nodegraphsearch
graphsearch 时间:2021-05-25 阅读:()
CS347–IntroductiontoArtificialIntelligenceDr.
DanielTauritz(Dr.
T)DepartmentofComputerSciencetauritzd@mst.
eduhttp://web.
mst.
edu/~tauritzd/CS347coursewebsite:http://web.
mst.
edu/~tauritzd/courses/cs347/WhatisAISystemsthat…actlikehumans(TuringTest)thinklikehumansthinkrationallyactrationallyPlayUltimatumGameKeyhistoricaleventsforAI4thcenturyBCAristotlepropositionallogic1600'sDescartesmind-bodyconnection1805FirstprogrammablemachineMid1800'sCharlesBabbage's"differenceengine"&"analyticalengine"LadyLovelace'sObjection1847GeorgeBoolepropositionallogic1879GottlobFregepredicatelogicKeyhistoricaleventsforAI1931KurtGodel:IncompletenessTheoremInanylanguageexpressiveenoughtodescribenaturalnumberproperties,thereareundecidable(incomputable)truestatements1943McCulloch&Pitts:NeuralComputation1956Term"AI"coined1976Newell&Simon's"PhysicalSymbolSystemHypothesis"Aphysicalsymbolsystemhasthenecessaryandsufficientmeansforgeneralintelligentaction.
HowdifficultisittoachieveAIThreeSistersPuzzleRationalAgentsEnvironmentSensors(percepts)Actuators(actions)AgentFunctionAgentProgramPerformanceMeasuresRationalBehaviorDependson:Agent'sperformancemeasureAgent'spriorknowledgePossibleperceptsandactionsAgent'sperceptsequenceRationalAgentDefinition"Foreachpossibleperceptsequence,arationalagentselectsanactionthatisexpectedtomaximizeitsperformancemeasure,giventheevidenceprovidedbytheperceptsequenceandanypriorknowledgetheagenthas.
"TaskEnvironmentsPEASdescription&properties:Fully/PartiallyObservableDeterministic,Stochastic,StrategicEpisodic,SequentialStatic,Dynamic,Semi-dynamicDiscrete,ContinuousSingleagent,MultiagentCompetitive,CooperativeProblem-solvingagentsAdefinition:Problem-solvingagentsaregoalbasedagentsthatdecidewhattodobasedonanactionsequenceleadingtoagoalstate.
Problem-solvingstepsProblem-formulation(actions&states)Goal-formulation(states)Search(actionsequences)ExecutesolutionWell-definedproblemsInitialstateActionsetTransitionmodel:RESULT(s,a)GoaltestPathcostSolution/optimalsolutionExampleproblemsVacuumworldTic-tac-toe8-puzzle8-queensproblemSearchtreesRootcorrespondswithinitialstateVacuumstatespacevs.
searchtreeSearchalgorithmsiteratethroughgoaltestingandexpandingastateuntilgoalfoundOrderofstateexpansioniscritical!
Searchnodedatastructuren.
STATEn.
PARENT-NODEn.
ACTIONn.
PATH-COSTStatesareNOTsearchnodes!
FrontierFrontier=SetofleafnodesImplementedasaqueuewithops:EMPTY(queue)POP(queue)INSERT(element,queue)Queuetypes:FIFO,LIFO(stack),andpriorityqueueProblem-solvingperformanceCompletenessOptimalityTimecomplexitySpacecomplexityComplexityinAIb–branchingfactord–depthofshallowestgoalnodem–maxpathlengthinstatespaceTimecomplexity:#generatednodesSpacecomplexity:max#nodesstoredSearchcost:time+spacecomplexityTotalcost:search+pathcostTreeSearchBreadthFirstTreeSearch(BFTS)UniformCostTreeSearch(UCTS)Depth-FirstTreeSearch(DFTS)Depth-LimitedTreeSearch(DLTS)Iterative-DeepeningDepth-FirstTreeSearch(ID-DFTS)Examplestatespace#1BreadthFirstTreeSearch(BFTS)Frontier:FIFOqueueComplete:ifbanddarefiniteOptimal:ifpath-costisnon-decreasingfunctionofdepthTimecomplexity:O(b^d)Spacecomplexity:O(b^d)UniformCostTreeSearch(UCTS)Frontier:priorityqueueorderedbyg(n)DepthFirstTreeSearch(DFTS)Frontier:LIFOqueue(a.
