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Knowledge-Based Systems Priti Srinivas Sajja Associate Professor Department of Computer Science Sardar Patel University Visit priti sajja.info for detail.

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1 Knowledge-Based Systems Priti Srinivas Sajja Associate Professor Department of Computer Science Sardar Patel University Visit priti sajja.info for detail 1Created By Priti Srinivas Sajja

2 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems Contact 2 Created By Priti Srinivas Sajja Name: Dr. Priti Srinivas Sajja Communication: Mobile : URL : Academic qualifications : Ph. D in Computer Science Thesis title: Knowledge-Based Systems for Socio- Economic Development Subject area of specialization : Artificial Intelligence Publications : 84 in Books, Book Chapters, Journals and in Proceedings of International and National Conferences

3 Knowledge-Based Systems Created By Priti Srinivas Sajja3 This slideshow is available here

4 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems Introduction 4 Created By Priti Srinivas Sajja Natural Intelligence Responds 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. 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 toward problem solving in a symbolic and non-algorithmic way.

5 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 5 Created By Priti Srinivas Sajja “Artificial Intelligence(AI) is the study of how to make computers do things at which, at the moment, people are better ” Elaine Rich, Artificial Intelligence, McGraw Hill Publications, 1986 Introduction

6 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 6 Created By Priti Srinivas Sajja where people are better human thought process characteristics we associate with intelligence knowledge using symbols heuristic methods non-algorithmic Constituents of artificial intelligence Extreme solution, either best or worst taking  (infinite) time time Acceptable solution in acceptable time Nature of AI solutions Introduction

7 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 7 Created By Priti Srinivas Sajja Turing test will fail to test for intelligence in two circumstances; 1.A machine may well be intelligent without being able to chat exactly like a human; and; 2.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. The Turing test Introduction

8 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 8 Created By Priti Srinivas Sajja Creating Your Own Test… Can you find any test to check the given system is intelligent or not? If it talks like human Translates, summarizes, and learns Solves your problem Reacts differently Walks, perceives, tests, smells, and feels like human Makes and understands joke Introduction

9 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 9 Created By Priti Srinivas Sajja Rich & Knight (1991) classified and described the different areas that Artificial Intelligence techniques have been applied to as follows: Mundane Tasks Perception - vision and speech Natural language understanding, generation, and translation Commonsense reasoning Robot control Mundane Tasks Perception - vision and speech Natural language understanding, generation, and translation Commonsense reasoning Robot control Formal Tasks Games - chess, backgammon, checkers, etc. Mathematics- geometry, logic, integral calculus, theorem proving, etc. Formal Tasks Games - chess, backgammon, checkers, etc. Mathematics- geometry, logic, integral calculus, theorem proving, etc. Expert Tasks Engineering - design, fault finding, manufacturing planning, etc. Scientific analysis Medical diagnosis Financial analysis Expert Tasks Engineering - design, fault finding, manufacturing planning, etc. Scientific analysis Medical diagnosis Financial analysis Introduction

10 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 10 Created By Priti Srinivas Sajja Basic transactions by operational staff using data processing Middle management uses reports/info. generated though analysis and acts accordingly Higher management generates knowledge by synthesizing information Strategy makers apply morals, principles, and experience to generate policies Wisdom (experience) Knowledge (synthesis) Information (analysis) Data (processing of raw observations ) VolumeSophistication and complexity TPS DSS, MIS KBS WBS IS Data Pyramid

11 KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 11 Created By Priti Srinivas Sajja Data Information Knowledge Wisdom Understanding Experience Novelty Researching Absorbing Doing Interacting Reflecting Raw Data through fact finding Concepts Rules Heuristics and models Data Pyramid

12 KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 12 Created By Priti Srinivas Sajja MIS DSS EES ESS ES EIS TPS OAS Hardware base/technology Users’ requirements IS I ntelligent systems: 21 st century challenge EES: Executive Expert System is a hybridization of an expert system, executive information system, and decision support system. EES: Executive Expert System is a hybridization of an expert system, executive information system, and decision support system. Software resources Data Pyramid

13 KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 13 Created By Priti Srinivas Sajja Knowledge-Based Systems KBS K K Knowledge-Based Systems (KBS) are Productive Artificial Intelligence Tools working in a narrow domain.

