Table Of Content

(cid:0) Arti(cid:2)cial Intelligence For articles on related subjects see Artificial Life(cid:2) Automated Plan(cid:3) ning(cid:2) Cognitive Science(cid:2) Computer Chess(cid:2) Computer Games(cid:2) Com(cid:3) puterMusic(cid:2)ComputerVision(cid:2)ExpertSystems(cid:2)GeneticAlgorithms(cid:2) Heuristic(cid:2)KnowledgeRepresentation(cid:2) LanguageTranslation(cid:2)Multi(cid:3) Agent Systems(cid:2) Natural Language Processing(cid:2) Neural Networks(cid:2) PatternRecognition(cid:2) Robotics(cid:2)Searching(cid:2)Simon(cid:4)HerbertA(cid:5)(cid:2)Speech Recognition and Synthesis(cid:2) Theorem Proving and Turing(cid:4) Alan M(cid:5) Introduction (cid:0) Arti(cid:2)cial Intelligence (cid:3)AI(cid:4) is a (cid:2)eld of computer science and engineering con(cid:5) cernedwiththecomputationalunderstandingofwhatiscommonlycalledintel(cid:5) ligent behavior(cid:6) and with the creation of artifacts that exhibit such behavior(cid:7) This de(cid:2)nition may be examined more closely by considering the (cid:2)eld from three pointsofview(cid:8) computationalpsychology(cid:6)computationalphilosophy(cid:6)and machine intelligence(cid:7) Computational Psychology The goal of computational psychology is to understand human intelligent be(cid:5) haviorby creating computer programs that behave in the same way people do(cid:7) For this goal it is important that the algorithm expressed by the program be the same algorithmthat people actually use(cid:6) and that the data structures used by the program be the same data structures used by the human mind(cid:7) The programshould doquicklywhat people doquickly(cid:6) shoulddomoreslowlywhat peoplehavedi(cid:9)cultydoing(cid:6) andshould eventend tomakemistakeswherepeo(cid:5) pletendtomakemistakes(cid:7) Iftheprogramwereput intothe sameexperimental situationsthathumansaresubjectedto(cid:6)theprogram(cid:10)sresultsshouldbewithin the range of human variability(cid:7) Computational Philosophy Thegoalofcomputationalphilosophyistoformacomputationalunderstanding of human(cid:5)level intelligent behavior(cid:6) without being restricted to the algorithms and data structures that the human mind actually does (cid:3)or conceivably might(cid:4) use(cid:7) By (cid:11)computational understanding(cid:12) is meant a model that is expressed as a procedure that is at least implementable (cid:3)if not actually implemented(cid:4) on a computer(cid:7) By (cid:11)human(cid:5)level intelligent behavior(cid:12)is meant behavior that(cid:6) when (cid:0) ThisisapreliminaryversionofStuartC(cid:2)Shapiro(cid:3)(cid:4)Arti(cid:5)cialIntelligence(cid:2)(cid:6) InA(cid:2)Ralston(cid:3) E(cid:2)D(cid:2)ReillyandD(cid:2)Hemmendinger(cid:3)Eds(cid:2) EncyclopediaofComputerScience(cid:2)FourthEdition(cid:2) VanNostrandReinhold(cid:3)NewYork(cid:3)forthcoming(cid:2) (cid:2) ThisarticleisarevisedversionofShapiro(cid:3)S(cid:2)C(cid:2)(cid:4)Arti(cid:5)cialIntelligence(cid:3)(cid:6)inS(cid:2)C(cid:2)Shapiro(cid:3) Ed(cid:2) Encyclopedia of Arti(cid:3)cial Intelligence(cid:2) Second Edition(cid:4) New York(cid:7) John Wiley (cid:8) Sons(cid:3) (cid:9)(cid:10)(cid:10)(cid:9)(cid:2) (cid:0) engaged in by people(cid:6) is commonly taken as being part of human intelligent cognitive behavior(cid:7) It is acceptable(cid:6) though not required(cid:6) if the implemented model perform some tasks better than any person would(cid:7) Bearing in mind Church(cid:10)sThesis(cid:3)see Church(cid:4) Alonzo(cid:4)(cid:6)thisgoalmightberewordedasasking the question(cid:6) (cid:11)Is