, J.M. Zytkow, in Abstract Intelligent Agent, 2. Printed by ENEA, Rome 1995, ISSN/1120-558X ^ Although there is some controversy on this point see Crevier 1993, p. 50, McCarthy states unequivocally I came up with the term in a c|net interview. See Getting Machines to Think Like Us. ^ See John McCarthy, What is Artificial Intelligence? ^ a b This list of intelligent traits is based on the topics covered by the major AI textbooks, including: Russell Norvig 2003, Luger Stubblefield 2004, Poole, Mackworth Goebel 1998 and Nilsson 1998. ^ a b General intelligence strong AI is discussed by popular introductions to AI, such as: Kurzweil 1999 and Kurzweil 2005 ^ Russell Norvig 2003, pp. 5-16 ^ See AI Topics: applications ^ a b Poole, Mackworth Goebel 1998, p. 1 ^ The name of the journal Intelligent Systems ^ Russell Norvig 2003, p. 17 ^ AI in Myth: McCorduck 2004, p. 4-5 Russell Norvig 2003, p. 939 ^ Sacred statues as artificial intelligence: Crevier 1993, p. 1 statue of Amun McCorduck 2004, pp. 6-9 ^ These were the first machines to be believed to have true intelligence and consciousness. Hermes Trismegistus expressed the common belief that with these statues, craftsman had reproduced the true nature of the gods, their sensus and spiritus. McCorduck makes the connection between sacred automatons and Mosaic law developed around the same time, which expressly forbids the worship of robots McCorduck 2004, pp. 6-9 ^ Needham 1986, p. 53 ^ McCorduck 2004, p. 6 ^ A Thirteenth Century Programmable Robot ^ McCorduck 2004, p. 17 ^ Takwin: O'Connor, Kathleen Malone 1994. The alchemical creation of life takwin and other concepts of Genesis in medieval Islam. University of Pennsylvania. Retrieved on 2007-01-10. ^ Golem: McCorduck 2004, p. 15-16, Buchanan 2005, p. 50 ^ McCorduck 2004, p. 13-14 ^ a b This is a central idea of Pamela McCorduck's Machines That Think. She writes: I like to think of artificial intelligence as the scientific apotheosis of a veneralbe cultural tradition. McCorduck 2004, p. 34 Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized. McCorduck 2004, p. xviii Our history is full of attempts-nutty, eerie, comical, earnest, legendary and real-to make artificial intelligences, to repreduce what is the essential us-bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction. McCorduck 2004, p. 3 She traces the desire back to its Hellenistic roots and calls it the urge to forge the Gods. McCorduck 2004, p. 340-400 ^ McCorduck 2004, p. 190-25 discusses Frankenstein and identifies the key ethical issues as scientific hubris and the suffering of the monster, i.e. robot rights. ^ Robot rights: Russell Norvig 2003, p. 964 Robots could demand legal rights ^ See the Times Online, Human rights for robots? We're getting carried away ^ Russell Norvig 2003, p. 960-961 ^ a b c Singularity, transhumanism: Kurzweil 2005 Russell Norvig 2003, p. 963 ^ Joseph Weizenbaum's critique of AI: Weizenbaum 1976 Crevier 1993, pp. 132-144 McCorduck 2004, pp. 356-373 Russell Norvig 2003, p. 961 Weizenbaum the AI researcher who developed the first chatterbot program, ELIZA argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. ^ Quoted in McCorduck 2004, p. 401 ^ a b AI's immediate precursors: McCorduck 2004, pp. 51-107 Crevier 1993, pp. 27-32 Russell Norvig 2003, pp. 15,940 Moravec 1988, p. 3 Among the researchers who laid the foundations of the theory of computation, cybernetics, information theory and neural networks were Alan Turing, John Von Neumann, Norbert Weiner, Claude Shannon, Warren McCullough, Walter Pitts and Donald Hebb ^ Dartmouth conference: McCorduck, pp. 111-136 Crevier 1993, pp. 47-49 Russell Norvig 2003, p. 17 NRC 1999, pp. 200-201 ^ Russell and Norvig write it was astonishing whenever a computer did anything kind of smartish. Russell