14 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 14 Created By Priti Srinivas Sajja Comparison Traditional Computer-Based Information Systems (CBIS) Knowledge-Based Systems (KBS) Gives a guaranteed solution and concentrate on efficiency Adds powers to the solution and concentrates on effectiveness without any guarantee of solution Data and/or information processing approach Knowledge and/or decision processing approach Assists in activities related to decision making and routine transactions; supports need for information Transfer of expertise; takes a decision based on knowledge, explains it, and upgrades it, if required Examples are TPS, MIS, DSS, etc.Examples are expert systems, CASE-based systems, etc. Manipulation method is numeric and algorithmic Manipulation method is primarily symbolic/connectionist and nonalgorithmic These systems do not make mistakesThese systems learn by mistakes Need complete information and/or dataPartial and uncertain information, data, or knowledge will do Works for complex, integrated, and wide areas in a reactive manner Works for narrow domains in a reactive and proactive manner KBS

15 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 15 Created By Priti Srinivas Sajja Categories of KBS Expert systems Linked systems Intelligent tutoring system CASE based system Intelligent user interface for databases KBS

16 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 16 Created By Priti Srinivas Sajja Objectives Provides a high intelligence level Assists people in discovering and developing unknown fields Offers a vast amount of knowledge in different areas Aids in management Solves social problems in better way than the traditional CBIS Acquires new perceptions by simulating unknown situations Offers significant software productivity improvement Significantly reduces cost and time to develop computerized systems

17 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 17 Created By Priti Srinivas Sajja Components of KBS Structure Knowledge base Inference engine User interface Explanation and reasoning Self- learning Enriches the system with self-learning capabilities Provides explanation and reasoning facilitates 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 base 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 base Friendly interface to users working in their native language

18 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 18 Created By Priti Srinivas Sajja Advantages and Difficulties Completeness of Knowledge Base Characteristics of Knowledge Large Size of Knowledge Base Acquisition of Knowledge Slow Learning and Execution Development model and Standards Permanent Documentation of Knowledge Cheaper Solution and Easy Availability of Knowledge Dual Advantages of Effectiveness and Efficiency Consistency and Reliability Justification for Better Understanding Self-Learning and Ease of Updates Characteristics

19 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 19 Created By Priti Srinivas Sajja Types of Knowledge Experience Satellite Broadcasting (Internet, TV, and Radio) Printed Media Experts Sources of knowledge Types of Knowledge Tacit knowledge Explicit knowledge Commonsense knowledge Informed commonsense knowledge Heuristic knowledge Domain knowledge Meta knowledge Types of Knowledge Tacit knowledge Explicit knowledge Commonsense knowledge Informed commonsense knowledge Heuristic knowledge Domain knowledge Meta knowledge

20 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 20 Created By Priti Srinivas Sajja Knowledge Components Facts – Facts represent sets of raw observation, alphabets, symbols, or statements. The earth moves around the sun. Every car has a battery. Rules – Rules encompass conditions and actions, which are also known as antecedents and consequences. If there is daylight, then the Sun is in the sky. If the car does not start, then check the battery and fuel. Heuristics – It is a rule of thumb, which is practically applicable however, does not offer guarantee of solution. If there is total eclipse of the sun, there is no daylight, even though the sun is in the sky. If it is a rainy season and a car was driven through water, silencer would have water in it, so it may not start. Types of Knowledge

21 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 21 Created By Priti Srinivas Sajja Inference Engine Match Select Execute Conflict Setting Knowledge Base Working Memory Typical Inference Cycle An inference engine is a software program that refers the existing knowledge, manipulates the knowledge according to need, and makes decisions about actions to be taken. Structure

22 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 22 Created By Priti Srinivas Sajja Forward Chaining 1.Consider initial facts and store them into working memory of the knowledge base. 2.Check the antecedent part (left hand side) of the production rules. 3.If all the conditions are matched, fire the rule (execute the right hand side). 4.If there is only one rule do the following: 4.1 Perform necessary actions. 4.2 Modify working memory and update facts. 4.3 Check for new conditions. 5.If more than one rule is selected use the conflict resolution strategy to select the most appropriate rules and go to step 4. 6.Continue until appropriate rule is found and executed. Structure

23 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 23 Created By Priti Srinivas Sajja Backward Chaining 1.Start with possible hypothesis, say H. 2.Store the hypothesis H in working memory along with the available facts. Also consider a rule indicator R, and set it to Null. 3.If H is in the initial facts, the hypothesis it is proven. Go to step 7. 4.If H is not in the initial facts, find a rule, say R, that has a descendent (action) part mentioning the hypothesis. 5.Store R in working memory. 6.Check conditions of the R and match with the existing facts. 7.If matched, then fire the rule R and stop. Otherwise, continue to step 4. Structure