k.
a.
stack)Complete:noOptimal:noTimecomplexity:O(bm)Spacecomplexity:O(bm)BacktrackingversionofDFTShasaspacecomplexityof:O(m)Depth-LimitedTreeSearch(DLTS)Frontier:LIFOqueue(a.
k.
a.
stack)Complete:notwhenl=βPruneiffail-lowforMin-playerPruneiffail-highforMax-playerDLMw/Alpha-BetaPruningTimeComplexityWorst-case:O(bd)Best-case:O(bd/2)[Knuth&Moore,1975]Average-case:O(b3d/4)MoveOrderingHeuristicsKnowledgebasedKillerMove:thelastmoveatagivendepththatcausedAB-pruningorhadbestminimaxvalueHistoryTableExamplegametreeExamplegametreeSearchDepthHeuristicsTimebased/StatebasedHorizonEffect:thephenomenonofdecidingonanon-optimalprincipalvariantbecauseanultimatelyunavoidabledamagingmoveseemstobeavoidedbyblockingittillpassedthesearchdepthSingularExtensions/QuiescenceSearchTimePerMoveConstantPercentageofremainingtimeStatedependentHybridQuiescenceSearchWhensearchdepthreached,computequiescencestateevaluationheuristicIfstatequiescent,thenproceedasusual;otherwiseincreasesearchdepthifquiescencesearchdepthnotyetreachedCallformat:QSDLM(root,depth,QSdepth),QSABDLM(root,depth,QSdepth,α,β),etc.
QSgametreeEx.
1QSgametreeEx.
2ForwardpruningBeamSearch(nbestmoves)ProbCut(forwardpruningversionofalpha-betapruning)TranspositionTables(1)HashtableofpreviouslycalculatedstateevaluationheuristicvaluesSpeedupisparticularlyhugeforiterativedeepeningsearchalgorithms!
GoodforchessbecauseoftenrepeatedstatesinsamesearchTranspositionTables(2)Datastructure:HashtableindexedbypositionElement:StateevaluationheuristicvalueSearchdepthofstoredvalueHashkeyofposition(toeliminatecollisions)(optional)BestmovefrompositionTranspositionTables(3)ZobristhashkeyGenerate3d-arrayofrandom64-bitnumbers(piecetype,locationandcolor)Startwitha64-bithashkeyinitializedto0Loopthroughcurrentposition,XOR'inghashkeywithZobristvalueofeachpiecefound(note:onceakeyhasbeenfound,useanincrementalapporachthatXOR'sthe"from"locationandthe"to"locationtomoveapiece)MTD(f)MTDf(root,guess,depth){lower=-∞;upper=∞;do{beta=guess+(guess==lower);guess=ABMaxV(root,depth,beta-1,beta);if(guessExtendedFutilityPruningRazoringState-SpaceSearchComplete-stateformulationObjectivefunctionGlobaloptimaLocaloptima(don'tusetextbook'sdefinition!
)Ridges,plateaus,andshouldersRandomsearchandlocalsearchSteepest-AscentHill-ClimbingGreedyAlgorithm-makeslocallyoptimalchoicesExample8queensproblemhas88≈17MstatesSAHCfindsglobaloptimumfor14%ofinstancesinonaverage4steps(3stepswhenstuck)SAHCw/upto100consecutivesidewaysmoves,findsglobaloptimumfor94%ofinstancesinonaverage21steps(64stepswhenstuck)StochasticHill-ClimbingChoosesatrandomfromamonguphillmovesProbabilityofselectioncanvarywiththesteepnessoftheuphillmoveOnaverageslowerconvergence,butalsolesschanceofprematureconvergenceMoreLocalSearchAlgorithmsFirst-choicehill-climbingRandom-restarthill-climbingSimulatedAnnealingPopulationBasedLocalSearchDeterministiclocalbeamsearchStochasticlocalbeamsearchEvolutionaryAlgorithmsParticleSwarmOptimizationAntColonyOptimizationParticleSwarmOptimizationPSOisastochasticpopulation-basedoptimizationtechniquewhichassignsvelocitiestopopulationmembersencodingtrialsolutionsPSOupdaterules:PSOdemo:http://www.
borgelt.
net//psopt.
htmlAntColonyOptimizationPopulationbasedPheromonetrailandstigmergeticcommunicationShortestpathsearchingStochasticmovesOnlineSearchOfflinesearchvs.