Norvig 2003, p. 18 ^ Golden years of AI successful symbolic reasoning programs 1956-1973: McCorduck, pp. 243-252 Crevier 1993, pp. 52-107 Moravec 1988, p. 9 Russell Norvig 2003, p. 18-21 The programs described are Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. ^ DARPA pours money into undirected pure research into AI during the 1960s: McCorduck 2005, pp. 131 Crevier 1993, pp. 51, 64-65 NRC 1999, pp. 204-205 ^ Simon 1965, p. 96 quoted in Crevier 1993, p. 109 ^ Minsky 1967, p. 2 quoted in Crevier 1993, p. 109 ^ See History of artificial intelligence - the problems. ^ First AI Winter: Crevier 1993, pp. 115-117 Russell Norvig 2003, p. 22 NRC 1999, pp. 212-213 Howe 1994 ^ a b Expert systems: ACM 1998, I.2.1, Russell Norvig 2003, pp. 22-24 Luger Stubblefield 2004, pp. 227-331, Nilsson 1998, chpt. 17.4 McCorduck 2004, pp. 327-335, 434-435 Crevier 1993, pp. 145-62, 197-203 ^ Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: McCorduck 2004, pp. 426-441 Crevier 1993, pp. 161-162,197-203, 211, 240 Russell Norvig 2003, p. 24 NRC 1999, pp. 210-211 ^ Second AI Winter: McCorduck 2004, pp. 430-435 Crevier 1993, pp. 209-210 NRC 1999, pp. 214-216 ^ AI applications widely used behind the scenes: Russell Norvig 2003, p. 28 Kurzweil 2005, p. 265 NRC 1999, pp. 216-222 ^ a b Formal methods are now preferred Victory of the neats: Russell Norvig 2003, pp. 25-26 McCorduck 2004, pp. 486-487 ^ All of these positions below are mentioned in standard discussions of the subject, such as: Russell Norvig 2003, pp. 947-960 Fearn 2007, pp. 38-55 ^ Philosophical implications of the Turing test: Turing 1950, Haugeland 1985, pp. 6-9, Crevier 1993, p. 24, Russell Norvig 2003, pp. 2-3 and 948 ^ Dartmouth proposal: McCarthy et al. 1955 ^ The physical symbol systems hypothesis: Newell Simon 1976, p. 116 Russell Norvig 2003, p. 18 ^ Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the psychological assumption: The mind can be viewed as a device operating on bits of information according to formal rules. Dreyfus 1992, p. 156 ^ Dreyfus' Critique of AI: Dreyfus 1972, Dreyfus Dreyfus 1986, Russell Norvig 2003, pp. 950-952, Crevier 1993, pp. 120-132 and ^ This is a paraphrase of the important implication of Gödel's theorems. ^ The Mathematical Objection: Russell Norvig 2003, p. 949 McCorduck 2004, p. 448-449 Refuting Mathematical Objection: Turing 1950 under 2 The Mathematical Objection Hofstadter 1979, Making the Mathematical Objection: Lucas 1961, Penrose 1989. Background: Gödel 1931, Church 1936, Kleene 1935, Turing 1937, ^ This version is from Searle 1999, and is also quoted in Dennett 1991, p. 435. Searle's original formulation was The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states. Searle 1980, p. 1. Strong AI is defined similarly by Russell Norvig 2003, p. 947: The assertion that machines could possibly act intelligently or, perhaps better, act as if they were intelligent is called the 'weak AI' hypothesis by philosophers, and the assertion that machines that do so are actually thinking as opposed to simulating thinking is called the 'strong AI' hypothesis. ^ Searle's Chinese Room argument: Searle 1980, Searle 1991 Russell Norvig 2003, pp. 958-960 McCorduck 2004, pp. 443-445 Crevier 1993, pp. 269-271 ^ Artificial brain: Moravec 1988 Kurzweil 2005, p. 262 Russell Norvig, p. 957 Crevier 1993, pp. 271 and 279 The most extreme form of this argument the brain replacement scenario was put forward by Clark Glymour in the mid-70s and was touched on by Zenon Pylyshyn and John Searle in 1980. Daniel Dennett sees human consciousness as multiple functional thought patterns; see Consciousness Explained. ^ We cannot yet characterize in general what kinds of computational procedures we want to call intelligent. John McCarthy, Basic Questions ^ Problem solving, puzzle solving, game playing and deduction: Russell Norvig 2003, chpt. 3-9, Poole et al. chpt. 2,3,7,9, Luger Stubblefield 2004, chpt. 3,4,6,8, Nilsson, chpt. 7-12. ^ Uncertain reasoning: Russell Norvig 2003, pp. 452-644, Poole, Mackworth Goebel 1998, pp. 345-395, Luger Stubblefield 2004, pp. 333-381, Nilsson 1998, chpt. 19 ^ Intractability and efficiency and the combinatorial explosion: Russell Norvig 2003, pp. 9, 21-22 ^ Several famous examples: Wason 1966 showed that people do poorly on completely abstract problems, but if the problem is restated to allowed the use of intuitive social intelligence, performance dramatically improves. See Wason selection task Tversky, Slovic Kahnemann 1982 have shown that people are terrible at elementary problems that involve uncertain reasoning. See list of cognitive biases for several examples. Lakoff Núñez 2000 have controversially argued that even our skills at mathematics depend on knowledge and skills that come from the body, i.e. sensorimotor and perceptual skills. See Where Mathematics Comes From ^ Knowledge representation: ACM 1998, I.2.4, Russell Norvig 2003, pp. 320-363, Poole, Mackworth Goebel 1998, pp. 23-46, 69-81, 169-196, 235-277, 281-298, 319-345, Luger Stubblefield 2004, pp. 227-243, Nilsson 1998, chpt. 18 ^ Knowledge engineering: Russell Norvig 2003, pp. 260-266, Poole, Mackworth Goebel 1998, pp. 199-233, Nilsson 1998, chpt. ~17.1-17.4 ^ a b Representing categories and relations: Semantic networks, description logics, inheritance including frames and scripts: Russell Norvig 2003, pp. 349-354, Poole, Mackworth Goebel 1998, pp. 174-177, Luger Stubblefield 2004, pp. 248-258, Nilsson 1998, chpt. 18.3 ^ a b Representing events and time:Situation calculus, event calculus, fluent calculus including solving the frame problem: Russell Norvig 2003, pp. 328-341, Poole, Mackworth Goebel 1998, pp. 281-298, Nilsson 1998, chpt. 18.2 ^ a b Causal calculus: Poole, Mackworth Goebel 1998, pp. 335-337 ^ a b Representing knowledge about knowledge: Belief calculus, modal logics: Russell Norvig 2003, pp. 341-344, Poole, Mackworth Goebel 1998, pp. 275-277 ^ Ontology: Russell Norvig 2003, pp. 320-328 ^ McCarthy Hayes 1969 ^ a b Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction Poole et al. places abduction under default reasoning. Luger et al. places this under uncertain reasoning: Russell Norvig 2003, pp. 354-360, Poole, Mackworth Goebel 1998, pp. 248-256, 323-335, Luger Stubblefield 2004, pp. 335-363, Nilsson 1998, ~18.3.3 ^ Breadth of commonsense knowledge: Russell Norvig 2003, p. 21, Crevier 1993, pp. 113-114, Moravec 1988, p. 13, Lenat Guha 1989 Introduction ^ Planning: ACM 1998, ~I.2.8, Russell Norvig 2003, pp. 375-459, Poole, Mackworth Goebel 1998, pp. 281-316, Luger Stubblefield 2004, pp. 314-329, Nilsson 1998, chpt. 10.1-2, 22 ^ a b Information value theory: Russell Norvig 2003, pp. 600-604 ^ Classical planning: Russell Norvig 2003, pp. 375-430, Poole, Mackworth Goebel 1998, pp. 281-315, Luger Stubblefield 2004, pp. 314-329, Nilsson 1998, chpt. 10.1-2, 22 ^ Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: Russell Norvig 2003, pp. 430-449 ^ Multi-agent planning and emergent behavior: Russell Norvig 2003, pp. 449-455 ^ Learning: ACM 1998, I.2.6, Russell Norvig 2003, pp. 649-788, Poole, Mackworth Goebel 1998, pp. 397-438, Luger Stubblefield 2004, pp. 385-542, Nilsson 1998, chpt. 3.3 , 10.3, 17.5, 20 ^ Reinforcement learning: Russell Norvig 2003, pp. 763-788 Luger Stubblefield 2004, pp. 442-449 ^ Natural language processing: ACM 1998, I.2.7 Russell Norvig 2003, pp. 790-831 Poole, Mackworth Goebel 1998, pp. 91-104 Luger Stubblefield 2004, pp. 591-632 ^ Applications of natural language processing, including information retrieval i.e. text mining and machine