《应用人机交互》课程教学大纲

《应用人机交互》教学大纲课程英文名AppliedHumanComputerInteraction课程代码03M0211学分3.5总学时56理论学时40实验/实践学时16课程类别专业课课程性质专业必修先修课程ArtificialIntelligence适用专业电子信息工程开课学院信息工程学院一、课程地位与课程目标（一）课程地位人机交互应用是计算机科学的一个重要分支，人机交互的应用十分广泛，可以应用到人工智能、模式识别、机器学习、图像处理、图形处理等等方面。但总体来说，人机交互是一种手段，通过计算机辅助来使人类的生活便捷化和现代化。本课程将着重研究计算机辅助手段在生活中的各种应用。结合算法（包括人工智能和机器学习），通过游戏手段让学生领略到计算机在生活当中的重要作用。（二）课程目标课程目标毕业要求目标分类1.人机交互研究的发展和基本原则理解人机交互的基本概念、研究领域、应用领域，了解人机交互未来发展的争论和展望。问题分析设计/开发解决方法记忆理解分析2.人工智能方法（主要是搜索方法）的介绍理解搜索的基本概念，掌握状态空间的盲目搜索和启发式搜索、与/或树的盲目搜索和启发式搜索、搏弈搜索等策略。掌握自然演绎推理和归结演绎推理的基本原理。问题分析设计/开发解决方法分析应用3.机器学习是目前热门的研究领域。在一定程度上来说，机器学习强化了人工智能在科学及工程方面的作用而弱化了人机交互的重要性，但这也是当前科技发展的方向。问题分析设计/开发解决方法分析应用评价二、课程目标达成的途径与方法课程目标教学环节对应内容课程目标1人机交互研究的发展和基本原则1.OverviewofthehistoryofHumanComputerInteraction2.HCImethodologyintroduction3.State-of-arttoolsintroduction课程目标2人工智能方法（主要是搜索方法）的介绍4.Introductionofsearchmethods5.Introductionofsearchmethodsapplications课程目标3人机交互式的游戏设计机器学习6.Introductionofgameengines7.Introductionofmachinelearningingamedevelopmenthistory8.Intelligentgamedesign三、课程目标与相关毕业要求的对应关系课程目标课程目标对毕业要求的支撑程度（H、M、L）毕业要求2毕业要求3毕业要求4课程目标1H课程目标2HM课程目标3HH四、课程主要内容与基本要求1、OverviewofHCITheobjectiveofthisspecialintroductorysectionofthecourseistoprovidenewcomerstoHuman-ComputerInteraction(HCI)withanintroductionandoverviewofthefield.Thematerialwillbeginwithabriefhistoryofthefield,followedbypresentationanddiscussionofhowgoodapplicationdevelopmentmethodspullontheinterdisciplinarytechnologiesofHCI.Thetopicswillincludethepsychologyofhuman-computerinteraction,usabilityengineering,psychologically-baseddesignmethodsandtools,userinterfacemediaandtools,andintroductiontouserinterfacearchitecture.2、HCImethodologyWhileadoptingaknowledge-basedperspectiveonorganizationshasbeenvaluable,since,amongotherthings,itenablesustoseelinksbetweenorganizationallearningandafirm'scompetitiveadvantagethroughthedevelopmentofidiosyncraticcapabilities,ithasnonethelesstendedtotreatorganizationalknowledgeas‘given’,exploringhowitisrelatedtoother‘given’variables.Thefocusofthisspecialissueistounpackthenotionoforganizationalknowledgebyexploringtheprocessesandpracticesthroughwhichknowledgeisconstructedandcreatedinorganizations.Aconstructivistperspectiveassumesthat‘knowledge’presupposesworkandseekstoexplorehowwhatcomestobeconsideredasorganizationalknowledgeisestablishedandvalidated(orfailstodoso).Byseeingorganizationalknowledgeasworkwecanfurtherprobeintohowknowledgeisshapedbyorganizationalstrategiesandincentivesand,moreradically,howpowerandpoliticsinfluencethestrugglebetweencompetingbodiesofknowledgeinorganizations.3、Searchmethods

Thereareseveralsearchmethodsthattrytoemulatesomeprocessesobservedinnature.Themethodsinthisclasshavebeengivendifferentnames,suchasstochasticprogrammingmethods,evolutionaryalgorithms,metaheuristicmethods,andsoon.Themethodsalsofallintotheclassofdirectsearchmethodsthatdonotrequireuseofderivativesoffunctionsintheirsearch.Inthischapter,fourmethodsarepresentedandexplained:geneticalgorithms,differentialevolution(DE)(canbeconsideredasavariationofthegeneticalgorithm),antcolonyoptimization(ACO)andparticleswarmoptimization(PSO).Thebasicideasofthemethodsaredescribedandastep-by-stepalgorithmforeachispresented.Themethodsareusuallydiscussedfortheunconstrainedoptimizationproblem,assumingthattheconstraintscanbeincludedviaapenaltyfunctionapproach.Themethodstendtoconvergeintoaglobalminimumpoint,althoughthereisnoguarantee.Thereisnoprecisestoppingcriterionforsuchalgorithms;mostcomputefunctionvaluesatseveralthousandpointsandthusarecomputationallyquiteextensive.Theycanbeimplementedforparallelcomputationstospeedupthesearchforanacceptablesolution.Someexamplesarepresentedtoillustrateuseofsomeofthemethods4、MachinelearningThegoalofmachinelearningistoprogramcomputerstouseexampledataorpastexperiencetosolveagivenproblem.Manysuccessfulapplicationsofmachinelearningexistalready,includingsystemsthatanalyzepastsalesdatatopredictcustomerbehavior,optimizerobotbehaviorsothatataskcanbecompletedusingminimumresources,andextractknowledgefrombioinformaticsdata.isacomprehensivetextbookonthesubject,coveringabroadarrayoftopicsnotusuallyincludedinintroductorymachinelearningtexts.Inordertopresentaunifiedtreatmentofmachinelearningproblemsandsolutions,itdiscussesmanymethodsfromdifferentfields,includingstatistics,patternrecognition,neuralnetworks,artificialintelligence,signalprocessing,control,anddatamining.Alllearningalgorithmsareexplainedsothatthestudentcaneasilymovefromtheequationsinthebooktoacomputerprogram.Thetextcoverssuchtopicsassupervisedlearning,Bayesiandecisiontheory,parametricmethods,multivariatemethods,multilayerperceptrons,localmodels,hiddenMarkovmodels,assessingandcomparingclassificationalgorithms,andreinforcementlearning.Newtothesecondeditionarechaptersonkernelmachines,graphicalmodels,andBayesianestimation;expandedcoverageofstatisticaltestsinachapterondesignandanalysisofmachinelearningexperiments;casestudiesavailableontheWeb(withdownloadableresultsforinstructors);andmanyadditionalexercises.Allchaptershavebeenrevisedandupdated.canbeusedbyadvancedundergraduatesandgraduatestudentswhohavecompletedcoursesincomputerprogramming,probability,calculus,andlinearalgebra.Itwillalsobeofinteresttoengineersinthefieldwhoareconcernedwiththeapplicationofmachinelearningmethods.5、MachinelearningingamedevelopmentArtificialintelligencefordigitalgamesconstitutestheimplementationofasetofalgorithmsandtechniquesfrombothtraditionalandmodernartificialintelligenceinordertoprovidesolutionstoarangeofgamedependentproblems.However,themajorityofcurrentapproachesleadtopredefined,staticandpredictablegameagentresponses,withnoabilitytoadjustduringgame-playtothebehaviourorplayingstyleoftheplayer.Machinelearningtechniquesprovideawaytoimprovethebehaviouraldynamicsofcomputercontrolledgameagentsbyfacilitatingtheautomatedgenerationandselectionofbehaviours,thusenhancingthecapabilitiesofdigitalgameartificialintelligenceandprovidingtheopportunitytocreatemoreengagingandentertaininggame-playexperiences.Thissectionofthecourseprovidesasurveyofthecurrentstateofacademicmachinelearningresearchfordigitalgameenvironments,withrespecttotheuseoftechniquesfromneuralnetworks,evolutionarycomputationandreinforcementlearningforgameagentcontrol.五、课程学时安排章节号教学内容学时数学生任务对应课程目标1OverviewofHCI4Essaywriting课程目标12HCImethodologyintroduction6Essaywriting课程目标13State-of-arttoolsintroduction4Essaywriting课程目标24Introductionofsearchmethods6Essaywriting课程目标25Introductionofsearchmethodsapplications6Essaywriting课程目标26Introductionofgameengines4Essaywriting课程目标37Introductionofmachinelearningingamedevelopmenthistory6Essaywriting课程目标38Intelligentgamedesign4Essaywriting课程目标3六、实践环节及基本要求序号实验项目名称学时基本要求学生任务实验性质实验类别1Enrichinghandgesturedatabase2TakehandgesturephotosfromcameraCorrectgesturestaken验证性Singlechoice2Introductiontogameengine4UnderstandthegameenginecodesFindthesuitablegame验证性3Gamecontrolusinghand4InputcontrolcommandsusinghandInsertcodeintogameengine验证性4UCIdatabaseintroduction2SelecttherightdatasetforclassificationUnderstandthemeaningofdatasets验证性Multiplechoice5ClassificationusingKNN,DecisionTreeorSVM4Performclassificationandcalcu