intelligence a computable function(cid:13)(cid:12) In the AI areas of computer vision (cid:3)q(cid:2)v(cid:2)(cid:4) and robotics (cid:3)q(cid:2)v(cid:2)(cid:4)(cid:6) computa(cid:5) tional philosophy is sometimes replaced by computational natural philosophy (cid:3)science(cid:4)(cid:7) For example(cid:6) some computer vision researchers are interested in the computational optics question of how the information contained in light waves re(cid:14)ectedfrom anobject canbe used toreconstruct the object(cid:7) Notice that this is a di(cid:15)erent question from the computational psychology question of how the humanvisualsystemuseslightwavesfallingontheretina toidentifyobjects in theworld(cid:6)oreventhecomputationalphilosophyquestionofhowanyintelligent entity could use light waves falling on a two(cid:5)dimensional retinal grid to dis(cid:5) criminateonethree(cid:5)dimensionalobject(cid:5)in(cid:5)the(cid:5)worldfromasetofotherpossible objects(cid:7) Machine Intelligence The goal of machine intelligence (cid:3)called(cid:6) in earlier versions of this article(cid:6) (cid:11)ad(cid:5) vancedcomputerscience(cid:12)(cid:4)istopushoutwardsthefrontierofwhatweknowhow to programon computers(cid:6) especiallyin the direction of tasks that(cid:6) although we don(cid:10)t know how to program them(cid:6) people can perform(cid:7) This goal led to one of theoldestde(cid:2)nitionsofAI(cid:8)theattempttoprogramcomputerstodowhat(cid:6)until recently(cid:6) only people could do(cid:7) Although this expressesthe idea of pushing out thefrontier(cid:6)itisalsoperpetuallyself(cid:5)defeatinginthat(cid:6)assoonasataskiscon(cid:5) quered(cid:6) it no longerfalls within the domain of AI(cid:7) Thus(cid:6) AI is left with only its failures(cid:16)itssuccessesbecomeotherareasofcomputerscience(cid:7) Themostfamous example is the area of symbolic calculus(cid:7) (cid:3)see Computer Algebra(cid:4)(cid:7) When JamesSlaglewrotetheSAINTprogram(cid:6)itwasthe(cid:2)rstprograminhistorythat couldsolvesymbolicintegrationproblemsatthe levelof freshman calculusstu(cid:5) dents(cid:6) and was considered an AI project(cid:7) Now that there are multiple systems on the market that can do much more than what SAINT did(cid:6) many people do notconsiderthesesystemstobetheresultsofAIresearch(cid:7) Thegoalofmachine intelligence di(cid:15)ers from computational psychology and computational philoso(cid:5) phy in being task(cid:5)oriented rather than oriented toward the understanding of generalintelligentbehavior(cid:7) Amachineintelligenceapproachtoataskistouse any technique that helps accomplish the task(cid:6) even if the technique is not used by humans and would probably not be used by generally intelligent entities(cid:7) Subsymbolic AI Computationalpsychology(cid:6)computationalphilosophy(cid:6)andmachineintelligence are subareas of AI divided by their goals(cid:7) AI researcherswander among two or all three of these areas throughout their career(cid:6) and may even have a mixture of these goals at the same time(cid:7) Cutting across these goals(cid:6) however(cid:6) is a (cid:17) recent division of approachinto (cid:11)symbolic(cid:12) AI and (cid:11)subsymbolic(cid:12)AI(cid:7) The key assumption of symbolic AI is that knowledge is represented by structures of semantically meaningful symbols(cid:7) Each symbol representing some entity(cid:6) be it abstractorconcrete(cid:6)thattheintelligentsystemoragentisdiscussing(cid:6)observing(cid:6) reasoning about(cid:6) or operating on(cid:7) On the contrary(cid:6) the key