translation: Russell Norvig 2003, pp. 840-857, Luger Stubblefield 2004, pp. 623-630 ^ Robotics: ACM 1998, I.2.9, Russell Norvig 2003, pp. 901-942, Poole, Mackworth Goebel 1998, pp. 443-460 ^ a b Moving and configuration space: Russell Norvig 2003, pp. 916-932 ^ Robotic mapping localization, etc: Russell Norvig 2003, pp. 908-915 ^ Machine perception: Russell Norvig 2003, pp. 537-581, 863-898, Nilsson 1998, ~chpt. 6 ^ Computer vision: ACM 1998, I.2.10 Russell Norvig 2003, pp. 863-898 Nilsson 1998, chpt. 6 ^ Speech recognition: ACM 1998, ~I.2.7 Russell Norvig 2003, pp. 568-578 ^ Object recognition: Russell Norvig 2003, pp. 885-892 ^ Emotion and affective computing: Minsky 2007 Picard 1997 ^ Shapiro 1992, p. 9 ^ Fractioning of AI into subfields: McCorduck 2004, pp. 421-425 ^ Haugeland 1985, pp. 112-117 ^ Cognitive simulation, Newell and Simon, AI at CMU then called Carnegie Tech: McCorduck 2004, pp. 139-179, 245-250, 322-323 EPAM Crevier 2004, pp. 145-149 ^ Soar history: McCorduck 2004, pp. 450-451 Crevier 1993, pp. 258-263 ^ McCarthy's opposition to cognitive simulation: Science at Google Books McCarthy's presentation at AI@50 ^ McCarthy and AI research at SAIL and SRI: McCorduck 2004, pp. 251-259 Crevier 1993, pp. CHECK ^ AI research at Edinburgh and France, birth of Prolog: Crevier 1993, pp. 193-196 Howe 1994 ^ AI at MIT under Marvin Minsky in the 1960s : McCorduck 2004, pp. 259-305 Crevier 1993, pp. 83-102, 163-176 CHECK Russell Norvig 2003, p. 19 ^ Neats vs. scruffies: McCorduck 2004, pp. 421-424, 486-489 Crevier 1993, pp. 168 ^ Cyc: McCorduck 2004, p. 489, who calls it a determinedly scruffy enterprise Crevier 1993, pp. 239-243 Russell Norvig 2003, p. 363-365 Lenat Guha 1989 ^ Knowledge revolution: McCorduck 2004, pp. 266-276, 298-300, 314, 421 Russell Norvig 2003, pp. 22-23 ^ The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. ^ Nilsson 1998, p. 7 characterizes these newer approaches to AI as sub-symbolic. ^ Embodied approaches to AI: McCorduck 2004, pp. 454-462 Brooks 1990 Moravec 1988 ^ Revival of connectionism: Crevier 1993, pp. 214-215 Russell Norvig 2003, p. 25 ^ See IEEE Computational Intelligence Society ^ The whole-agent view is now widely accepted in the field Russell Norvig 2003, p. 55. ^ The intelligent agent paradigm: Russell Norvig 2003, pp. 27, 32-58, 968-972, Poole, Mackworth Goebel 1998, pp. 7-21, Luger Stubblefield 2004, pp. 235-240 ^ Agent architectures, hybrid intelligent systems: Russell Norvig 1998, pp. 27, 932, 970-972 Nilsson 1998, chpt. 25 ^ Albus, J. S. 4-D/RCS reference model architecture for unmanned ground vehicles. In G Gerhart, R Gunderson, and C Shoemaker, ors, Proceedings of the SPIE AeroSense Session on Unmanned Ground Vehicle Technology, volume 3693, pages 11-20 ^ Search algorithms: Russell Norvig 2003, pp. 59-189 Poole, Mackworth Goebel 1998, pp. 113-163 Luger Stubblefield 2004, pp. 79-164, 193-219 Nilsson 1998, chpt. 7-12 ^ a b Forward chaining, backward chaining, Horn clauses, and logical deduction as search: Russell Norvig 2003, pp. 217-225, 280-294 Poole, Mackworth Goebel 1998, pp. ~46-52 Luger Stubblefield 2004, pp. 62-73 Nilsson 1998, chpt. 4.2, 7.2 ^ State space search and planning: Russell Norvig 2003, pp. 382-387 Poole, Mackworth Goebel 1998, pp. 298-305 Nilsson 1998, chpt. 10.1-2 ^ Uninformed searches breadth first search, depth first search and general state space search: Russell Norvig 2003, pp. 59-93 Poole, Mackworth Goebel 1998, pp. 113-132 Luger Stubblefield 2004, pp. 79-121 Nilsson 1998, chpt. 8 ^ Heuristic or informed searches e.g., greedy best first and A: Russell Norvig 2003, pp. 94-109, Poole, Mackworth Goebel 1998, pp. pp. 132-147, Luger Stubblefield 2004, pp. 133-150, Nilsson 1998, chpt. 9 ^ Optimization searches: Russell Norvig 2003, pp. 110-116,120-129 Poole, Mackworth Goebel 1998, pp. 56-163 Luger Stubblefield 2004, pp. 127-133 ^ Artificial life and society based learning: Luger Stubblefield 2004, pp. 530-541 ^ Genetic algorithms for learning: Luger Stubblefield 2004, pp. 509-530, Nilsson 1998, chpt. 4.2. See also: Holland, John H. 