人工智能课后习题答案部分已翻译考试Word文档下载推荐.docx
《人工智能课后习题答案部分已翻译考试Word文档下载推荐.docx》由会员分享，可在线阅读，更多相关《人工智能课后习题答案部分已翻译考试Word文档下载推荐.docx（17页珍藏版）》请在冰豆网上搜索。
Thisdependsonyourdefinitionof“intelligent”and“tell.”Inonesensecomputersonlydowhatthe
programmerscommandthemtodo,butinanothersensewhattheprogrammersconsciouslytellsthecomputertodooftenhasverylittletodowithwhatthecomputeractuallydoes.Anyonewhohaswrittenaprogramwithanornerybugknowsthis,asdoesanyonewhohaswrittenasuccessfulmachinelearningprogram.SoinonesenseSamuel“told”thecomputer“learntoplaycheckersbetterthanIdo,andthenplaythatway,”butinanothersensehetoldthecomputer“followthislearningalgorithm”anditlearnedtoplay.Sowe’releftinthesituationwhereyoumayormaynotconsiderlearningtoplaycheckerstobessignofintelligence（oryoumaythinkthatlearningtoplayintherightwayrequiresintelligence,butnotinthisway）,andyoumaythinktheintelligenceresidesintheprogrammerorinthecomputer
Chapter2
2.1Defineinyourownwordsthefollowingterms:
agent,agentfunction,agentprogram,rationality,reflexagent,model-basedagent,goal-basedagent,utility-basedagent,learningagent.
Thefollowingarejustsomeofthemanypossibledefinitionsthatcanbewritten:
•Agent智能体:
anentity（实体）thatperceives（感知）andacts行为;
or,onethatcanbeviewedasperceivingandacting.Essentially本质上anyobjectqualifies限定;
thekeypointisthewaytheobject
implementsanagentfunction.（Note:
someauthorsrestrictthetermtoprogramsthatoperateonbehalfofahuman,ortoprogramsthatcancausesomeoralloftheircodetorunonothermachinesonanetwork,asinmobileagents.MOBILEAGENT）
一个具有感知和行文的实体，或者是一个可以观察到感觉的实体，本质上，任何限定对象，只要的观
点是一种对象执行智能体函数的方法。
（注意，一些作者）可以感知环境，并在环境中行动的某种东西。
•Agentfunction智能体函数:
afunctionthatspecifiestheagent’sactioninresponsetoeverypossibleperceptsequence.智能体相应任何感知序列所采取的行动
•Agentprogram智能体程序:
thatprogramwhich,combinedwithamachinearchitecture,implementsanagentfunction.Inoursimpledesigns,theprogramtakesanewperceptoneachinvocationandreturnsan
action.实现了智能函数。
有各种基本的智能体程序设计，反应出现实表现的一级用于决策过程的信息种类。
设计可能在效率、压缩性和灵活性方面有变化。
适当的智能体程序设计取决于环境的本性
•Rationality；
理性:
apropertyofagentsthatchooseactionsthatmaximizetheirexpectedutility,giventheperceptstodate.
•Autonomy自主:
apropertyofagentswhosebehaviorisdeterminedbytheirownexperienceratherthansolelybytheirinitialprogramming.
•Reflexagent反射型智能体:
anagentwhoseactiondependsonlyonthecurrentpercept.
一个智能体的行为仅仅依赖于当前的知觉。
•Model-basedagent基于模型的智能体:
anagentwhoseactionisderiveddirectlyfromaninternalmodelofthecurrentworldstatethatisupdatedovertime.
一个智能体的行为直接得自于内在模型的状态，这个状态是当前世界通用的不断更新。
•Goal-basedagen基于目标的智能体t:
anagentthatselectsactionsthatitbelieveswillachieveexplicitlyrepresentedgoals.智能体选择它相信能明确达到目标的行动。
•Utility-basedagen基于效用的智能体t:
anagentthatselectsactionsthatitbelieveswillmaximizetheexpectedutilityoftheoutcomestate.试图最大化他们自己期望的快乐
•Learningagent学习智能体:
anagentwhosebehaviorimprovesovertimebasedonitsexperience.
2.2Boththeperformancemeasureandtheutilityfunctionmeasurehowwellanagentisdoing.Explainthedifferencebetweenthetwo.
Aperformancemeasure（性能度量）isusedbyanoutsideobservertoevaluate（评估）howsuccessfulan
agentis.Itisafunctionfromhistoriestoarealnumber.Autilityfunction（效用函数）isusedbyanagentitselftoevaluatehowdesirable（令人想要）statesorhistoriesare.Inourframework,theutilityfunction
maynotbethesameastheperformancemeasure;
furthermore,anagentmayhavenoexplicitutilityfunctionatall,whereasthereisalwaysaperformancemeasure.
2.5Foreachoffollowingagents,developaPEASdescriptionofthetaskenvironment:
a.Robotsoccerplayer;
b.Internetbook-shoppingagent;
c.AutonomousMarsrover;
d.Mathematician’stheorem-provingassistant.
Somerepresentative,butnotexhaustive,answersaregiveninFigureS2.1.
智能体类型性能度量环境执行器传感器
机器人足球运动员因特网购
赢得比赛，打败对手获得请求/感兴趣
裁判，自己队伍，
其他队伍，自己身体装置（腿）行走踢球向下连接，输入提
相机，触摸传感器加速器
书智能体
的书，最小支出因特网
交数据，用户显示器网页，用户请求
自主火星漫步者数学家的定理证明助手
地形探测，汇报，样本采集分析
火星，运行装置，登陆器
轮子/腿，简单手机装置，分析装置，无线电发射装置
相机，触摸传感器方向传感器
2.6ForeachoftheagenttypeslistedinExercise2.5,characterizetheenvironmentaccordingtothepropertiesgiveninSection2.3,andselectasuitableagentdesign.Thefollowingexercisesallconcerntheimplementationofenvironmentandagentsforthevacuum-cleanerworld.