assumption of subsymbolicAIisthatintelligentbehaviorcanbeattainedwithoutsemantically meaningful symbols(cid:7) Much of subsymbolic AI is included in the (cid:2)eld of (cid:11)soft computing(cid:12)(cid:8) (cid:11)In contrast to the traditional(cid:6) hard computing(cid:6) soft computing is tolerant of imprecision(cid:6) uncertainty and partial truth(cid:7) The basic premises of soft computing are(cid:8) (cid:0) imprecision and uncertainty are pervasive (cid:0) precision and certainty carry a cost The guiding principle of soft computing is(cid:8) (cid:0) exploit the tolerance for imprecision(cid:6) uncertainty and partial truth to achieve tractability(cid:6) robustness and low solution cost(cid:7) Soft computing is not a single methodology(cid:16) rather(cid:6) it is a con(cid:5) sortium or a partnership of methodologies(cid:7) At this juncture(cid:6) the principal constituents of soft computing (cid:3)SC(cid:4) are(cid:8) fuzzy logic (cid:3)FL(cid:4)(cid:6) neurocomputing (cid:3)NC(cid:4) (cid:18)see Neural Networks(cid:19)(cid:6) and genetic algo(cid:5) rithms (cid:3)GA(cid:4) (cid:18)q(cid:2)v(cid:2)(cid:19)(cid:7) The principal contribution of FL is a methodol(cid:5) ogyforapproximatereasoningand(cid:6)inparticular(cid:6)forcomputingwith words(cid:16)thatofNCiscurve(cid:2)tting(cid:6)learningandsystemidenti(cid:2)cation(cid:16) and that of GA is systematized random search and optimization(cid:7)(cid:12) (cid:18)Zadeh(cid:6) (cid:0)(cid:20)(cid:20)(cid:21)(cid:19) Heuristic Programming Another way of distinguishing AI as a (cid:2)eld is by noting the AI researcher(cid:10)s interestin heuristics (cid:3)q(cid:2)v(cid:2)(cid:4) ratherthan inalgorithms (cid:3)q(cid:2)v(cid:2)(cid:4)(cid:7) HereIam takinga wideinterpretationofaheuristic asanyproblemsolvingprocedurethat failsto beanalgorithm(cid:6)orthathasnotbeenshowntobeanalgorithm(cid:6)foranyreason(cid:7) An interesting view of the tasks that AI researchers consider to be their own may be gained by considering those ways in which a procedure (cid:3)q(cid:2)v(cid:2)(cid:4) may fail to qualify as an algorithm(cid:7) Bycommonde(cid:2)nition(cid:6)analgorithmforageneralproblemPisanunambigu(cid:5) ousprocedure that(cid:6) foreveryparticular instance of P(cid:6) terminates and produces the correct answer(cid:7) The most common reasons that a heuristic H fails to be an algorithm are that it doesn(cid:10)t terminate for some instances of P(cid:6) it has not beenprovedcorrectforallinstancesofPbecauseofsomeproblemwithH(cid:6)orit hasnot been provedcorrectforall instancesof P becauseP is not well(cid:5)de(cid:2)ned(cid:7) Common examples of heuristic AI programs that don(cid:10)t terminate for all in(cid:5) stancesoftheproblemtheyhavebeen designedforincludesearching(cid:3)q(cid:2)v(cid:2)(cid:4) and theoremproving(cid:3)q(cid:2)v(cid:2)(cid:4) programs(cid:7) Anysearchprocedurewillrunforeverifgiven (cid:22) an in(cid:2)nite searchspacethat containsnosolutionstate(cid:7) G(cid:23)odel(cid:10)s Incompleteness Theorem states that there are formal theories that contain true but unprov(cid:5) ablepropositions(cid:7) Inactualpractice(cid:6)AIprogramsforthese problemsstopafter someprespeci(cid:2)ed time(cid:6) space(cid:6) or workbound hasbeen reached(cid:7) They can then reportonly that they wereunable to (cid:2)nd a solution even though(cid:24)in any given case(cid:24)alittle more workmight haveproduced an answer(cid:7) An exampleof an AI heuristicthathasnotbeenprovedcorrectisanystaticevaluationfunctionused in a program for playing computer