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Knowledge Based Systems -Artificial Intelligence  by Priti Srinivas Sajja S P University
 

Knowledge Based Systems -Artificial Intelligence by Priti Srinivas Sajja S P University

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Priti Srinivas Sajja is an Associate Professor working with Post Graduate Department of Computer Science, Sardar Patel University, India since 1994. She specializes in Artificial Intelligence ...

Priti Srinivas Sajja is an Associate Professor working with Post Graduate Department of Computer Science, Sardar Patel University, India since 1994. She specializes in Artificial Intelligence especially in knowledge-based systems, soft computing and multiagent systems. She is co-author of Knowledge-Based Systems (2009) and Intelligent Technologies for Web Applications (2012).

She has 104 publications in books, book chapters, journals, and in the proceedings of national and international conferences. Three of her publications have won best research paper awards. Visit pritisajja.info for material.

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    Knowledge Based Systems -Artificial Intelligence  by Priti Srinivas Sajja S P University Knowledge Based Systems -Artificial Intelligence by Priti Srinivas Sajja S P University Presentation Transcript

    • Knowledge-Based Systems Priti Srinivas Sajja Associate Professor Department of Computer Science Sardar Patel University Visit priti sajja.info for detail Created By Priti Srinivas Sajja 1
    • Knowledge-Based SystemsContactIntroduction • Name: Dr. Priti Srinivas SajjaData Pyramid • Communication: • Email : priti_sajja@yahoo.comKBS • Mobile : +91 9824926020Objectives and • URL :http://pritisajja.infoCharacteristics • Academic qualifications : Ph. D in Computer ScienceStructure • Thesis title: Knowledge-Based Systems for Socio-Types of • Economic Development (2000)Knowledge • Subject area of specialization : Artificial IntelligenceKnowledgeAcquisition • Publications : 106 in Books, Book Chapters, Journals andKnowledge in Proceedings of International and National ConferencesRepresentationExamples 2 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsThis slideshow is available here Created By Priti Srinivas Sajja 3
    • Knowledge-Based SystemsIntroductionIntroduction Natural Intelligence • Responds to situations flexibly.Data Pyramid • Makes sense of ambiguous or erroneous messages. • Assigns relative importance to elements of a situation. • Finds similarities even though the situations might beKBS different.Objectives and • Draws distinctions between situations even though there may be many similarities between them.CharacteristicsStructure Artificial IntelligenceTypes of • According to Rich & Knight (1991) “AI is the study of how to makeKnowledge computers do things, at which, at the moment, people areKnowledge better”.Acquisition • A machine is regarded as intelligent if it exhibits humanKnowledge characteristics generated through natural intelligence.Representation • AI is the study of human thought processes and moving toward problem solving in a symbolic and non-algorithmic way.Examples 4 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroductionIntroductionData PyramidKBSObjectives andCharacteristicsStructure “Artificial Intelligence(AI) is the study of howTypes of to make computers do things at which,Knowledge at the moment, people are better”KnowledgeAcquisition • Elaine Rich, Artificial Intelligence,Knowledge McGraw Hill Publications, 1986RepresentationExamples 5 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroductionIntroduction human thought process heuristic methodsData Pyramid where people are better non-algorithmicKBS characteristics we knowledge usingObjectives and associate with intelligence symbolsCharacteristics Constituents of artificial intelligenceStructureTypes ofKnowledge Acceptable solution Extreme solution, either best orKnowledge in acceptable time worst taking  (infinite) timeAcquisitionKnowledge timeRepresentation Nature of AI solutionsExamples 6 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroductionIntroduction Turing test will fail to test for intelligence in two circumstances;Data Pyramid 1. A machine may well be intelligent without beingKBS Can you tell me what is able to chat exactly like a 222222*67344? human; and;Objectives and Why 2. The test fails to capture theCharacteristics Sir? general properties ofStructure intelligence, such as the ability to solve difficult problems orTypes of come up with original insights.Knowledge If a machine can solve aKnowledge difficult problem that noAcquisition person could solve, it would,Knowledge in principle, fail the test.Representation The Turing testExamples 7 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroductionIntroduction Creating Your Own Test…Data Pyramid Can you find any test to check the given system is intelligent or not?KBS Reacts Walks,Objectives and differently perceives, If it talksCharacteristics tests, smells, like and feels like human Makes and humanStructure understands jokeTypes ofKnowledge Solves Translates,Knowledge your summarizes, problem and learnsAcquisitionKnowledgeRepresentationExamples 8 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroductionIntroduction Rich & Knight (1991) classified and described the different areas thatData Pyramid Artificial Intelligence techniques have been applied to as follows:KBSObjectives and Mundane Tasks Expert TasksCharacteristics • Perception - vision • Engineering - design, and speech Formal Tasks fault finding, • Natural language • Games - chess, manufacturingStructure backgammon, understanding, planning, etc. generation, and checkers, etc.Types of • Scientific analysis translation • Mathematics-Knowledge geometry, logic, • Medical diagnosis • CommonsenseKnowledge integral calculus, • Financial analysis reasoningAcquisition theorem proving, • Robot control etc.KnowledgeRepresentationExamples 9 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroductionDataPyramidData Pyramid ISKBS Strategy makers apply morals, principles, WBS Wisdom (experience) and experience to generate policiesObjectives andCharacteristics Higher management generates KBS Knowledge (synthesis) knowledge by synthesizing informationStructure Middle management uses reports/info. DSS, MIS generated though analysis and acts Information (analysis) accordinglyTypes ofKnowledge Basic transactions by operational TPS Data (processing of raw staff using data processing observations )KnowledgeAcquisition Volume Sophistication andKnowledge complexityRepresentationExamples 10 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroductionDataPyramidData Pyramid HeuristicsKBS and models WisdomObjectives and NoveltyCharacteristics Rules KnowledgeStructure Information Experience ConceptsTypes ofKnowledge DataKnowledge Raw Data through Understanding fact findingAcquisition Researching Absorbing Doing Interacting ReflectingKnowledgeRepresentationExamples 11 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Intelligent systems:DataPyramidData Pyramid 21st century challenge Software resources ISKBS EESObjectives and 1990 ESCharacteristics ESS Users’ requirements EISStructure DSS 1970 OASTypes of MIS TPSKnowledge 1950Knowledge Hardware base/technologyAcquisitionKnowledgeRepresentationExamples 12 Created By Priti Srinivas Sajja
    • Knowledge-Based Systems Knowledge-Based SystemsIntroductionData PyramidKBSKBS KObjectives andCharacteristicsStructure Knowledge-Based Systems (KBS) are ProductiveTypes ofKnowledge Artificial Intelligence Tools working in aKnowledge narrow domain.AcquisitionKnowledgeRepresentationExamples 13 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Comparison Traditional Computer-Based Information Knowledge-Based Systems (KBS)Data Pyramid Systems (CBIS) Gives a guaranteed solution and Adds powers to the solution and concentrates concentrate on efficiency on effectiveness without any guarantee ofKBSKBS solution Data and/or information processing Knowledge and/or decision processingObjectives and approach approachCharacteristics Assists in activities related to decision Transfer of expertise; takes a decision based making and routine transactions; supports on knowledge, explains it, and upgrades it, ifStructure need for information required Examples are TPS, MIS, DSS, etc. Examples are expert systems, CASE-basedTypes of systems, etc.Knowledge Manipulation method is numeric and Manipulation method is primarily algorithmic symbolic/connectionist and nonalgorithmicKnowledge These systems do not make mistakes These systems learn by mistakesAcquisition Need complete information and/or data Partial and uncertain information, data, orKnowledge knowledge will doRepresentation Works for complex, integrated, and wide Works for narrow domains in a reactive and areas in a reactive manner proactive mannerExamples 14 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Categories of KBSData Pyramid • Expert systemsKBSKBS • Linked systemsObjectives and • Intelligent tutoring systemCharacteristics • CASE based systemStructure • Intelligent user interface for databasesTypes ofKnowledgeKnowledgeAcquisitionKnowledgeRepresentationExamples 15 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction • Provides a high intelligence levelData Pyramid • Assists people in discovering and developing unknown fieldsKBS • Offers a vast amount of knowledge in different areasObjectives and • Aids in managementObjectivesCharacteristics • Solves social problems in better way than the traditional CBISStructure • Acquires new perceptions by simulating