外文翻译-人工智能

英文原文ArtificialIntelligenceAdvancedIdea,AnticipatingIncomparabilityonArtificialIntelligence.Artificialintelligence(AI)isthefieldofengineeringthatbuildssystems,primarilycomputersystems,toperformtasksrequiringintelligence.Thisfieldofresearchhasoftensetitselfambitiousgoals,seekingtobuildmachinesthatcanoutlookhumansinparticulardomainsofskillandknowledge,andhasachievedsomesuccessinthis.ThekeyaspectsofintelligencearoundwhichAIresearchisusuallyfocusedincludeexpertsystem,industrialrobotics,systemsandlanguageslanguageunderstanding,learning,andgameplaying,etc.ExpertSystemAnexpertsystemisasetofprogramsthatmanipulateencodedknowledgetosolveproblemsinaspecializeddomainthatnormallyrequireshumanexpertise.Typically,theuserinteractswithanexpertsysteminaconsultationdialogue,justashewouldinteractwithahumanwhohadsometypeofexpertise,explaininghisproblem,performingsuggestedtests,andaskingquestionsaboutproposedsolutions.Currentexperimentalsystemshaveachievedhighlevelsofperformanceinconsultationtaskslikechemicalandgeologicaldataanalysis,computersystemconfiguration,structuralengineering,andevenmedicaldiagnosis.Expertsystemscanbeviewedasintermediariesbetweenhumanexperts,whointeractwiththesystemsinknowledgeacquisitionmode,andhumanuserswhointeractwiththesystemsinconsultationmode.Furthermore,muchresearchinthisareaofAIhasfocusedonendowingthesesystemswiththeabilitytoexplaintheirreasoning,bothtomaketheconsultationmoreacceptabletotheuserandtohelpthehumanexpertfinderrorsinthesystem´sreasoningwhentheyoccur.Herearethefeaturesofexpertsystems:①Expertsystemsuseknowledgeratherthandatatocontrolthesolutionprocess.②Theknowisencodedandmaintainedasanentityseparatedfromthecontrolprogram.Furthermore,itispossibleinsomecasestousedifferentknowledgebaseswiththesamecontrolprogramstoproducedifferenttypesofexpertsystem.Suchsystemareknownasexpertsystemshells.③Expertsystemsarecapableofexplaininghowaparticularconcl-usionisreached,andwhyrequestedinformationisneededduringaconsultation.④Expertsystemsusesymbolicrepresentationsforknowledgeandperformtheirinferencethroughsymboliccomputations.⑤Expertsystemsoftenreasonwithmetaknowledge.IndustrialRoboticsAnindustrialrobotisageneral-purposecomputer-controlledmanipulatorconsistingofseveralrigidlinksconnectedinseriesbyrevoluteorprismaticjoints.Researchinthisfieldhaslookedateverythingfromtheoptimalmovementofrobotarmstomethodsofplanningasequenceofactionstoachievearobot´sgoals.Althoughmorecomplexsystemshavebeenbuilt,thethousandsofrobotsthatarebeingusedtodayinindustrialapplicationsaresimpledevicesthathavebeenprogrammedtosomerepetitivetask.Robots,whencomparedtohumans,yieldmoreconsistentquality,morepredictableoutput,andaremorereliable.Robotshasbeenusedinindustrysince1965.Theyareusuallycharacterizedbythedesignofthemechanicalsystem.Therearesixrecognizablerobotconfigurations:①CartesianRobots:ArobotwhosemainframeconsistofthreeLinearaxes.