2 Introduction and Contact InformationName: Dr Priti Srinivas SajjaCommunication:Mobile :URL:Academic Qualifications: Ph. D in Computer ScienceThesis Title: Knowledge-Based Systems for Socio-EconomicRural DevelopmentSubject area of specialization : Knowledge-Based SystemsPublications : 84 in International/ National Journals and Conferences(Including two books and one chapter)Academic Position : Associate Professor atDepartment of Computer ScienceSardar Patel UniversityVallabh Vidyanagar
3 Outlines of the Lecture Part 1: Artificial IntelligenceNatural intelligence and Artificial IntelligenceNature of AI SolutionsTesting IntelligenceCategories of Application AreasPart 2: Symbolic Knowledge-Based SystemsData Pyramid and CBISDBMS and KBSStructure of KBSTypes of KBSExample KBSPart 3: Connectionist SystemsSymbolic and Connectionist SystemsExample ANN for Course Selection
4 Responds to situations flexibly. Natural IntelligenceResponds to situations flexibly.Makes sense of ambiguous or erroneous messages.Assigns relative importance to elements of a situation.Finds similarities even though the situations might be different.Draws distinctions between situations even though there may be many similarities between them.
5 Artificial Intelligence According to Rich & Knight (1991) “AI is the study of how to make computers do things, at which, at the moment, people are better”.A machine is regarded as intelligent if it exhibits human characteristics generated through natural intelligence.AI is the study of human thought processes and moving towards problem solving in a symbolic and non- algorithmic way.AI is the branch of Computer Science that attempts to solve problems by mimicking human thought process using heuristics, symbolic and non-algorithmic approach in areas where people are better.
6 Figure 1.1: Constituents of artificial intelligence Make Your Own Definition of AIwhere people are betterhuman thought processcharacteristics we associate with intelligenceknowledge using symbolsheuristic methodsnon-algorithmicAIFigure 1.1: Constituents of artificial intelligence
7 Figure 1.2: Nature of AI solutions Extreme solution, either best or worst taking (infinite) timetimeAcceptable solution in acceptable timeFigure 1.2: Nature of AI solutions
8 Figure 1.4: The Turing test Testing IntelligenceTuring test will fail to test for intelligence in two circumstances;A machine may well be intelligent without being able to chat exactly like a human; and;The test fails to capture the general properties of intelligence, such as the ability to solve difficult problems or come up with original insights. If a machine can solve a difficult problem that no person could solve, it would, in principle, fail the test.Can you tell me what is *67344?Why Sir?The Boss could not judge who was replying, thus the machine is as intelligent as the secretary.Figure 1.4: The Turing test
9 Application Areas of Artificial Intelligence Rich & Knight (1991) classified and described the different areas that Artificial Intelligence techniques have been applied to as follows:Mundane TasksPerception - vision and speechNatural language understanding, generation, and translationCommonsense reasoningRobot controlExpert TasksEngineering - design, fault finding, manufacturing planning, etc.Scientific analysisMedical diagnosisFinancial analysisFormal TasksGames - chess, backgammon, checkers, etc.Mathematics- geometry, logic, integral calculus, theorem proving, etc.