unknownTypes of situationsKnowledge • Offers significant software productivity improvementKnowledgeAcquisition • Significantly reduces cost and time to developKnowledge computerized systemsRepresentationExamples 16 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Components of KBSData Pyramid Knowledge base is a repository of domain knowledge and meta Enriches the knowledge. system withKBS self-learning Inference engine is a software program, which infers the capabilitiesObjectives and knowledge available in the knowledge baseCharacteristicsStructureStructure Explanation Knowledge base Inference engine and Self-Types of reasoning User interface learningKnowledge FriendlyKnowledge Provides interface to explanation and users workingAcquisition reasoning in their native facilitates languageKnowledgeRepresentationExamples 17 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Advantages and DifficultiesData Pyramid • Permanent Documentation of Knowledge • Cheaper Solution and Easy Availability ofKBS KnowledgeObjectives and • Dual Advantages of Effectiveness and EfficiencyCharacteristicsCharacteristics • Consistency and ReliabilityStructure • Justification for Better Understanding • Self-Learning and Ease of UpdatesTypes ofKnowledge • Completeness of Knowledge BaseKnowledge • Characteristics of KnowledgeAcquisition • Large Size of Knowledge BaseKnowledge • Acquisition of KnowledgeRepresentation • Slow Learning and Execution • Development model and StandardsExamples 18 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Experience ExpertsData Pyramid Sources of SatelliteKBS Broadcasting (Internet, TV, Printed knowledge and Radio)Objectives and MediaCharacteristics Types of KnowledgeStructure • Tacit knowledgeTypes of • Explicit knowledgeTypes ofKnowledgeKnowledge • Commonsense knowledgeKnowledge • Informed commonsense knowledgeAcquisition • Heuristic knowledgeKnowledge • Domain knowledgeRepresentation • Meta knowledgeExamples 19 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Knowledge Components • FactsData Pyramid – Facts represent sets of raw observation, alphabets, symbols, or statements.KBS • The earth moves around the sun. • Every car has a battery.Objectives and • RulesCharacteristics – Rules encompass conditions and actions, which are also known as antecedents and consequences.Structure • If there is daylight, then the Sun is in the sky. • If the car does not start, then check the battery and fuel.Types ofTypes of • HeuristicsKnowledgeKnowledge – It is a rule of thumb, which is practically applicable however,Knowledge does not offer guarantee of solution.Acquisition • If there is total eclipse of the sun, there is no daylight, even though the sun is in the sky.Knowledge • If it is a rainy season and a car was driven through water,Representation silencer would have water in it, so it may not start.Examples 20 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Inference EngineData Pyramid An inference engine is a software program that refers the existing knowledge, manipulates the knowledge according toKBS need, and makes decisions about actions to be taken.Objectives andCharacteristics MatchStructureStructure Conflict Setting Knowledge WorkingTypes of Base Select MemoryKnowledgeKnowledge ExecuteAcquisitionKnowledge Typical Inference CycleRepresentationExamples 21 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Forward ChainingData Pyramid 1. Consider initial facts and store them into working memory of the knowledge base.KBS 2. Check the antecedent part (left hand side) of the production rules.Objectives and 3. If all the conditions are matched, fire the rule (execute the rightCharacteristics hand side). 4. If there is only one rule do the following:StructureStructure 4.1 Perform necessary actions.Types of 4.2 Modify working memory and update facts.Knowledge 4.3 Check for new conditions.Knowledge 5. If more than one rule is selected use the conflict resolution strategyAcquisition to select the most appropriate rules and go to step 4.Knowledge 6. Continue until appropriate rule is found and executed.RepresentationExamples 22 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Backward ChainingData Pyramid 1. Start with possible hypothesis, say H.KBS 2. Store the hypothesis H in working memory along with the available facts. Also consider a rule indicator R, and set it toObjectives and Null.Characteristics 3. If H is in the initial facts, the hypothesis it is proven. Go toStructureStructure step 7.Types of 4. If H is not in the initial facts, find a rule, say R, that has aKnowledge descendent (action) part mentioning the hypothesis.Knowledge 5. Store R in working memory.AcquisitionKnowledge 6. Check conditions of the R and match with the existing facts.Representation 7. If matched, then fire the rule R and stop. Otherwise, continueExamples to step 4. 23 Created By Priti Srinivas Sajja