②GantryRobots:AGantryrobotisatypeofartesianrobotwhosestructureresemblesagantry.Thisstructureisusedtominimizedeflectionalongeachaxis.③CylindricalRobots:Acylindricalrobothastwolinearaxesandonerotaryaxis.④SphericalRobots:Asphericalrobothasonelinearaxisandtworotaryaxes.SphericalRobotsareusedinavarietyofindustrialtaskssuchasweldingandmaterialhandling.⑤ArticulatedRobots:Anarticulatedrobothasthreerotationalaxesconnectingthreerigidlinksandabase.⑥ScaraRobots:Onestyleofrobotthathasrecentlybecomequitepopularisacombinationofthearticulatedarmandthecylindricalrobot.Therobothasmorethanthreeaxesandiswidelyusedinelectronicassembly.SystemsandLanguagesComputer-systemsideasliketime-sharing,listprocessing,andinteractivedebuggingweredevelopedintheAIresearchenvironment.Specializedprogramminglanguagesandsystems,withfeaturesdesignedtofacilitatededuction,robotmanipulation,cognitivemodeling,andsoon,haveoftenbeenrichsourcesofnewideas.Mostrecently,reveralknowledge-representationlanguages,computerlanguagesforencodingknowledgeandreasoningmethodsasdatastructureandprocedures,whichhavebeendevelopedinthelastfewyearstoexploreavarietyofideasabouthowtobuildreasoningprograms.ProblemSolvingThefirstbigsuccessinAIwasprogramsthatcouldsolvepuzzlesandplaygameslikechess.Techniqueslikelookingaheadseveralmovesanddividingdifficultproblemsintoeasiersub-problemsevolvedintothefundamentalAItechniquesofsearchandproblemreduction.Today´sprogramsplaychampionship-levelcheckersandbackgammon,aswellasverygoodchess.Anotherproblem-solvingprogramthatintegratesmathematicalformulatessymbolicallyhasattainedveryhighlevelsofperformanceandisbeingusedbyscientistsandengineers.Someprogramscanevenimprovetheirperformancewithexperience.Asdiscussedabove,theopenquestionsinthisareainvolvecapabilitiesthathumanplayershavebutcannotarticulate,likethechessmaster´sabilitytoseetheboardconfigurationintermsofmeaningfulpatterns.Anotherbasicopenquestioninvolvestheoriginalconceptualizationofaproblem,calledinAIthechoiceofproblemrepresentation.Humansoftensolveaproblembyfindingawayofthinkingaboutitthatmakesthesolutioneasy-AIproblems,sofar,mustbetoldhowtothinkabouttheproblemstheysolve.LogicalReasoningCloselyrelatedtoproblemandpuzzlesolvingwasearlyworkonlogicaldeduction.Programsweredevelopedthatcouldproveassertionsbymanipulatingadatabaseoffacts,eachrepresentedbydiscretedatastructuresjustastheyarerepresentedbydiscreteformulasinmathematicallogic.Thesemethods,unlikemanyotherAItechniques,couldbeshowntobecompleteandconsistent.Thatis,solongastheoriginalfactswerecorrect,theprogramscouldprovealltheoremsthatfollowedfromthefacts,andonlythosetheorems.LogicalreasoninghasbeenoneofthemostpersistentlyinvestigatedsubareasofAIresearch.Ofparticularinterestaretheproblemsoffindingwaysoffocusingononlytherelevantfactsofalargedatabaseandofkeepingtrackofthejustificationsforbeliefsandupdatingthemwhennewinformationarrives.LanguageUnderstandingThedomainoflanguageunderstandingwasalsoinvestigatedbyearlyAIresearchersandhasconsistentlyattractedinterest.ProgramshavebeenwrittenthatanswerquestionsposedinEnglishfromaninternaldatabase,thattranslatesentencesfromonelanguagetoanother,thatfollowinstructiongiveninEnglish,andthatacquireknowledgebyreadingtextualmaterialandbuildinganinternaldatabase.Someprogramshaveevenachievedlimitedsuccessininterpretinginstructionsspokenintoamicrophoneinsteadoftypedintothecomputer.Althoughtheselanguagesystemsarenotnearlyasgoodaspeopleareatanyofthesetasks,theyareadequateforsomeapplications.Earlysuccesseswithprogramsthatansweredsimplequeriesandfollowedsimpledirections,andearlyfailuresatmachinetranslation,haveresultedinasweepingchangeinthewholeAIapproachtolanguage.Theprincipalthemesofcurrentlanguage-understandingresearcharetheimportanceofvaseamountsofgeneral,commonsenseworldknowledgeandtheroleofexpectations,basedonthesubjectmatterandtheconversationalsituation,ininterpretingsentences.LearningLearninghasremainedachallengingareaforAI.Certainlyoneofthemostsalientandsignificantaspectsofhumanintelligenceistheabilitytolearn.ThisisagoodexampleofcognitivebehaviorthatissopoorlyunderstoodthatvarylittleprogresshasbeenmadeinachievingitinAIsystem.Therehavebeenseveralinterestingattempts,includingprogramslearnfromexamples,formtheirownperformance,andfrombeingtold.Anexpertsystemmayperformextensiveandcostlycomputationstosolveaproblem.Mostexpertsystemsarehinderedbytheinflexibilityoftheirproblem-solvingstrategiesandthedifficultyofmodifyinglargeamountsofcode.Theobvioussolutiontotheseproblemsisforprogramstolearnontheirown,eitherfromexperience,analogy,andexamplesorbybeingtoldwhattodo.GamePlayingMuchoftheearlyresearchinstatespacesearchwasdoneusingcommonboardgamessuchascheckers,chess,andthe15-puzzle.Inadditiontotheirinherentintellectualappeal,boardgameshavecertainpropertiesthatmakethemidealsubjectsforthisearlywork.Mostgamesareplayedusingawell-definedsetofrules,thismakesiteasytogeneratethesearchspaceandfreestheresearcherfrommanyoftheambiguitiesandcomplexitiesinherentinlessstructuredproblems.Theboardconfigurationsusedinplayingthesegamesareeasilyrepresentedonacomputer,requiringnoneofthecomplexformalisms.ConclusionWehaveattemptedtodefineartificialintelligencethroughdiscussionofitsmajorareasofresearchandapplication.Inspiteofthevarietyofproblemsaddressedinartificialintelligenceresearch,anumberofimportantfeaturesemergethatseemcommontoalldivisionsofthefield,theseinclude:①Theuseofcomputerstodoreasoning,learning,orsomeotherformofintelligence.