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press. ISBN 0262581116. ^ Koza, John R. 1992. Genetic Programming. MIT Press. ^ Poli, R., Langdon, W. B., McPhee, N. F. 2008. A Field Guide to Genetic Programming. Lulu.com, freely available from http://www.gp-field-guide.org.uk/. ISBN 978-1-4092-0073-4. ^ Logic: ACM 1998, ~I.2.3, Russell Norvig 2003, pp. 194-310, Luger Stubblefield 2004, pp. 35-77, Nilsson 1998, chpt. 13-16 ^ Resolution and unification: Russell Norvig 2003, pp. 213-217, 275-280, 295-306, Poole, Mackworth Goebel 1998, pp. 56-58, Luger Stubblefield 2004, pp. 554-575, Nilsson 1998, chpt. 14 16 ^ a b History of logic programming: Crevier 1993, pp. 190-196. Howe 1994 Advice Taker: McCorduck 2004, p. 51, Russell Norvig 2003, pp. 19 ^ Satplan: Russell Norvig 2003, pp. 402-407, Poole, Mackworth Goebel 1998, pp. 300-301, Nilsson 1998, chpt. 21 ^ Explanation based learning, relevance based learning, inductive logic programming, case based reasoning: Russell Norvig 2003, pp. 678-710, Poole, Mackworth Goebel 1998, pp. 414-416, Luger Stubblefield 2004, pp. ~422-442, Nilsson 1998, chpt. 10.3, 17.5 ^ Propositional logic: Russell Norvig 2003, pp. 204-233, Luger Stubblefield 2004, pp. 45-50 Nilsson 1998, chpt. 13 ^ First-order logic and features such as equality: ACM 1998, ~I.2.4, Russell Norvig 2003, pp. 240-310, Poole, Mackworth Goebel 1998, pp. 268-275, Luger Stubblefield 2004, pp. 50-62, Nilsson 1998, chpt. 15 ^ Fuzzy logic: Russell Norvig 2003, pp. 526-527 ^ Judea Pearl's contribution to AI: Russell Norvig 2003, pp. 25-26 ^ Stochastic methods for uncertain reasoning: ACM 1998, ~I.2.3, Russell Norvig 2003, pp. 462-644, Poole, Mackworth Goebel 1998, pp. 345-395, Luger Stubblefield 2004, pp. 165-191, 333-381, Nilsson 1998, chpt. 19 ^ Bayesian networks: Russell Norvig 2003, pp. 492-523, Poole, Mackworth Goebel 1998, pp. 361-381, Luger Stubblefield 2004, pp. ~182-190, ~363-379, Nilsson 1998, chpt. 19.3-4 ^ Bayesian inference algorithm: Russell Norvig 2003, pp. 504-519, Poole, Mackworth Goebel 1998, pp. 361-381, Luger Stubblefield 2004, pp. ~363-379, Nilsson 1998, chpt. 19.4 7 ^ Bayesian learning and the expectation-maximization algorithm: Russell Norvig 2003, pp. 712-724, Poole, Mackworth Goebel 1998, pp. 424-433, Nilsson 1998, chpt. 20 ^ Bayesian decision networks: Russell Norvig 2003, pp. 597-600 ^ Dynamic Bayesian network: Russell Norvig 2003, pp. 551-557 ^ Stochastic temporal models: Russell Norvig 2003, pp. 537-581 ^ Hidden Markov model: Russell Norvig 2003, pp. 549-551 ^ Kalman filter: Russell Norvig 2003, pp. 551-557 ^ decision theory and decision analysis: Russell Norvig 2003, pp. 584-597, Poole, Mackworth Goebel 1998, pp. 381-394 ^ a b Markov decision processes and dynamic decision networks: Russell Norvig 2003, pp. 613-631 ^ Game theory and mechanism design: Russell Norvig 2003, pp. 631-643 ^ Statistical learning methods and classifiers: Russell Norvig 2003, pp. 712-754, Luger Stubblefield 2004, pp. 453-541 ^ a b Neural networks and connectionism: Russell Norvig 2003, pp. 736-748, Poole, Mackworth Goebel 1998, pp. 408-414, Luger Stubblefield 2004, pp. 453-505, Nilsson 1998, chpt. 3 ^ Kernel methods: Russell Norvig 2003, pp. 749-752 ^ K-nearest neighbor algorithm: Russell Norvig 2003, pp. 733-736 ^ Gaussian mixture model: Russell Norvig 2003, pp. 725-727 ^ Naive Bayes classifier: Russell Norvig 2003, pp. 718 ^ Decision tree: Russell Norvig 2003, pp. 653-664, Poole, Mackworth Goebel 1998, pp. 403-408, Luger Stubblefield 2004, pp. 408-417 ^ van der Walt, Christiaan. Data characteristics that determine classifier performance. ^ Perceptrons: Russell Norvig 2003, pp. 740-743, Luger Stubblefield 2004, pp. 458-467 ^ Backpropagation: Russell Norvig 2003, pp. 744-748, Luger Stubblefield 2004, pp. 467-474, Nilsson 1998, chpt. 3.3 ^ Competitive learning, Hebbian coincidence learning, Hopfield networks and attractor networks: Luger Stubblefield 2004, pp. 474-505. ^ Hawkins Blakeslee 2004 ^ Control theory: ACM 1998, ~I.2.8, Russell Norvig 2003, pp. 926-932 ^ Crevier 1993, p. 46-48 ^ Lisp: Luger Stubblefield 2004, pp. 723-821 ^ Crevier 1993, pp. 59-62, Russell Norvig 2003, p. 18 ^ Prolog: Poole, Mackworth Goebel 1998, pp. 477-491, Luger Stubblefield 2004, pp. 641-676, 575-581 ^ Schaeffer, Jonathan 2007-07-19. Checkers Is Solved. Science. Retrieved on 2007-07-20. ^ Computer Chess#Computers versus humans ^ AI set to exceed human brain power web article. CNN.com 2006-07-26. Retrieved on 2008-02-26. References Major AI textbooks Luger, George Stubblefield, William 2004, Artificial Intelligence: Structures and Strategies for Complex Problem Solving 5th ed., The Benjamin/Cummings Publishing Company, Inc., pp. 720, ISBN 0-8053-4780-1, http://www.cs.unm.edu/~luger/ai-final/tocfull.html Nilsson, Nils 1998, Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-55860-467-4 Russell, Stuart J. Norvig, Peter 2003, Artificial Intelligence: A Modern Approach 2nd ed., Upper Saddle River, NJ: Prentice Hall, ISBN 0-13-790395-2, http://aima.cs.berkeley.edu/ Poole, David; Mackworth, Alan Goebel, Randy 1998, Computational Intelligence: A Logical Approach, Oxford University Press, http://www.cs.ubc.ca/spider/poole/ci.html History of AI Crevier, Daniel 1993, AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0-465-02997-3 McCorduck, Pamela 2004, Machines Who Think 2nd ed., Natick, MA: A. K. Peters, Ltd., ISBN 1-56881-205-1, http://www.pamelamc.com/html/machines_who_think.html Other sources ACM, Association of Computing Machinery 1998, ACM Computing Classification System: Artificial intelligence, http://www.acm.org/class/1998/I.2.html Brooks, Rodney 1990, Elephants Don't Play Chess, Robotics and Autonomous Systems 6: 3-15, doi:10.1016/S0921-88900580025-9, http://people.csail.mit.edu/brooks/papers/elephants.pdf. Retrieved on 30 August 2007 Buchanan, Bruce G. 2005, A Very Brief History of Artificial Intelligence, AI Magazine date = Winter 2005: 53-60, http://www.aaai.org/AITopics/assets/PDF/AIMag26-04-016.pdf. Retrieved on 30 August 2007 Dreyfus, Hubert 1972, What Computers Can't Do, New York: MIT Press, ISBN 0060110821 Dreyfus, Hubert 1979, What Computers Still Can't Do, New York: MIT Press . Dreyfus, Hubert Dreyfus, Stuart 1986, Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer, Oxford, UK: Blackwell Haugeland, John 1985, Artificial Intelligence: The Very Idea, Cambridge, Mass.: MIT Press, ISBN 0-262-08153-9 . Hawkins, Jeff Blakeslee, Sandra 2004, On Intelligence, New York, NY: Owl Books, ISBN 0-8050-7853-3 . Hofstadter, Douglas 1979, Gödel, Escher, Bach: an Eternal Golden Braid . Howe, J. November 1994, Artificial Intelligence at Edinburgh University: a Perspective, http://www.inf.ed.ac.uk/about/AIhistory.html . Kahneman, Daniel; Slovic, D. Tversky, Amos 1982, Judgment under uncertainty: Heuristics and biases, New York: Cambridge University Press . Kurzweil, Ray 1999, The Age of Spiritual Machines, Penguin Books, ISBN 0-670-88217-8 Kurzweil, Ray 2005, The Singularity is Near, Penguin Books, ISBN 0-670-03384-7 Lakoff, George 1987, Women, Fire, and Dangerous Things: What Categories Reveal About the Mind, University of Chicago Press., ISBN 0-226-46804-6 Lakoff, George Núñez, Rafael E. 2000, Where Mathematics Comes From: How the Embodied Mind Brings Mathematics into Being, Basic Books, ISBN 