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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 Introduction and Contact Information • • Name: Dr Priti Srinivas Sajja Communication: • Email : [email protected] • • • • • • Mobile : 9824926020 • URL: http://priti.sajja.info 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 388120 2 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 3 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. 4 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 nonalgorithmic 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. 5 Make Your Own Definition of AI human thought process heuristic methods where people are better non-algorithmic characteristics we associate with intelligence knowledge using symbols Figure 1.1: Constituents of artificial intelligence 6 Nature of AI Solutions Acceptable solution in acceptable time Extreme solution, either best or worst taking (infinite) time time Figure 1.2: Nature of AI solutions 7 Testing Intelligence Turing test will fail to test for intelligence in two circumstances; Can you tell me what is 222222*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 8 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. 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 Expert Tasks • • Engineering - design, fault finding, manufacturing planning, etc. • Scientific analysis • Medical diagnosis • Financial analysis • • • 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. 9 Data Pyramid and Computer Based Systems Heuristics and models Wisdom Novelty Knowledge Rules Information Experience Concepts Data Understanding Raw Data through fact finding Researching Absorbing Doing Interacting Reflecting Figure 1.6: Convergence from data to intelligence 10 Data Pyramid and Computer-Based Systems IS Strategy makers apply morals, principles, and experience to generate policies WBS Higher management generates knowledge by synthesizing information KBS Middle management uses reports/info. generated though analysis and acts accordingly Basic transactions by operational staff using data processing DSS, MIS TPS Volume Wisdom (experience) Knowledge (synthesis) Information (analysis) Data (processing of raw observations ) Sophistication and complexity Figure 1.7: Data pyramid: Managerial perspectives 11 Computer-Based Information Systems Tree Intelligent systems: 21st century challenge Software resources IS EES ES 1990 ESS EIS Users’ requirements EES: Executive expert system, which is a hybridization of an expert system , executive information system, and decision support system DSS MIS OAS TPS Hardware base/technology Figure 1.8: CBIS tree 12 1970 1950 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 Knowledge and/or decision processing approach 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 Manipulation method is primarily symbolic/connectionist and nonalgorithmic and algorithmic These systems do These systems learn by mistakes not make mistakes Need complete data information and/or Works for complex, integrated, and wide areas in a reactive 13 manner Partial and uncertain information, data, or knowledge will do Works for narrow domains in a reactive and proactive manner 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 14 Components of KBS Knowledge base is a repository of domain knowledge and meta knowledge. Enriches the system with self-learning capabilities Inference engine is a software program, which infers the knowledge available in the knowledge base Explanation and reasoning Provides explanation and reasoning facilitates Knowledge base Inference engine Selflearning User interface Figure 1.10: General structure of KBS 15 Friendly interface to users working in their native language Categories of KBS According to the classifications by Tuthhill & Levy (1991), five main types of KBS exists: Expert systems Linked systems Intelligent tutoring systems CASE-based systems Database in conjunction with an intelligent user interface 16 Difficulties with the KBS • Completeness of Knowledge Base • Characteristics of Knowledge • Large Size of Knowledge Base • Acquisition of Knowledge • Slow Learning and Execution 17 An example of a Multi-agent KBS on Grid Knowledge Utilization Knowledge Management Knowledge Discovery and Grid FTP ReplicaLocation Services Learning Mgt. Resources Drills and Quizzes Knowledge Mgt. Question/Answer Meta knowledge Conceptual system Tutorial Path Content knowledge Explanation Learner’s ontology Information Discovery Services Documentation Security Services Documents Local DataBases Semantic Search Mail E-mail & Chat Resource Management Distributed databases 18 Middleware Services and Protocols User Interface Agent Internet Grid Middleware Services Resource Management (Grid Resource Allocation Protocol-GRAM) Agents Users Experts Communication Between Agents • Agents developed here are communicating with a tool named KQML. • Knowledge based Query Management Language. (register Action intended for the message : sender agent_Lerning_Mgt Agents name sharing message : receiver agent_Tutorail-Path : reply-with message : language common_language : ontology common_ontology : content ) 19 “content.data” Action intended for the message Language of both agents Ontology of both the agents Context-specific information describing the specifics of this message Knowledge Representation of a Tutorial Topic: Array 20 Prototype Screen Designs for the KBS 21 Prototype Screen Designs for the KBS 22 Result from the System 23 An Example of a Connectionist System Availability of expertise BioInformatics Availability of hardware/based technology Content /length of the course Degree of assistance required suggeste d decision for Current Trends [[[ Knowledge level required for the course/ depth of the course Market trend towards technology/course Personal interest Hidden Layers Success history if any (last few years result in%) Time taken to complete (revision) Input Layer 24 Output Layer Wireless Tech. 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. 25