10 Data Pyramid and Computer Based Systems InformationKnowledgeWisdomUnderstandingExperienceNoveltyResearching Absorbing Doing Interacting ReflectingRaw Data through fact findingConceptsRulesHeuristics and modelsFigure 1.6: Convergence from data to intelligence
11 Data Pyramid and Computer-Based Systems Basic transactions by operational staff using data processingMiddle management uses reports/info. generated though analysis and acts accordinglyHigher management generates knowledge by synthesizing informationStrategy makers apply morals, principles, and experience to generate policiesWisdom (experience)Knowledge (synthesis)Information (analysis)Data (processing of raw observations )VolumeSophistication and complexityTPSDSS, MISKBSWBSISFigure 1.7: Data pyramid: Managerial perspectives
12 Computer-Based Information Systems Tree MISDSSEESESSESEISTPSOASFigure 1.8: CBIS tree199019701950Hardware base/technologyUsers’ requirementsISIntelligent systems: 21st century challengeEES:Executive expert system, which is a hybridization of an expert system , executive information system, and decision support systemSoftware resources
13 Comparison of KBS with Traditional CBIS Systems Traditional Computer-Based Information Systems (CBIS)Knowledge-Based Systems (KBS)Gives a guaranteed solution and concentrate on efficiencyAdds powers to the solution and concentrates on effectiveness without any guarantee of solutionData and/or information processing approachKnowledge and/or decision processing approachAssists in activities related to decision making and routine transactions; supports need for informationTransfer of expertise; takes a decision based on knowledge, explains it, and upgrades it, if requiredExamples are TPS, MIS, DSS, etc.Examples are expert systems, CASE-based systems, etc.Manipulation method is numeric and algorithmicManipulation method is primarily symbolic/connectionist and nonalgorithmicThese systems do not make mistakesThese systems learn by mistakesNeed complete information and/or dataPartial and uncertain information, data, or knowledge will doWorks for complex, integrated, and wide areas in a reactive mannerWorks for narrow domains in a reactive and proactive manner
14 Objectives of KBSKBS is an example of fifth-generation computer technology. Some of its objectives are as follows:Provides a high intelligence levelAssists people in discovering and developing unknown fieldsOffers a vast amount of knowledge in different areasAids in managementSolves social problems in better way than the traditional CBISAcquires new perceptions by simulating unknown situationsOffers significant software productivity improvementSignificantly reduces cost and time to develop computerized systems
15 Components of KBS Figure 1.10: General structure of KBS Knowledge base is a repository of domain knowledge and meta knowledge.Inference engine is a software program, which infers the knowledge available in the knowledge baseEnriches the system with self-learning capabilitiesKnowledge baseInference engineUser interfaceExplanationandreasoningSelf-learningFigure 1.10: General structure of KBSProvides explanation and reasoning facilitatesFriendly interface to users working in their native language
16 Intelligent tutoring systems Categories of KBSAccording to the classifications by Tuthhill & Levy (1991), five main types of KBS exists:Intelligent tutoring systemsExpert systemsLinked systemsCASE-based systemsDatabase in conjunction with an intelligent user interface
17 Difficulties with the KBS Completeness of Knowledge BaseCharacteristics of KnowledgeLarge Size of Knowledge BaseAcquisition of KnowledgeSlow Learning and Execution
18 An example of a Multi-agent KBS on Grid UsersExpertsUser Interface AgentAgentsLearning Mgt.Drills and QuizzesExplanationSemantic Search& ChatResource ManagementQuestion/AnswerTutorial PathDocumentationInternetGrid Middleware ServicesResource Management (Grid Resource Allocation Protocol-GRAM)andGrid FTP Replica-LocationServicesInformation Discovery ServicesSecurity ServicesDistributed databasesMiddleware Services and ProtocolsLocal Data-BasesResourcesKnowledge Mgt.Meta knowledgeConceptual systemContent knowledgeLearner’s ontologyMailDocumentsKnowledge DiscoveryKnowledge UtilizationKnowledge Management
19 Communication Between Agents Agents developed here are communicating with a tool named KQML.Knowledge based Query Management Language.(register : sender agent_Lerning_Mgt : receiver agent_Tutorail-Path : reply-with message : language common_language : ontology common_ontology : content “content.data”)Action intended for the messageAgents name sharing messageContext-specific information describing the specifics of this messageOntology of both the agentsLanguage of both agents
20 Knowledge Representation of a Tutorial Topic: Array
24 An Example of a Connectionist System Input LayerOutput LayerHidden LayersAvailability of expertiseAvailability of hardware/based technologyContent /length of the courseDegree of assistance required[[[Knowledge level required for the course/ depth of the courseMarket trend towards technology/coursePersonal interestSuccess history if any (last few years result in%)Time taken to complete (revision)Bio-Informaticssuggested decision for Current TrendsWireless Tech.
25 Acknowledgement Thanks to GCET and Charutar Vidya Mandal Reference “Knowledge-Based Systems”Rajendra Akerkar and Priti Srinivas SajjaBook published by Jones and Bartlett Publishers, Massachusetts (MA), USA.