chess (cid:3)q(cid:2)v(cid:2)(cid:4)(cid:7) The static evaluation function returns an estimate of the value of some state of the board(cid:7) To be correct(cid:6) it would return (cid:25)(cid:2) if the state were a sure win for the side to move(cid:6) (cid:3)(cid:2) if it were a sure win for the opponent(cid:6) and (cid:26) if it were a forced draw(cid:7) Moreover(cid:6) for any state it is theoretically possible to (cid:2)nd the correct answer algorithmically by doing a full minimax search of the game tree rooted in the state being ex(cid:5) amined(cid:7) Suchafull searchisinfeasableformoststates(cid:6)however(cid:6)becauseofthe size of the game tree(cid:7) Nonetheless(cid:6) static evaluation functions are still useful(cid:6) even withoug being proved correct(cid:7) An example of a heuristic AI program that has not been proved correct because the problem for which it has been designed is not well(cid:5)de(cid:2)ned is any natural language understanding program or natural language interface(cid:7) Since no one has any well(cid:5)de(cid:2)ned criteria for whether a person understands a given language(cid:6) there cannot be any well(cid:5)de(cid:2)ned criteria for programs either(cid:7) Early History Although the dream of creating intelligent artifacts has existed for many cen(cid:5) turies(cid:6) the (cid:2)eld of arti(cid:2)cial intelligence is considered to have had its birth at a conference held at Dartmouth College in the summer of (cid:0)(cid:20)(cid:21)(cid:27)(cid:7) The conference was organized by Marvin Minsky and John McCarthy(cid:6) and McCarthy coined thename(cid:11)Arti(cid:2)cialIntelligence(cid:12)fortheproposaltoobtainfundingforthecon(cid:5) ference(cid:7) Among theattendeeswereHerbertSimon(cid:3)q(cid:2)v(cid:2)(cid:4) andAllen Newellwho had already implemented the Logic Theorist program at the Rand Corpora(cid:5) tion(cid:7) ThesefourpeopleareconsideredthefathersofAI(cid:7)MinskyandMcCarthy founded the AI Laboratory at M(cid:7)I(cid:7)T(cid:7)(cid:16) Simon and Newell founded the AI lab(cid:5) oratory at Carnegie(cid:5)Mellon University(cid:7) McCarthy later moved from M(cid:7)I(cid:7)T(cid:7) to Stanford University(cid:6) where he founded the AI laboratory there(cid:7) These three universities(cid:6) along with Edinburgh University(cid:6) whose Department of Machine IntelligencewasfoundedbyDonaldMichie(cid:6)haveremainedthepremierresearch universitiesin the (cid:2)eld(cid:7) Thename Arti(cid:2)cialIntelligenceremainedcontroversial forsome years(cid:6)evenamongpeople doing researchin the area(cid:6)but it eventually was accepted(cid:7) The(cid:2)rstAItextwasComputersandThought(cid:3)editedbyEdwardFeigenbaum andJulianFeldman(cid:6)andpublishedbyMcGraw(cid:5)Hillin(cid:0)(cid:20)(cid:27)(cid:22)(cid:7) Thisisacollection of (cid:17)(cid:0) papers(cid:6) some of them short versions of Ph(cid:7)D(cid:7) dissertations(cid:6) by early AI researchers(cid:7) Most of the papers in this collection are still considered classics of AI(cid:6) but of particular note is a reprint of Alan M(cid:7) Turing(cid:10)s (cid:0)(cid:20)(cid:21)(cid:26)paper in which (cid:28) the Turing Test was introduced(cid:7) (cid:3)see Turing(cid:4) Alan(cid:7)(cid:4) Regular AI conferences began in the mid to late (cid:0)(cid:20)(cid:27)(cid:26)s(cid:7) The Machine In(cid:5) telligence Workshops series began in (cid:0)(cid:20)(cid:27)(cid:21) in Edinburgh(cid:7) A conference at Case WesternUniversityinSpring(cid:6)(cid:0)(cid:20)(cid:27)(cid:29)drewmanyoftheU(cid:7)S(cid:7)AIresearchersofthe