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1 Dr. Syed Noman Hasany Review of known methodologies Analysis of software requirements Real-time software Software cost, quality, testing and measurements Object programming Knowledge engineering issues: knowledge representation using rules, frames & logic, basics of logical inference, and basics of search. 2 Knowledge representation using rules Frames & logic, Basics of logical inference, and Basics of search. 4 What o Knowledge is the sort of information that people use to solve problems. Knowledge includes: o is knowledge? facts, concepts, procedures, models, heuristics, examples. Knowledge may be: o o o specific or general exact or fuzzy procedural or declarative 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 Volume DSS, MIS TPS Wisdom (experience) Knowledge (synthesis) Information (analysis) Data (processing of raw observations ) Sophistication and complexity Data pyramid: Managerial perspectives 6 What o A is a knowledge-based system? A system which is built around a knowledge base. i.e. a collection of knowledge, taken from a human, and stored in such a way that the system can reason with it. branch of Artificial Intelligence What is an expert system? o o A particular kind of knowledge-based system One in which the knowledge, stored in the knowledge base, has been taken from an expert in some particular field. Therefore, an expert system can, to a certain extent, act as a substitute for the expert from whom the knowledge was taken. Conventional Programming Algorithms + Data Structures = Programs Knowledge-Based Systems Knowledge + Inference = Expert System General problem in Computer Science Solutions = Data Structures o words, arrays o records o lists, queues o objects More specific problem in AI Solutions = knowledge structures o decision trees o logic and predicate calculus o rules: production systems o description logics, semantic nets, frames o scripts o ontologies CSC 9010 Spring 2011. Paula Matuszek Slides taken in part from Eric Eaton, http://www.csc.villanova.edu/~matuszek/fall2008/KnowledgeRepresentation.ppt 10 The term “knowledge engineering” is often used to mean the process of o o o designing building installing a knowledge-based system. Some authors use the term to mean just the knowledge acquisition phase. Knowledge Representation means: o Capturing human knowledge o In a form computer can reason about Why? o Model human cognition o Add power to search-based methods Actually a component of all software development CSC 9010 Spring 2011. Paula Matuszek Slides taken in part from Eric Eaton, http://www.csc.villanova.edu/~matuszek/fall2008/KnowledgeRepresentation.ppt 12 Knowledge base is a repository of domain knowledge and metaknowledge. Enriches the system with self-learning capabilities Inference engine is a software program that infers the knowledge available in the knowledge base. Explanation and reasoning Provides explanation and reasoning facilities Knowledge base Inference engine Selflearning User interface Figure 1.10: General structure of KBS 13 Friendly interface to users working in their native language Knowledge Engineering (KE) concerns the basic issues involved in building and using KBS, i.e. Acquisition Representation Explanation Validation of knowledge in a KBS 14 Knowledge validation (test cases) Sources of knowledge (experts, others) Knowledge Acquisition Knowledge base Encoding Knowledge Representation Explanation justification Inferencing Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 15 Acquisition of knowledge o General knowledge or metaknowledge o From experts, books, documents, sensors, files Knowledge representation o Organized knowledge Knowledge validation and verification Inferences o Software designed to pass statistical sample data to generalizations Explanation and justification capabilities 11-16 Logic based representation – first order predicate logic, Prolog Procedural representation – rules, production system Network representation – semantic networks, conceptual graphs Structural representation – scripts, frames, objects