1.  Dr. Syed Noman Hasany 1

2.  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

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4.  Knowledge representation using rules  Frames & logic,  Basics of logical inference, and  Basics of search. 4

5.  What is knowledge? o Knowledge is the sort of information that people use to solve problems.  Knowledge includes: o facts, concepts, procedures, models, heuristics, examples.  Knowledge may be: o specific or general o exact or fuzzy o procedural or declarative

6. 6 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: Managerial perspectives

7.  What is a knowledge-based system? o 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.  A branch of Artificial Intelligence

8.  What is an expert system? o A particular kind of knowledge-based system o 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.

9. Conventional Programming Knowledge-Based Systems Algorithms + Data Structures = Programs Knowledge + Inference = Expert System

10. 10 CSC 9010 Spring 2011. Paula MatuszekSlides taken in part from Eric Eaton, http://www.csc.villanova.edu/~matuszek/fall2008/KnowledgeRepresentation.ppthttp://www.csc.villanova.edu/~matuszek/fall2008/KnowledgeRepresentation.ppt  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

11.  The term “knowledge engineering” is often used to mean the process of o designing o building o installing a knowledge-based system.  Some authors use the term to mean just the knowledge acquisition phase.

12. 12 CSC 9010 Spring 2011. Paula MatuszekSlides taken in part from Eric Eaton, http://www.csc.villanova.edu/~matuszek/fall2008/KnowledgeRepresentation.ppthttp://www.csc.villanova.edu/~matuszek/fall2008/KnowledgeRepresentation.ppt  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

13. 13 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 facilities Knowledge base is a repository of domain knowledge and metaknowledge. Inference engine is a software program that infers the knowledge available in the knowledge base.

14.  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

15. 15 Knowledge validation (test cases) Knowledge Representation Knowledge Acquisition Encoding Inferencing Sources of knowledge (experts, others) Explanation justification Knowledge base Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

16. 11-16  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

17.  Logic based representation – first order predicate logic, Prolog  Procedural representation – rules, production system  Network representation – semantic networks, conceptual graphs  Structural representation – scripts, frames, objects