onlinesearchInterleavingcomputation&actionExplorationproblems,safelyexplorableAgentshaveaccessto:ACTIONS(s)c(s,a,s')GOAL-TEST(s)OnlineSearchOptimalityCR–CompetitiveRatioTAPC–TotalActualPathCostC*-OptimalPathCostBestcase:CR=1Worstcase:CR=∞OnlineSearchAlgorithmsOnline-DFS-AgentRandomWalkLearningReal-TimeA*(LRTA*)OnlineSearchExampleGraph1OnlineSearchExampleGraph2OnlineSearchExampleGraph3AIcoursesatS&TCS345ComputationalRoboticManipulation(SP2012)CS347IntroductiontoArtificialIntelligence(SP2012)CS348EvolutionaryComputing(FS2011)CS434DataMining&KnowledgeDiscovery(FS2011)CS447AdvancedTopicsinAI(SP2013)CS448AdvancedEvolutionaryComputing(SP2012)CpE358ComputationalIntelligence(FS2011)SysEng378IntrotoNeuralNetworks&Applications
DanielTauritz(Dr.
T)DepartmentofComputerSciencetauritzd@mst.
eduhttp://web.
mst.
edu/~tauritzd/CS347coursewebsite:http://web.
mst.
edu/~tauritzd/courses/cs347/WhatisAISystemsthat…actlikehumans(TuringTest)thinklikehumansthinkrationallyactrationallyPlayUltimatumGameKeyhistoricaleventsforAI4thcenturyBCAristotlepropositionallogic1600'sDescartesmind-bodyconnection1805FirstprogrammablemachineMid1800'sCharlesBabbage's"differenceengine"&"analyticalengine"LadyLovelace'sObjection1847GeorgeBoolepropositionallogic1879GottlobFregepredicatelogicKeyhistoricaleventsforAI1931KurtGodel:IncompletenessTheoremInanylanguageexpressiveenoughtodescribenaturalnumberproperties,thereareundecidable(incomputable)truestatements1943McCulloch&Pitts:NeuralComputation1956Term"AI"coined1976Newell&Simon's"PhysicalSymbolSystemHypothesis"Aphysicalsymbolsystemhasthenecessaryandsufficientmeansforgeneralintelligentaction.
HowdifficultisittoachieveAIThreeSistersPuzzleRationalAgentsEnvironmentSensors(percepts)Actuators(actions)AgentFunctionAgentProgramPerformanceMeasuresRationalBehaviorDependson:Agent'sperformancemeasureAgent'spriorknowledgePossibleperceptsandactionsAgent'sperceptsequenceRationalAgentDefinition"Foreachpossibleperceptsequence,arationalagentselectsanactionthatisexpectedtomaximizeitsperformancemeasure,giventheevidenceprovidedbytheperceptsequenceandanypriorknowledgetheagenthas.
"TaskEnvironmentsPEASdescription&properties:Fully/PartiallyObservableDeterministic,Stochastic,StrategicEpisodic,SequentialStatic,Dynamic,Semi-dynamicDiscrete,ContinuousSingleagent,MultiagentCompetitive,CooperativeProblem-solvingagentsAdefinition:Problem-solvingagentsaregoalbasedagentsthatdecidewhattodobasedonanactionsequenceleadingtoagoalstate.
Problem-solvingstepsProblem-formulation(actions&states)Goal-formulation(states)Search(actionsequences)ExecutesolutionWell-definedproblemsInitialstateActionsetTransitionmodel:RESULT(s,a)GoaltestPathcostSolution/optimalsolutionExampleproblemsVacuumworldTic-tac-toe8-puzzle8-queensproblemSearchtreesRootcorrespondswithinitialstateVacuumstatespacevs.
searchtreeSearchalgorithmsiteratethroughgoaltestingandexpandingastateuntilgoalfoundOrderofstateexpansioniscritical!
Searchnodedatastructuren.
STATEn.
PARENT-NODEn.
ACTIONn.
PATH-COSTStatesareNOTsearchnodes!
