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A Lecture for GCET Students September 11, 2009 Dr. Priti Srinivas Sajja Department of Computer Science Sardar Patel University Vallabh Vidyanagar.

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Presentation on theme: "A Lecture for GCET Students September 11, 2009 Dr. Priti Srinivas Sajja Department of Computer Science Sardar Patel University Vallabh Vidyanagar."— Presentation transcript:

1 A Lecture for GCET Students September 11, 2009 Dr. Priti Srinivas Sajja Department of Computer Science Sardar Patel University Vallabh Vidyanagar

2 2 Introduction and Contact Information Name: Dr Priti Srinivas Sajja Communication: Mobile : URL: Academic Qualifications: Ph. D in Computer Science Thesis Title: Knowledge-Based Systems for Socio-Economic Rural Development Subject area of specialization : Knowledge-Based Systems Publications : 84 in International/ National Journals and Conferences (Including two books and one chapter) Academic Position : Associate Professor at Department of Computer Science Sardar Patel University Vallabh Vidyanagar

3 3 Outlines of the Lecture Part 1: Artificial Intelligence  Natural intelligence and Artificial Intelligence  Nature of AI Solutions  Testing Intelligence  Categories of Application Areas Part 2: Symbolic Knowledge-Based Systems  Data Pyramid and CBIS  DBMS and KBS  Structure of KBS  Types of KBS  Example KBS Part 3: Connectionist Systems  Symbolic and Connectionist Systems  Example ANN for Course Selection

4 4 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.

5 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 6 where people are better human thought process characteristics we associate with intelligence knowledge using symbols heuristic methods non-algorithmic Figure 1.1: Constituents of artificial intelligence Make Your Own Definition of AI

7 7 Extreme solution, either best or worst taking  (infinite) time time Acceptable solution in acceptable time Figure 1.2: Nature of AI solutions Nature of AI Solutions

8 8 Testing Intelligence 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. Figure 1.4: The Turing test

9 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 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. Expert Tasks Engineering - design, fault finding, manufacturing planning, etc. Scientific analysis Medical diagnosis Financial analysis

10 10 Data Pyramid and Computer Based Systems Data Information Knowledge Wisdom Understanding Experience Novelty Researching Absorbing Doing Interacting Reflecting Raw Data through fact finding Concepts Rules Heuristics and models Figure 1.6: Convergence from data to intelligence

11 11 Data Pyramid and Computer-Based Systems 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 Figure 1.7: Data pyramid: Managerial perspectives

12 12 Computer-Based Information Systems Tree MIS DSS EES ESS ES EIS TPS OAS Figure 1.8: CBIS tree Hardware base/technology Users’ requirements IS Intelligent systems: 21 st century challenge EES: Executive expert system, which is a hybridization of an expert system, executive information system, and decision support system Software resources

13 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 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 mistakes These systems learn by mistakes Need complete information and/or data Partial 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

14 14 Objectives of KBS KBS is an example of fifth-generation computer technology. Some of its objectives are as follows: 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

15 15 Components of KBS Knowledge base Inference engine User interface Explanation and reasoning Self- learning Figure 1.10: General structure of KBS 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 Friendly interface to users working in their native language

16 16 Categories of KBS According to the classifications by Tuthhill & Levy (1991), five main types of KBS exists: Expert systems Linked systems CASE-based systems Database in conjunction with an intelligent user interface Intelligent tutoring systems

17 17 Difficulties with the KBS Completeness of Knowledge Base Characteristics of Knowledge Large Size of Knowledge Base Acquisition of Knowledge Slow Learning and Execution

18 18 An example of a Multi-agent KBS on Grid Users Experts User Interface Agent Agents Learning Mgt. Drills and Quizzes Explanation Semantic Search & Chat Resource Management Question/Answer Tutorial Path Documentation Internet Grid Middleware Services Resource Management (Grid Resource Allocation Protocol-GRAM) and Grid FTP Replica- Location Services Information Discovery Services Security Services Distributed databases Middleware Services and Protocols Local Data- Bases Resources Knowledge Mgt. Meta knowledge Conceptual system Content knowledge Learner’s ontology Mail Documents Knowledge Discovery Knowledge Utilization Knowledge Management

19 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 message Agents name sharing message Action intended for the message Context-specific information describing the specifics of this message Ontology of both the agents Language of both agents

20 20 Knowledge Representation of a Tutorial Topic: Array

21 21 Prototype Screen Designs for the KBS

22 22 Prototype Screen Designs for the KBS

23 23 Result from the System

24 24 An Example of a Connectionist System Input Layer Output Layer Hidden Layers Availability of expertise Availability of hardware/based technology Content /length of the course Degree of assistance required [[[ Knowledge level required for the course/ depth of the course Market trend towards technology/course Personal interest Success history if any (last few years result in%) Time taken to complete (revision ) Bio- Informatics suggeste d decision for Current Trends Wireless Tech.

25 25 Acknowledgement Thanks to GCET and Charutar Vidya Mandal Reference “Knowledge-Based Systems” Rajendra Akerkar and Priti Srinivas Sajja Book published by Jones and Bartlett Publishers, Massachusetts (MA), USA.


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