    • Knowledge-Based Systems A Short Break …. Created By Priti Srinivas Sajja 24
    • Knowledge-Based Systems IDENTIFICATIONIntroduction Other CONCEPTULIZATION Knowledge Sources IDENTIFICATION Knowledge AcquisitionData Pyramid Experts Techniques Knowledge KBS requirements • Literature review Engineer • Protocol analysis • Diagram-based techniques UserKBS • Concept sorting Knowledge representation Knowledge • etc. discovery and FORMALIZATIONObjectives and verification IMPLEMENTATIONCharacteristics Knowledge Base Data BaseStructure Automatic creation from TESTING Cases and cases documentsTypes ofKnowledgeKnowledgeKnowledge Activities in the knowledge acquisition processAcquisitionAcquisition • Find suitable experts and a knowledge engineerKnowledge • Proper homework and planningRepresentation • Interpreting and understanding the knowledge provided by the experts • Representing the knowledge provided by the expertsExamples 25 Created By Priti Srinivas Sajja
    • Knowledge-Based Systems Knowledge AcquisitionIntroduction • Problem SolvingData Pyramid • Talking and Story TellingKBSObjectives and • Supervisory StyleCharacteristics • Dealing with multiple expertsStructureTypes ofKnowledgeKnowledgeKnowledge Knowledge Group Engineer IndividualAcquisitionAcquisition expert Hierarchical handling handling handlingKnowledgeRepresentationExamples 26 Created By Priti Srinivas Sajja
    • Knowledge-Based SystemsIntroduction Knowledge UpdateData PyramidKBSObjectives andCharacteristics Self-update by Update by expertStructure system Update by knowledge through interface engineerTypes ofKnowledgeKnowledgeKnowledgeAcquisitionAcquisitionKnowledgeRepresentationExamples 27 Created By Priti Srinivas Sajja
    • Knowledge-Based Systems Knowledge RepresentationIntroduction Constant: RAM, LAXMANData Pyramid Variable: Man Function: Elder (RAM, LAXMAN) returns any value, here, RAMKBS Predicate: Mortal (RAM) returns a Boolean value, here, True WFF: ‘If you do not exercise, you will gain weight is represented as:Objectives and  x[{Human(x) ^ ~Exercise (x)}  Gain weight(x)]Characteristics Factual Knowledge RepresentationStructureTypes of Instance Person InstanceKnowledgeKnowledge Doctor Agent Give PatientAcquisition RecipientKnowledgeKnowledge MedicineRepresentationRepresentation FrameExamples 28 Created By Priti Srinivas Sajja
    • Knowledge-Based Systems Knowledge RepresentationIntroduction Name: Visit to Pharmacy Scene 1: Entry P enters to the pharmacy.Data Pyramid Props: Money P goes to reception. P meets R. Symptoms P pays registration and/or fees and gets appointment. Treatment Go to Scene 2. MedicineKBS Roles: Dentist - D Scene 2: Consulting DoctorObjectives and Receptionist - R Patient - P P meets D. P conveys symptoms.Characteristics Entry Conditions: P gets treatment. P gets appointment.Structure Patient P has toothache. Patient P has money. Go to Scene 3.Types of Exit ConditionsKnowledge Patient P has less money. Patient P returns with treatment. Scene 3: Exiting P pays money to R.Knowledge Patient P has appointment. P exits the pharmacy. Patient P has prescription.AcquisitionKnowledgeKnowledgeRepresentationRepresentationExamples 29 Created By Priti Srinivas Sajja
    • Knowledge-Based Systems ExamplesTypology Created By Priti Srinivas Sajja 30
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    • Knowledge-Based Systems ExamplesIntroductionData Pyramid • ELIZA is a computer program and an early example ofKBS primitive natural language processing.Objectives and • ELIZA was written at MIT by Joseph WeizenbaumCharacteristics between 1964 to 1966.Structure • ELIZA was implemented using simple pattern matching techniques, but was taken seriously by several of itsTypes ofKnowledge users, even after Weizenbaum explained to them howKnowledge it worked.Acquisition • It was one of the first chatterbots in existence.KnowledgeRepresentationExamplesExamples 42 Created By Priti Srinivas Sajja
    • Knowledge-Based Systems Examples // Description: this is a very basic example of a chatterbot program by Gonzales Cenelia #include <iostream> #include <string> #include <ctime> int main() { std::string Response[] = {"I HEARD YOU!", "SO, YOU ARE TALKING TO ME.", CONTINUE, I AM LISTENING.", "VERY INTERESTING CONVERSATION.", "TELL ME MORE..." }; srand((unsigned) time(NULL)); std::string sInput = ""; std::string sResponse = ""; while(1) { std::cout << ">"; std::getline(std::cin, sInput); int nSelection = rand() % 5; sResponse = Response[nSelection]; std::cout << sResponse << std::endl; } return 0; } Created By Priti Srinivas Sajja 43
    • Knowledge-Based SystemsIntroductionData PyramidKBSObjectives andCharacteristicsStructureTypes ofKnowledgeKnowledgeAcquisitionKnowledgeRepresentationExamples 44 Created By Priti Srinivas Sajja