FrontierFrontier=SetofleafnodesImplementedasaqueuewithops:EMPTY(queue)POP(queue)INSERT(element,queue)Queuetypes:FIFO,LIFO(stack),andpriorityqueueProblem-solvingperformanceCompletenessOptimalityTimecomplexitySpacecomplexityComplexityinAIb–branchingfactord–depthofshallowestgoalnodem–maxpathlengthinstatespaceTimecomplexity:#generatednodesSpacecomplexity:max#nodesstoredSearchcost:time+spacecomplexityTotalcost:search+pathcostTreeSearchBreadthFirstTreeSearch(BFTS)UniformCostTreeSearch(UCTS)Depth-FirstTreeSearch(DFTS)Depth-LimitedTreeSearch(DLTS)Iterative-DeepeningDepth-FirstTreeSearch(ID-DFTS)Examplestatespace#1BreadthFirstTreeSearch(BFTS)Frontier:FIFOqueueComplete:ifbanddarefiniteOptimal:ifpath-costisnon-decreasingfunctionofdepthTimecomplexity:O(b^d)Spacecomplexity:O(b^d)UniformCostTreeSearch(UCTS)Frontier:priorityqueueorderedbyg(n)DepthFirstTreeSearch(DFTS)Frontier:LIFOqueue(a.
k.
a.
stack)Complete:noOptimal:noTimecomplexity:O(bm)Spacecomplexity:O(bm)BacktrackingversionofDFTShasaspacecomplexityof:O(m)Depth-LimitedTreeSearch(DLTS)Frontier:LIFOqueue(a.
k.
a.
stack)Complete:notwhenl=βPruneiffail-lowforMin-playerPruneiffail-highforMax-playerDLMw/Alpha-BetaPruningTimeComplexityWorst-case:O(bd)Best-case:O(bd/2)[Knuth&Moore,1975]Average-case:O(b3d/4)MoveOrderingHeuristicsKnowledgebasedKillerMove:thelastmoveatagivendepththatcausedAB-pruningorhadbestminimaxvalueHistoryTableExamplegametreeExamplegametreeSearchDepthHeuristicsTimebased/StatebasedHorizonEffect:thephenomenonofdecidingonanon-optimalprincipalvariantbecauseanultimatelyunavoidabledamagingmoveseemstobeavoidedbyblockingittillpassedthesearchdepthSingularExtensions/QuiescenceSearchTimePerMoveConstantPercentageofremainingtimeStatedependentHybridQuiescenceSearchWhensearchdepthreached,computequiescencestateevaluationheuristicIfstatequiescent,thenproceedasusual;otherwiseincreasesearchdepthifquiescencesearchdepthnotyetreachedCallformat:QSDLM(root,depth,QSdepth),QSABDLM(root,depth,QSdepth,α,β),etc.
QSgametreeEx.
1QSgametreeEx.
2ForwardpruningBeamSearch(nbestmoves)ProbCut(forwardpruningversionofalpha-betapruning)TranspositionTables(1)HashtableofpreviouslycalculatedstateevaluationheuristicvaluesSpeedupisparticularlyhugeforiterativedeepeningsearchalgorithms!
GoodforchessbecauseoftenrepeatedstatesinsamesearchTranspositionTables(2)Datastructure:HashtableindexedbypositionElement:StateevaluationheuristicvalueSearchdepthofstoredvalueHashkeyofposition(toeliminatecollisions)(optional)BestmovefrompositionTranspositionTables(3)ZobristhashkeyGenerate3d-arrayofrandom64-bitnumbers(piecetype,locationandcolor)Startwitha64-bithashkeyinitializedto0Loopthroughcurrentposition,XOR'inghashkeywithZobristvalueofeachpiecefound(note:onceakeyhasbeenfound,useanincrementalapporachthatXOR'sthe"from"locationandthe"to"locationtomoveapiece)MTD(f)MTDf(root,guess,depth){lower=-∞;upper=∞;do{beta=guess+(guess==lower);guess=ABMaxV(root,depth,beta-1,beta);if(guess
)Ridges,plateaus,andshouldersRandomsearchandlocalsearchSteepest-AscentHill-ClimbingGreedyAlgorithm-makeslocallyoptimalchoicesExample8queensproblemhas88≈17MstatesSAHCfindsglobaloptimumfor14%ofinstancesinonaverage4steps(3stepswhenstuck)SAHCw/upto100consecutivesidewaysmoves,findsglobaloptimumfor94%ofinstancesinonaverage21steps(64stepswhenstuck)StochasticHill-ClimbingChoosesatrandomfromamonguphillmovesProbabilityofselectioncanvarywiththesteepnessoftheuphillmoveOnaverageslowerconvergence,butalsolesschanceofprematureconvergenceMoreLocalSearchAlgorithmsFirst-choicehill-climbingRandom-restarthill-climbingSimulatedAnnealingPopulationBasedLocalSearchDeterministiclocalbeamsearchStochasticlocalbeamsearchEvolutionaryAlgorithmsParticleSwarmOptimizationAntColonyOptimizationParticleSwarmOptimizationPSOisastochasticpopulation-basedoptimizationtechniquewhichassignsvelocitiestopopulationmembersencodingtrialsolutionsPSOupdaterules:PSOdemo:http://www.
borgelt.
net//psopt.
htmlAntColonyOptimizationPopulationbasedPheromonetrailandstigmergeticcommunicationShortestpathsearchingStochasticmovesOnlineSearchOfflinesearchvs.
onlinesearchInterleavingcomputation&actionExplorationproblems,safelyexplorableAgentshaveaccessto:ACTIONS(s)c(s,a,s')GOAL-TEST(s)OnlineSearchOptimalityCR–CompetitiveRatioTAPC–TotalActualPathCostC*-OptimalPathCostBestcase:CR=1Worstcase:CR=∞OnlineSearchAlgorithmsOnline-DFS-AgentRandomWalkLearningReal-TimeA*(LRTA*)OnlineSearchExampleGraph1OnlineSearchExampleGraph2OnlineSearchExampleGraph3AIcoursesatS&TCS345ComputationalRoboticManipulation(SP2012)CS347IntroductiontoArtificialIntelligence(SP2012)CS348EvolutionaryComputing(FS2011)CS434DataMining&KnowledgeDiscovery(FS2011)CS447AdvancedTopicsinAI(SP2013)CS448AdvancedEvolutionaryComputing(SP2012)CpE358ComputationalIntelligence(FS2011)SysEng378IntrotoNeuralNetworks&Applications
- nodegraphsearch相关文档
- resultsgraphsearch
- reciprocatedgraphsearch
- memorygraphsearch
- graphgraphsearch
- swappedgraphsearch
- annotategraphsearch
SugarHosts糖果主机商更换域名
昨天，遇到一个网友客户告知他的网站无法访问需要帮他检查到底是什么问题。这个同学的网站是我帮他搭建的，于是我先PING看到他的网站是不通的，开始以为是服务器是不是出现故障导致无法打开的。检查到他的服务器是有放在SugarHosts糖果主机商中，于是我登录他的糖果主机后台看到服务器是正常运行的。但是，我看到面板中的IP地址居然是和他网站解析的IP地址不同。看来官方是有更换域名。于是我就问 客服到底是什...
[6.18]IMIDC：香港/台湾服务器月付30美元起,日本/俄罗斯服务器月付49美元起
IMIDC发布了6.18大促销活动，针对香港、台湾、日本和莫斯科独立服务器提供特别优惠价格最低月付30美元起。IMIDC名为彩虹数据(Rainbow Cloud)，是一家香港本土运营商，全线产品自营，自有IP网络资源等，提供的产品包括VPS主机、独立服务器、站群独立服务器等，数据中心区域包括香港、日本、台湾、美国和南非等地机房，CN2网络直连到中国大陆。香港服务器 $39/...
MineServer：香港CMI/洛杉矶GIA VPS,2核/2GB内存/20GB NVME/3.5TB流量/200Mbps/KVM,288元/年
mineserver怎么样?mineserver是一家国人商家，主要提供香港CN2 KVM VPS、香港CMI KVM VPS、日本CN2 KVM VPS、洛杉矶cn2 gia端口转发等服务，云服务器网(yuntue.com)介绍过几次，最近比较活跃。现在新推出了3款特价KVM VPS，性价比高，香港CMI/洛杉矶GIA VPS，2核/2GB内存/20GB NVME/3.5TB流量/200Mbps...
graphsearch为你推荐
-
朝阳分局犯罪嫌疑人标准化信息采集系统中南财经政法大学知识产权研究中心229.254route浙江世纪华通集团股份有限公司支持ipadcss3圆角怎样用css实现圆角矩形？127.0.0.1127.0.0.1打不开联通版iphone4s怎么知道到苹果4s是联通版，还是移动版重庆电信宽带管家如何才能以正确的流程在重庆电信安装上宽带谷歌sb为什么百度一搜SB是谷歌，谷歌一搜SB是百度？