time(cid:6) and the (cid:2)rst biennial International Joint Conference on Arti(cid:2)cial Intelli(cid:5) gence was held in Washington(cid:6) D(cid:7) C(cid:7) in May(cid:6) (cid:0)(cid:20)(cid:27)(cid:20)(cid:7) Arti(cid:4)cial Intelligence(cid:3) still the premier journal of AI research(cid:6)began publication in (cid:0)(cid:20)(cid:30)(cid:26)(cid:7) For a more complete history of AI(cid:6) see McCorduck (cid:0)(cid:20)(cid:30)(cid:20)(cid:7) Neighboring Disciplines Arti(cid:2)cialIntelligenceisgenerallyconsideredtobeasub(cid:2)eldofcomputerscience(cid:6) though there are some computer scientists who have only recently and grudg(cid:5) inglyacceptedthisview(cid:7) Thereareseveraldisciplinesoutsidecomputerscience(cid:6) however(cid:6) that strongly impact AI and that(cid:6) in turn(cid:6) AI strongly impacts(cid:7) Cognitive psychology is the sub(cid:2)eld of psychology that uses experimental methods to study human cognitive behavior(cid:7) The goal of AI called compu(cid:5) tational psychology earlier is obviously closely related to cognitive psychology(cid:6) di(cid:15)ering mainly in the use of computationalmodels rather than experments on human subjects(cid:7) However(cid:6) most AI researchers pay some attention to the re(cid:5) sults of cognitive psychology(cid:6)and cognitive psychologists tend to pay attention to AI as suggesting possible cognitive procedures that they might look for in humans(cid:7) Cognitivescience(cid:3)q(cid:2)v(cid:2)(cid:4) isaninterdisciplinary(cid:2)eldthatstudieshumancogni(cid:5) tivebehaviorunderthehypothesisthatcognitionis(cid:3)orcanusefullybemodeled as(cid:4) computation(cid:7) Although the overlap between AI and cognitive science is large(cid:6) there are researchers in each (cid:2)eld that would not consider themselves to be in the other(cid:7) AI researchers whose primary goal is what was called machine intelligenceearlierin this article generallydo not considerthemselvesto be do(cid:5) ingcognitivescience(cid:6)andcognitivesciencecontainsnotonlyAIresearchers(cid:6)but alsocognitivepsychologists(cid:6)linguists(cid:6)philosophers(cid:6)anthropologists(cid:6)andothers(cid:6) all using the methodology of their own discipline on a common problem(cid:24)that of understanding human cognitive behavior(cid:7) Computationallinguistsusecomputers(cid:6)oratleastthecomputationalparadigm(cid:6) to study and(cid:31)or to process human languages(cid:7) Like cognitive science(cid:6) compu(cid:5) tational linguistics overlaps(cid:6) but is not coextensive with(cid:6) AI(cid:7) It includes those areasofAIcallednaturallanguageunderstanding(cid:6)naturallanguagegeneration(cid:6) speechrecognitionandsynthesis(cid:3)q(cid:2)v(cid:2)(cid:4)(cid:6)andmachinetranslation(cid:3)see Language Translation(cid:4)(cid:6) but also non(cid:5)AI areassuch as the use of statistical methods to (cid:2)nd index keywords useful for retrieving a document(cid:7) (cid:21) AI(cid:3)Complete Tasks There are many subtopics in the (cid:2)eld of AI(cid:24)subtopics that vary from the considerationofaveryparticular(cid:6)technicalproblem(cid:6)tobroadareasofresearch(cid:7) Several of these broad areas can be considered AI(cid:5)complete(cid:3) in the sense that solvingtheproblemoftheareaisequivalenttosolvingtheentireAI problem(cid:24) producingagenerallyintelligentcomputerprogram(cid:7) Researchersinoneofthese areas may see themselves as attacking the entire AI problem from a particular direction(cid:7) The following sections discuss some of the AI(cid:5)complete