EnvironmentpropertiesaregiveninFigureS2.2.Suitableagenttypes:
a.Amodel-basedreflexagentwouldsufficeformostaspects;
fortacticalplay,autilitybasedagentwithlookaheadwouldbeuseful.基于模型的映射能够满足大多数要求，对于战术游戏，向前效用智能体将会有用
b.Agoal-basedagentwouldbeappropriateforspecificbookrequests.Formoreopenendedtasks—e.g.,“Findmesomethinginterestingtoread”—tradeoffs（权衡折中）areinvolved（棘手的）andtheagentmustcompareutilitiesforvarious（不同的）possiblepurchases.基于目标的智能体将适当的明确书的请求，为更多开放的任务，例如查找我有兴趣读的书，智能体必须比较各种可能的购买方式之间的效用
c.Amodel-basedreflexagentwouldsufficeforlow-levelnavigationandobstacleavoidance;
forroute
planning,explorationplanning,experimentation,etc.,somecombinationofgoal-basedandutility-basedagentswouldbeneeded.基于模型的映射智能体能够满足低水平的航线和避免障碍，为了路由计划，探测计划，实验等。
这需要基于目标和效用的智能体。
d.Forspecificprooftasks,agoal-basedagentisneeded.For“exploratory”tasks—e.g.,“Provesomeusefullemmataconcerningoperationsonstrings”—autility-basedarchitecturemightbeneeded.
为了明确的检验任务，需要基于目标的智能体，为探测任务，
任务环境
机器人足
可观察性
确定性
片段性
静态性
离散型
智能体数
球运动员
部分
随机的
连续的
动态的
多
因特网购
确定的
静态的
离散的
单
自主火星
漫步者
数学家的定理
证明助手
完全
3.1Defineinyourownwordsthefollowingterms:
state,statespace,searchtree,searchnode,goal,action,successorfunction,andbranchingfactor.
•state:
Astateisasituationthatanagentcanfinditselfin.Wedistinguishtwotypesofstates:
worldstates（theactualconcretesituationsintherealworld）andrepresentationalstates（theabstractdescriptionsoftherealworldthatareusedbytheagentindeliberatingaboutwhattodo）.
•statespace:
Astatespaceisagraphwhosenodesarethesetofallstates,andwhoselinksareactionsthattransformonestateintoanother.
•searchtree:
Asearchtreeisatree（agraphwithnoundirectedloops）inwhichtherootnodeisthestartstateandthesetofchildrenforeachnodeconsistsofthestatesreachablebytakinganyaction.
•searchnode:
Asearchnodeisanodeinthesearchtree.
•goal:
Agoalisastatethattheagentistryingtoreach.
•action:
Anactionissomethingthattheagentcanchoosetodo.
•successorfunction:
Asuccessorfunctiondescribedtheagent’soptions:
givenastate,itreturnsasetof
（action,state）pairs,whereeachstateisthestatereachablebytakingtheaction.
•branchingfactor:
Thebranchingfactorinasearchtreeisthenumberofactionsavailabletotheagent.
3.7Givetheinitialstate,goaltest,successorfunction,andcostfunctionforeachofthefollowing.Chooseaformulationthatispreciseenoughtobeimplemented.
a.Youhavetocoloraplanarmapusingonlyfourcolors,insuchawaythatnotwoadjacentregionshavethesamecolor.
Initialstate:
Noregionscolored.
Goaltest:
Allregionscolored,andnotwoadjacentregionshavethesamecolor.Successorfunction:
Assignacolortoaregion.
Costfunction:
Numberofassignments.路径耗损
b.A3-foot-tallmonkeyisinaroomwheresomebananasaresuspendedfromthe8-footceiling.Hewouldliketogetthebananas.Theroomcontainstwostackable,movable,climbable3-foot-highcrates.
Asdescribedinthetext.初始状态：
Monkeyhasbananas.目标测试：
猴子拿到香蕉
后继函数:
Hoponcrate;
Hopoffcrate;
Pushcratefromonespottoanother;
Walkfromonespottoanother;
grabbananas（ifstandingoncrate）.挪动箱子，，把箱子叠起，走到箱子上拿香蕉
Numberofactions.行动数量
c.Youhaveaprogramthatoutputsthemessage“illegalinputrecord”whenfedacertainfileofinputrecords.Youknowthatprocessingofeachrecordisindependentoftheotherrecords.Youwanttodiscoverwhatrecordisillegal.
consideringallinputrecords.
consideringasinglerecord,anditgives“illegalinput”message.
Successorfunction:
runagainonthefirsthalfoftherecords;
runagainonthesecondhalfoftherecords.
Numberofruns.
Note:
Thisisacontingencyproblem;
youneedtoseewhetherarungivesanerrormessageornottodecidewhattodonext.
d.Youhavethreejugs,measuring12gallons,8gallons,and3gallons,andawaterfaucet.Youcanfillthejugsuporemptythemoutfromonetoanotherorontotheground.Youneedtomeasureoutexactlyonegallon.
jugshavevalues[0,0,0].
givenvalues[x,y,z],generate[12,y,z],[x,8,z],[x,y,3]（byfilling）;
[0,y,z],[x,0,z],[x,y,0]（byemptying）;
orforanytwojugswithcurrentvaluesxandy,pouryintox;
thischangesthejugwithxtotheminimumofx+yandthecapacityofthejug,anddecrementsthejugwithybytheamountgainedbythefirstjug.
Numberofactions.
3.8Consider
升级会员 