②Afocusonproblemsthatdonotrespondtoalgorithmicsolutions.ThisunderliestherelianceonheuristicsearchasanAIproblem-solvingtechnique.③Reasoningaboutthesignificantqualitativefeaturesofasituation.④Anattempttodealwithissuesofsemanticmeaningaswellassyntacticform.⑤Theuseoflargeamountsofdomain-specificknowledgeinsolvingproblems.Thisisthebasisofexpertsystems.AbstractArtificialintelligence(AI)isthefieldofengineeringthatbuildssystems,primarilycomputersystems,toperformtasksrequiringintelligence.Thisfieldofresearchhasoftensetitselfambitiousgoals,seekingtobuildmachinesthatcanoutlookhumansinparticulardomainsofskillandknowledge,andhasachievedsomesuccessinthis.ThekeyaspectsofintelligencearoundwhichAIresearchisusuallyfocusedincludeexpertsystems,industrialrobotics,systemsandlanguages,languageunderstanding,learning,andgameplaying,machinetranslation,etc.中文译文人工智能先进的想法不断注入到人工智能的发展过程中，使其最新理念无与伦比。人工智能是一个构建系统的工程领域，主要用来构筑计算机系统，从而完成那些智能化工作。这个研究领域常常树立野心勃勃的目标，以寻觅来制造出一些拥有人类特定技能和知识的机器，并且已经获得了一些成功的案例。人工智能研究常常聚焦于专家系统，工业机器人，系统与语言，语言理解，自学习，智能游戏等等。专家系统专家系统是这样一组程序，它们操纵那些表示为代码的知识来解决一些需要人类专长的某些特定领域的问题。典型地，用户在向专家系统请教时，就像是在请教一个有某方面专长的人，这个专家能够解释问题，对建议进行检测，并对解决方案进行质疑。在化学和地址学的数据分析，计算机系统结构，结构工程，甚至在医疗诊断方面，当前实验性的专家系统都达到了高水平。专家系统可以看作一些专家们的仲裁者，以知识获取模式工作，而用户是以请教模式同系统进行交互。并且，在人工智能领域的研究已经聚焦于展现系统进行推理的过程，从而让用户心悦诚服接受建议，或者帮助用户专家发现系统推理时的错误。以下是专家系统的特性。①专家系统是利用知识而并非数据来控制解决进程。②知识转化成了代码，并被作为一个区别于控制程序的实体。而且，在一些情况下将不同的知识运用于同一个控制程序会产生不同类型的专家系统。这些系统被誉为专家系统外壳。③专家系统有能力解释一些特定的结论是如何形成的，并且在推理过程中需要哪些信息。④专家系统利用符号代表知识，并利用符号计算来进行推理论证。⑤专家系统经常利用元知识进行推理。工业机器人工业机器人是广泛使用的由计算机控制的通过外卷的，或棱镜似的连接结合起来的操作员。为了达到一个工业机器人的目标，这个领域的研究集中于设计一系列的运动来达到最佳的行动方案。虽然工业机器人需要更复杂的系统，成千上万的机器人已经应用于工业领域，它们都是一些简单的经过编程的装置，主要从事一些重复性工作。机器人和人类相比，工作质量好，稳定性强，可靠性高。机器人从1965年进入工业领域，它们具有机械系统的设计特征。以下是6种公认的工业机器人结构：①笛卡儿式机器人：一种主框架由三根直线轴组成的机器人。②桶架式机器人：桶架式机器人是一种喷水井机器人，它的结构组成了一个桶架。这个结构用来减少每个轴的倾斜度。③柱面机器人：柱面坐标式机器人有两根直线轴和一个旋转轴。④球式机器人：球式机器人有一根直线轴和两个旋转轴。球式机器人被应用于定位焊接和材料搬运之类的工业应用上。⑤挂接式机器人：挂接式机器人有三根直线轴连着三个节点和一个基座。⑥斯凯瑞机器人:一种最近变的非常流行的机器人，它是由有关节的手臂组成的圆柱体机器人。这种机器人有多于三根的直线轴，并被广泛应用于电子组装行业。系统与语言人工智能发展了计算机系统方面的一些理念，如：时间分配，编目处理，交互式调试，等等。专用于编程的系统与语言已经成为丰富思想的源泉，因为其包含了优化演绎，机器人操作，认知模型等等的新特性。特别是最近以来，一些具备知识表示能力的计算机语言已经得到进一步的发展，它们能够将知识转化为代码，将推理方法表示为数据结构。这些计算机语言的发展已经促进了关于如何构建推理机的新思想的萌发。问题求解人工智能所取得的首次成功是解决了迷宫和棋类游戏的问题。能提前预料几步的前瞻技术和将复杂问题划分为容易解决的子问题的技术已经卷入并促进了人工智能中最基本的搜索与问题优化技术的发展。当今的智能程序已经能够在西洋双陆棋等一些很好的棋类游戏中发挥世界冠军级的水平。另外一个整合数学理论的问题求解领域也已经达到了很高的水准，并被科学家和工程师广泛使用。其中有些程序甚至能够通过经验积累来不断提高水平。像上面所讨论的那样，在此领域都涉及到了人类的本领，但是却不能进行关联，比如有些老练的棋手有根据丰富的前景模式通观全局的本领。另外一个开放式的问题涉及到将一个待求解的问题概念化，在人工智能领域被称为问题表现的选择。人类经常利用求解问题中简单的方法来处理问题，因此，人工智能程序，到目前为止，应该被告知怎样去思考它们所要解决的问题。逻辑推理与问题求解密切相关的是逻辑推断的早期工作。智能程序不断的发展，能够通过对一个事实数据库的操作来产生断言，这些断言由一些不连续的数据结构表示，就像在数学逻辑中它们被不连续的规则表示一样。这些方法，不像许