0-465-03771-2 . Lenat, Douglas Guha, R. V. 1989, Building Large Knowledge-Based Systems, Addison-Wesley Lighthill, Professor Sir James 1973, Artificial Intelligence: A General Survey, Artificial Intelligence: a paper symposium, Science Research Council Lucas, John 1961, Minds, Machines and Gödel, in Anderson, A.R., Minds and Machines, http://users.ox.ac.uk/~jrlucas/Godel/mmg.html . McCarthy, John; Minsky, Marvin; Rochester, Nathan Shannon, Claude 1955, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html . McCarthy, John Hayes, P. J. 1969, Some philosophical problems from the standpoint of artificial intelligence, Machine Intelligence 4: 463-502, http://www-formal.stanford.edu/jmc/mcchay69.html Minsky, Marvin 1967, Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall Minsky, Marvin 2006, The Emotion Machine, New York, NY: Simon Schusterl, ISBN 0-7432-7663-9 Moravec, Hans 1976, The Role of Raw Power in Intelligence, http://www.frc.ri.cmu.edu/users/hpm/project.archive/general.articles/1975/Raw.Power.html Moravec, Hans 1988, Mind Children, Harvard University Press NRC 1999, Developments in Artificial Intelligence, Funding a Revolution: Government Support for Computing Research, National Academy Press Needham, Joseph 1986, Science and Civilization in China: Volume 2, Caves Books Ltd. Newell, Allen Simon, H. A. 1963, GPS: A Program that Simulates Human Thought, in Feigenbaum, E.A. Feldman, J., Computers and Thought, McGraw-Hill Newell, Allen Simon, H. A. 1976, Computer Science as Empirical Inquiry: Symbols and Search, Communications of the ACM, 19, http://www.rci.rutgers.edu/~cfs/472_html/AI_SEARCH/PSS/PSSH4.html Searle, John 1980, Minds, Brains and Programs, Behavioral and Brain Sciences 33: 417-457, http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html Searle, John 1999, Mind, language and society, New York, NY: Basic Books, ISBN 0465045219, OCLC 231867665 43689264 Shapiro, Stuart C. 1992, Artificial Intelligence, in Shapiro, Stuart C., Encyclopedia of Artificial Intelligence 2nd ed., New York: John Wiley, pp. 54-57, http://www.cse.buffalo.edu/~shapiro/Papers/ai.ps . Simon, H. A. 1965, The Shape of Automation for Men and Management, New York: Harper Row Turing, Alan October 1950, Computing Machinery and Intelligence, Mind LIX236: 433-460, doi:10.1093/mind/LIX.236.433, ISSN 0026-4423, http://loebner.net/Prizef/TuringArticle.html. Retrieved on 18 August 2008 Wason, P. C. 1966, Reasoning, in Foss, B. M., New horizons in psychology, Harmondsworth: Penguin Weizenbaum, Joseph 1976, Computer Power and Human Reason, San Francisco: W.H. Freeman Company, ISBN 0716704641 Further reading R. Sun L. Bookman, eds., Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994. Margaret Boden, Mind As Machine, Oxford University Press, 2006 John Johnston, 2008 The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press External links The external links in this article may not follow 's content policies or guidelines. Please improve this article by removing excessive or inappropriate external links. Find more about Artificial Intelligence on 's sister projects: Dictionary definitions Textbooks Quotations Source texts Images and media News stories Learning resources AI at the Open Directory Project The Association for the Advancement of Artificial Intelligence AAAI - AI Topics Freeview Video 'Machines with Minds' by the Vega Science Trust and the BBC/OU John McCarthy's frequently asked questions about AI The Futurist magazine interviews Ai chasers Rodney Brooks, Peter Norvig, Barney Pell, et al. Jonathan Edwards looks at AI BBC audioС Ray Kurzweil's website dedicated to AI including prediction of future development in AI Logic and Artificial Intelligence entry in the Stanford Encyclopedia of Philosophy by Richmond Thomason v d