areas(cid:7) Natural Language The AI subarea of Natural Language is essentially the overlap of AI and com(cid:5) putational linguistics (cid:3)see above(cid:4)(cid:7) The goal is to form a computational under(cid:5) standing of how people learn and use their native languages(cid:6) and to produce a computer program that can use a human language at the same level of compe(cid:5) tence as a native human speaker(cid:7) Virtually all human knowledge has been (cid:3)or couldbe(cid:4) encodedin humanlanguages(cid:7) Moreover(cid:6)researchin natural language understandinghasshownthatencyclopedicknowledgeisrequiredtounderstand naturallanguage(cid:7) Therefore(cid:6)a complete natural languagesystem will alsobe a complete intelligent system(cid:7) Problem Solving and Search Problemsolvingisthe areaofAIthatisconcernedwith(cid:2)ndingorconstructing the solution to a problem(cid:7) That sounds like a very general area(cid:6) and it is(cid:7) The distinctive characteristic of the area is probably its approach of seeing tasks as problems to be solved(cid:6) and of seeing problems as spaces of potential solutions thatmustbesearchedto(cid:2)ndthetrueoneorthebestone(cid:7) Thus(cid:6) theAI areaof search is very much connected to problem solving(cid:7) Since any area investigated by AI researchersmay be seen as consisting of problems to be solved(cid:6) all of AI may be seen as involving problem solving and search(cid:7) Knowledge Representation and Reasoning Knowledge representation (cid:3)q(cid:2)v(cid:2)(cid:4) is the area of AI concerned with the formal symbolic languages used to represent the knowledge (cid:3)data(cid:4) used by intelligent systems(cid:6) and the data structures (cid:3)q(cid:2)v(cid:2)(cid:4) used to implement those formal lan(cid:5) guages(cid:7) However(cid:6) one cannot study static representation formalisms and know anything about how useful they are(cid:7) Instead(cid:6) one must study how they are helpful for their intended use(cid:7) In most cases(cid:6) this use is to use explicitly stored knowledge to produce additional explicit knowledge(cid:7) This is what reasoningis(cid:7) Together(cid:6)knowledgerepresentationandreasoningcanbeseentobebothneces(cid:5) saryandsu(cid:9)cientforproducinggeneralintelligence(cid:24)itis anotherAI(cid:5)complete area(cid:7) Although they are bound up with each other(cid:6) knowledge representation and reasoning can be teased apart(cid:6) according to whether the particular study (cid:27) is more about the representation language(cid:31)data structure(cid:6) or about the active process of drawing conclusions(cid:7) Learning Learning is often cited as the criterial characteristic of intelligence(cid:6) and it has always seemed like the easy way to produce intelligent systems(cid:8) Why build an intelligent system when we could just build a learning system and send it to school(cid:13) Learningincludesallstylesoflearning(cid:6)fromrotelearningtothedesign and analysis of experiments(cid:6) and all subject areas(cid:7) If the ultimate learning machineisevercreated(cid:6)itwillacquiregeneralintelligence(cid:6)whichiswhylearning is AI(cid:5)complete(cid:7) Vision Vision(cid:6) or image understanding(cid:6) has to do with interpreting visual images that fall on the human retina or the camera lens(cid:7) (cid:3)see Computer Vision(cid:4)(cid:7) The actual scene being viewed could be (cid:17)(cid:5)dimensional(cid:6) such as a printed page of text(cid:6) or (cid:22)(cid:5)dimensional(cid:6) such as the world about us(cid:7) If we take (cid:11)interpreting(cid:12) broadly enough(cid:6) it is clear that general intelligence may be needed to do the interpretation(cid:6) and that correct interpretation implies general intelligence(cid:6) so this is another AI(cid:5)complete area(cid:7) Robotics The area of robotics (cid:3)q(cid:2)v(cid:2)(cid:4) is concerned with artifacts that can move about in theactualphysicalworldand(cid:31)orthatcanmanipulateotherobjectsintheworld(cid:7) Intelligent robots must be able to accommodate to new circumstances(cid:6) and to do this(cid:6) they need to be able to solve problems and to learn(cid:7) Thus intelligent robotics is also an AI(cid:5)complete area(cid:7) Integrated Systems The most direct work on the problem of generally intelligent system is to use a robot that has vision and(cid:31)or other senses(cid:6) plans and solves problems(cid:6) and communicates via natural language(cid:7) Periodically throughout the history of AI(cid:6) research groups have assembled such integrated robots as tests of the current state of AI(cid:7) Autonomous Agents (cid:11)Autonomousagentsarecomputersystemsthatarecapableofindependent ac(cid:5) (cid:2) tion in dynamic(cid:6) unpredictable environments(cid:7)(cid:12) Autonomous agents are like integrated robots(cid:6) except that they needn(cid:10)t have physical bodies nor need they (cid:3) Call for Papers(cid:3) AGENTS (cid:11)(cid:10)(cid:12)(cid:3) the Second International Conference on Autonomous Agents(cid:2) (cid:30) operate in the real(cid:6) physical world(cid:7) Instead(cid:6) they may be completely software agents(cid:6) sometimes called (cid:11)softbots(cid:6)(cid:12) and may operate in the world of the In(cid:5) ternet(cid:6) crawling around from (cid:2)le to (cid:2)le(cid:6) collecting information for their human clients(cid:7) To the extent that a softbot needs to be able to understand the (cid:2)les it comes across(cid:6) it needs to be able to understand natural language(cid:6) and to the extent that it needs to solve navigational problems to (cid:2)nd the information it was asked for(cid:6) it may need general purpose reasoning(cid:7) Applications Throughout the existence of the (cid:2)eld(cid:6) AI research has produced spino(cid:15)s into other areas of computer science(cid:7) Lately(cid:6) however(cid:6) programming techniques de(cid:5) veloped by AI researchers have found application to many programming prob(cid:5) lems(cid:7) This has largely come about through the subarea of AI known as expert systems (cid:3)q(cid:2)v(cid:2)(cid:4)(cid:7) Whether or not any particular program should be considered intelligent or an expert according to the common use of those words is largely irrelevantto the workersin and the observersofthe expert systems area(cid:7) From their point of view they have tools and a methodology that are more useful for solving their problems than traditional programming tools and methodologies(cid:7) Fromthe point ofview ofAI asawhole(cid:6) probablythebest thing about this de(cid:5) velopmentisthataftermanyyearsofbeingcriticizedasfollowinganimpossible dream by inappropriate and inadequate means(cid:6) AI has been recognized by the general public as having applications to everydayproblems(cid:7) References (cid:0)(cid:20)(cid:21)(cid:26)(cid:7) Turing(cid:6) A(cid:7)M(cid:7) (cid:11)Computing Machinery and Intelligence(cid:7)(cid:12) Mind(cid:3) (cid:0)(cid:2) (cid:3)Oc(cid:5) tober(cid:4)(cid:6) (cid:28)(cid:22)(cid:22) (cid:28)(cid:27)(cid:26)(cid:7) 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