Introduction to MIS - Chapter 9: Complex Decisions and Artificial Intelligence

Introduction to MISChapter 9Complex Decisions and Artificial IntelligenceComputer analysisof data and model.DecisionOperationsTacticsStrategyNeural networkCompanyComplex Decisions& Artificial IntelligenceOutlineSpecialized ProblemsExpert SystemsDSS and ESBuilding Expert SystemsKnowledge ManagementOther Specialized ProblemsPattern RecognitionDSS, ES, and AIMachine IntelligenceE-Business and Software AgentsCases: FranchisesAppendix: E-mail RulesSpecialized ProblemsDiagnosticsSpeedConsistencyTrainingCase-based reasoningExpert System ExampleCamcorder selection by ExSysLink: ItExpert SystemKnowledge BaseSymbolic & Numeric KnowledgeIf income > 20,000or expenses k cluster-size) do (for (j 0 (+ 1 j )) exit when (= j k) do (connect unit cluster k output o -A to unit cluster j input i - A )) . . . )Maintained by expert system shellProgrammerCustom program in LISPES DevelopmentES ShellsGuruExsysCustom ProgrammingLISPPROLOGSome Expert System ShellsCLIPSOriginally developed at NASAWritten in CAvailable free or at low cost in JavaGood for Web applicationsAvailable free or at low cost system with many featureswww.exsys.comLimitations of ESFragile systemsSmall environmental. changes can force revision. of all of the rules.MistakesWho is responsible?Expert?Multiple experts?Knowledge engineer?Company that uses it?Vague rulesRules can be hard to define.Conflicting expertsWith multiple opinions, who is right?Can diverse methods be combined?Unforeseen eventsEvents outside of domain can lead to nonsense decisions.Human experts adapt.Will human novice recognize a nonsense result?Knowledge ManagementA collection of a documents and dataCreated by expertsSearchableWith links to related topicsHighly organized groupwareEmphasizing contextExample—business decisionsStore problem, all notes, decision factors, commentsFuture problems, managers can search the database and find similar problemsBetter and more efficient decisions if you know the original problems, discussions, and contingency plansMain problem—convincing everyone to enter and update the documentsAI Research AreasComputer ScienceParallel ProcessingSymbolic ProcessingNeural NetworksRobotics ApplicationsVisual PerceptionTactilityDexterityLocomotion & NavigationNatural LanguageSpeech RecognitionLanguage TranslationLanguage ComprehensionCognitive ScienceExpert SystemsLearning SystemsKnowledge-Based SystemsOutput CellsSensory Input CellsHidden LayerSome of the connections3-274Input weightsIncompletepattern/missing inputs.Neural Network: Pattern recognitionMachine Vision ExampleThe Department of Defense has funded Carnegie Mellon University to develop software that is used to automatically drive vehicles. One system (Ranger) is used in an army ambulance that can drive itself over rough terrain for up to 16 km. ALVINN is a separate road-following system that has driven vehicles at speeds over 110 kph for as far as 140 km.Speech RecognitionLook at the user’s voice command:Copy the red, file the blue, delete the yellow mark.Now, change the commas slightly.Copy the red file, the blue delete, the yellow mark.I saw the Grand Canyon flying to New York.EmergencyVehiclesNoParkingAny TimeSubjective Definitionstemperaturereference pointe.g., averagetemperaturecoldhotMoving farther from the reference pointincreases the chance that the temperature isconsidered to be different (cold or hot).Subjective (fuzzy) DefinitionsDSS, ES, and AI: Bank ExampleDecision Support SystemExpert SystemArtificial IntelligenceName Loan #Late AmountBrown 25,000 5 1,250Jones 62,000 1 135Smith 83,000 3 2,435...DataIncomeExisting loansCredit reportModelLend in all but worst casesMonitor for late and missing payments.OutputES RulesWhat is the monthly income?3,000What are the total monthly payments on other loans? 450How long have they had the current job? 5 years. . .Should grant the loan since there is only a 5% chance of default.Determine Rulesloan 1 data: paidloan 2 data: 5 lateloan 3 data: lostloan 4 data: 1 lateData/Training CasesNeural Network WeightsEvaluate new data,make recommendation.Loan OfficerDecision Support SystemExpert SystemArtificial IntelligenceDataa estimate salesK order setup costh estimate holding costModelQ* = sqrt ( 2ak / h )OutputtimeQ*Inventory Levelsreorder pointsChoosing an Inventory SystemWhat is the cost of running out of inventory? 45,000 per dayWhat are daily profits? 250,000How many suppliers are there? 8Can more suppliers be added in an emergency? noHow close is the nearest supplier? 10 kilometresHow reliable is this supplier? very. . .Best choice is to use Just-In-Time inventory system. Only a 2% chance of running out of inventory for more than 2 days. . . .Automatically Analyzesite 1 data: JITsite 2 data: EOQsite 3 data: JITsite 4 data: hybridData/Training CasesNeural Network WeightsEvaluate new data,make recommendation.DSS, ES and AI: Inventory ExampleVacation ResortsSoftware agentResort DatabasesLocate &book trip.Software AgentsIndependentNetworks/CommunicationUsesSearchNegotiateMonitorAI QuestionsWhat is intelligence?Creativity?Learning?Memory?Ability to handle unexpected events?More?Can machines ever think like humans?How do humans think?Do we really want them to think like us?Cases: FranchisesCases: Mrs. FieldsBlockbuster VideoWhat is the company’s current status?What is the Internet strategy?How does the company use information technology?What are the prospects for the industry?www.mrsfields.comwww.blockbuster.comAppendix: E-Mail Rules - FoldersFolders make it easy to organize and handle your mail.Simple rules from the Tools + Organize button move messages directly to the specified folder.Rules: ConditionsThe Tools + Rules Wizard makes it easy to create rules. Begin with a blank rule.Set the ConditionsSet the ActionsDefine ExceptionsA sample rule to handle unsolicited credit card applications.Rules: ActionsChoose an action.You can choose multiple actions, but be careful. The marking options are often combined.Rules: ExceptionsRules can have exceptions. For example, you might want to delete company newsletters—unless one has your name in it.Rule Sequences: Decision TreeFrom boss, Subject: ExpensesMessage from ExpenseAccountingExpenses FolderSet expenses categoryMove itRule 1Rule 2Expenses categorySubject: PaymentRule 3Action: Mark important and notify.