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It is the science and engineering ofmaking intelligent machines, especiallyintelligent computer programs. It isrelated to the similar task of usingcomputers to understand human

intelligence.

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The general problem of simulating (orcreating) intelligence has been broken downinto a number of specific sub-problems.These consist of particular traits or

capabilities that researchers would like anintelligent system to display. The traitsdescribed below have received the mostattention.

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Deduction, reasoning, problem solving

Knowledge representation Planning

Learning

Natural language processing

Motion and manipulation Perception

Social intelligence

Creativity General intelligence

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Early AI researchers developed algorithms thatimitated the step-by-step reasoning that humansuse when they solve puzzles or make logicaldeductions.

For difficult problems, most of these algorithmscan require enormous computational resources most experience a "combinatorial explosion":the amount of memory or computer timerequired becomes astronomical when the

problem goes beyond a certain size. The searchfor more efficient problem-solving algorithms isa high priority for AI research

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Knowledge representation[43] and knowledgeengineering[44] are central to AI research.Many of the problems machines are expectedto solve will require extensive knowledge

about the world

Among the things that AI needs to representare: objects, properties, categories andrelations between objects, situations, events,states and time, causes and effects

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Intelligent agents must be able to set goalsand achieve them.[56] They need a way tovisualize the future (they must have arepresentation of the state of the world and

be able to make predictions about how theiractions will change it) and be able to makechoices that maximize the utility (or "value")of the available choices.[57]

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Machine learning has been central to AIresearch from the beginning.

Regression is the attempt to produce afunction that describes the relationship

between inputs and outputs and predicts howthe outputs should change as the inputschange.

In reinforcement learning the agent is

rewarded for good responses and punishedfor bad ones. These can be analyzed in termsofdecision theory, using concepts like utility

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Natural language processing[66] gives machinesthe ability to read and understand the languagesthat humans speak

A sufficiently powerful natural language

processing system would enable naturallanguage user interfaces and the acquisition ofknowledge directly from human-written sources,such as Internet texts. Some straightforward

applications of natural language processinginclude information retrieval (or text mining)and machine translation.[

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The field ofrobotics[68] is closely related toAI.

Intelligence is required for robots to be ableto handle such tasks as object manipulation

and navigation, with sub-problemsoflocalization (knowing where youare), mapping (learning what is around you)and motion planning (figuring out how to getthere)

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Machine perception is the ability to use inputfrom sensors (such as cameras, microphones,sonar and others more exotic) to deduceaspects of the world. Computer vision is the

ability to analyze visual input.

A few selected subproblems are speechrecognition, facial recognition and objectrecognition

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Emotion and social skills[75] play two roles for an

intelligent agent. First, it must be able to predictthe actions of others, by understanding theirmotives and emotional states. (This involveselements ofgame theory, decision theory, as wellas the ability to model human emotions and the

perceptual skills to detect emotions.) Also, in an effort to facilitate human-computer

interaction, an intelligent machine might want tobe able to displayemotionseven if it does not

actually experience them itself

in order toappear sensitive to the emotional dynamics ofhuman interaction.

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http://en.wikipedia.org/wiki/Kismet_(robot)http://en.wikipedia.org/wiki/Kismet_(robot)

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A sub-field of AI addresses creativity boththeoretically (from a philosophical andpsychological perspective) and practically (viaspecific implementations of systems that

generate outputs that can be consideredcreative, or systems that identify and assesscreativity)

Related areas of computational researchareArtificial intuition and Artificialimagination

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Most researchers hope that their work willeventually be incorporated into a machine

with general intelligence (combining all the skillsabove and exceeding human abilities at most orall ofthem. A few believethat anthropomorphic features like artificialconsciousness or an artificial brain may be

required for such a project.

Even a straightforward, specific task like machinetranslation requires that the machine follow theauthor's argument (reason), know what is being

talked about (knowledge), and faithfully reproducethe author's intention (socialintelligence). Machine translation, therefore, isbelieved to be AI-complete: it may require strongAI to be done as well as humans can do it.

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