e Major fields of Technology Applied science Artificial intelligence · Ceramic engineering · Computing technology · Electronics · Energy · Energy storage · Engineering physics · Environmental technology · Fisheries science · Materials science and engineering · Microtechnology · Nanotechnology · Nuclear technology · Optics · Zoography Information Communication · Graphics · Music technology · Speech recognition · Visual technology Industry Construction · Financial engineering · Manufacturing · Machinery · Mining · Business informatics Military Ammunition · Bombs · Guns · Military technology and equipment · Naval engineering Domestic Educational technology · Domestic appliances · Domestic technology · Food technology Engineering Aerospace · Agricultural · Architectural · Audio · Automotive · Biological · Biochemical · Biomedical · Broadcast · Ceramic · Chemical · Civil · Computer · Construction · Cryogenic · Electrical · Electronic · Environmental · Food · Industrial · Materials · Mechanical · Mechatronics · Metallurgical · Mining · Naval · Network · Nuclear · Optical · Petroleum · Radio Frequency · Software · Structural · Systems · Technician · Textile · Tissue · Transport Health and safety Biomedical engineering · Bioinformatics · Biotechnology · Cheminformatics · Fire protection engineering · Health technologies · Nutrition · Pharmaceuticals · Safety engineering · Sanitary engineering Transport Aerospace · Aerospace engineering · Automotive engineering · Marine engineering · Motor vehicles · Space technology Retrieved from http://en..org/wiki/Artificial_intelligence Categories: Artificial intelligence | Cybernetics | Formal sciences | Intelligence by type | History of technology | Technology in societyHidden categories: All pages needing cleanup | Articles with disputed statements from April 2008 | All articles with statements | Articles with statements since August 2008 | external links cleanup Views Article Discussion this page History Personal tools Log in / create account Navigation Main page Contents Featured content Current events Random article Search Go Search Interaction Community portal Recent changes Contact Donate to Help Toolbox What links here Related changes Upload file Special pages Printable version Permanent link Cite this page Languages العربية বাংলা Bân-lâm-gú БеларуÑ?каÑ? Bosanski БългарÑ?ки Català Česky Dansk Deutsch Eesti Ελληνικά Español Esperanto Euskara Ù?ارسی Français Galego í•œêµì–´ हिनà¥?दी Hrvatski Ido Bahasa Indonesia Interlingua Ã?slenska Italiano עברית LatvieÅ¡u Lietuvių Lojban Magyar മലയാളം मराठी Bahasa Melayu Nederlands 日本語 ‪Norsk bokmÃ¥l‬ ‪Norsk nynorsk‬ Polski Português Ripoarisch Română РуÑ?Ñ?кий Simple English SlovenÄ?ina SlovenÅ¡Ä?ina СрпÑ?ки / Srpski Srpskohrvatski / СрпÑ?кохрватÑ?ки Suomi Svenska தமிழà¯? ไทย Tiếng Việt Türkçe Türkmen УкраїнÑ?ька 粵語 ä¸æ–‡ This page was last modified on 12 September 2008, at 12:17
39 Reasons to Drink Acai Juice Every Day
What is MonaVie - Watch the 8-minute video
Discovering MonaVie Video
The Power of You Video
Effects of MonaVie Active on Antioxidant Capacity in Humans
Log into your Wholesale MonaVie Account
So many of us do not eat a balanced diet, get enough sleep, have too much stress, or are impacted with toxins and pollutants. Drinking 2 ounces of MonaVie twice a day will help your body detoxify as well as build your immune system. Its the smartest thing you can do for yourself, so start today. Buying MonaVie through our company guarantees you support 7 days a week and, if you would like to share MonaVie with your family and friends we will guide you from start to finish.
1. Click on Enroll Now (30 - 55% off retail price)
2. Pay $39 for your Wholesale ID number.
3. NO minimum order required.
4. MonaVie is delivered to your door in 3 to 5 days.