24 Knowledge-Based Systems Created By Priti Srinivas Sajja24

25 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 25 Created By Priti Srinivas Sajja Knowledge Acquisition Knowledge Acquisition KBS requirements Knowledge Engineer Data Base Cases and documents Knowledge Base Knowledge Acquisition Techniques Literature review Protocol analysis Diagram-based techniques Concept sorting etc. Experts Other Knowledge Sources User Automatic creation from cases Knowledge discovery and verification Activities in the knowledge acquisition process IDENTIFICATION CONCEPTULIZATION FORMALIZATION IMPLEMENTATION TESTING Knowledge representation Find suitable experts and a knowledge engineer Proper homework and planning Interpreting and understanding the knowledge provided by the experts Representing the knowledge provided by the experts

26 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 26 Created By Priti Srinivas Sajja Knowledge Acquisition Problem Solving Talking and Story Telling Supervisory Style Dealing with multiple experts Knowledge Engineer Hierarchical handling Group handling Individual expert handling Knowledge Acquisition Knowledge Acquisition

27 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 27 Created By Priti Srinivas Sajja Knowledge Update Update by knowledge engineer Self-update by system Update by expert through interface Knowledge Acquisition Knowledge Acquisition

28 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 28 Created By Priti Srinivas Sajja Knowledge Representation Constant:RAM, LAXMAN Variable:Man Function:Elder (RAM, LAXMAN) returns any value, here, RAM Predicate: Mortal (RAM) returns a Boolean value, here, True WFF:‘If you do not exercise, you will gain weight is represented as:  x[{Human(x) ^ ~Exercise (x)}  Gain weight(x)] Factual Knowledge Representation Person Doctor Patient Medicine Give Instance Agent Recipient Semantic Network Name: Power Bike Broad Category:Land Vehicle Sub Category:Gearless Fuel Type:Gas Cost:$ 350 Capacity:Two persons Speed: 160 Km/Hour Name: Power Bike Broad Category:Land Vehicle Sub Category:Gearless Fuel Type:Gas Cost:$ 350 Capacity:Two persons Speed: 160 Km/Hour Frame Knowledge Representation Knowledge Representation

29 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 29 Created By Priti Srinivas Sajja Knowledge Representation Script Name: Visit to Pharmacy Props:Money Symptoms Treatment Medicine Roles:Dentist - D Receptionist - R Patient - P Name: Visit to Pharmacy Props:Money Symptoms Treatment Medicine Roles:Dentist - D Receptionist - R Patient - P Entry Conditions: Patient P has toothache. Patient P has money. Exit Conditions Patient P has less money. Patient P returns with treatment. Patient P has appointment. Patient P has prescription. Entry Conditions: Patient P has toothache. Patient P has money. Exit Conditions Patient P has less money. Patient P returns with treatment. Patient P has appointment. Patient P has prescription. Scene 1: Entry P enters to the pharmacy. P goes to reception. P meets R. P pays registration and/or fees and gets appointment. Go to Scene 2. Scene 1: Entry P enters to the pharmacy. P goes to reception. P meets R. P pays registration and/or fees and gets appointment. Go to Scene 2. Scene 2: Consulting Doctor P meets D. P conveys symptoms. P gets treatment.P gets appointment. Go to Scene 3. Scene 2: Consulting Doctor P meets D. P conveys symptoms. P gets treatment.P gets appointment. Go to Scene 3. Scene 3: Exiting P pays money to R. P exits the pharmacy. Scene 3: Exiting P pays money to R. P exits the pharmacy. Knowledge Representation Knowledge Representation

30 Knowledge-Based Systems Created By Priti Srinivas Sajja30 Examples Typology

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42 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 42 Created By Priti Srinivas Sajja Examples ELIZA is a computer program and an early example of primitive natural language processing. ELIZA was written at MIT by Joseph Weizenbaum between 1964 to ELIZA was implemented using simple pattern matching techniques, but was taken seriously by several of its users, even after Weizenbaum explained to them how it worked. It was one of the first chatterbots in existence.chatterbots

43 Knowledge-Based Systems Created By Priti Srinivas Sajja43 Examples // Description: this is a very basic example of a chatterbot program by Gonzales Cenelia #include 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; }

44 Data Pyramid KBS Objectives and Characteristics Objectives and Characteristics Structure Types of Knowledge Types of Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Examples Introduction Knowledge-Based Systems 44 Created By Priti Srinivas Sajja


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