PPT-WEB BASED EXPERT SYSTEMS TO REPRESENT THE PROFESSOR'S EXPER

Authors V Jacobo A Ortiz R Schouwenaars E Lopez Databases Video Bibliography Autoevaluation and auto scheduling Ordinary online course WEB BASED EXPERT SYSTEMS TO REPRESENT THE PROFESSORS EXPERIENCE IN ENGINEERING COURSES ONLINE. Even though we are familiar with several pr oblemsolving techniques in the real world sometimes many problems cannot be solved by a technique we are familiar with Surprisingly for some compli cated problems no straight forward solution technique is The framework is based on the integration of two different knowledge representation formats The inte gration consists of the Rulebase and the Casebased format s using the Blackboard This scheme uses both procedural and dec larative knowledge represe By James Jennings. Introduction. . What is an Expert system?. expert system. is a computer system that emulates the decision-making ability of a human . expert. . Four . interactive roles form the activities of the expert system: . Eddie Lai. History. 1950s – scientists tried to use computers for “probabilistic reasoning and statistical pattern recognition”. 1970s – realized that physicians do not make decisions based on probability or patterns. SESSION 1. Introduction to Knowledge-based Intelligent Systems. By: . H.Nematzadeh. What is intelligence?. Intelligence is the ability to think and understand instead of doing things by instinct or automatically. (Essential English Dictionary, Collins, London, 1990). Session 2. Rule Based Expert System. By: . H.Nematzadeh. What is knowledge?. 1- If you know the solution of a question you have the knowledge = “know-how”. 2- Knowledge is a theoretical or practical understanding of a subject or a domain. Knowledge is also the sum of what is currently known.. 1. Intelligent Systems and Soft Computing. Lecture 2. Introduction, or what is knowledge?. Rules as a knowledge representation technique. The main players in the development team. Structure of a rule-based expert system. By: . H.Nematzadeh. ASSESSMENT. Midterm Exam = 8/20. Final Exam = 10/20. Research Paper = 2/20. - 20 minutes presentation of the selected . paper . (WORLDCOMP’10:. USA. , IEEE’11: . Can incorporate Neural, Genetic and Fuzzy Components. Expert Systems can perform many functions. Rules can be fuzzy, quantum, modal, neural, Bayesian, etc.. Special inference methods may be used. Concepts of Knowledge Representation: . . IE 469 Manufacturing Systems. 4. 69. . صنع . نظم . التصنيع. Outline. Introduction to Expert Systems (ES). Components of an ES. Building an ES. Implementations/References. 2. Introduction. and. Expert Systems. ARTIFICIAL INTELLIGENCE (AI). is the science of. R. L . . Being able . to. Ability to. solve a . problem. Comparing a DSS to Artificial Intelligence. Decision Support System (DSS). Leroy Garcia. 1. Definition of AI. Artificial Intelligence is . the branch of computer science that is concerned with the automation of intelligent behavior. (Luger, 2008). . 2. Different Approaches to AI. Can incorporate Neural, Genetic and Fuzzy Components. Many expert systems are based on rules. Expert Systems can perform many functions. Rules can be fuzzy, quantum, modal, neural, Bayesian, etc.. Special inference methods may be used. III YEAR VI. SEM. Presented by,. . Ms. M. . Rupa. , . AP/CSE,JCTCET. Motivation & Objectives. Utilization of computers to deal with knowledge. Quantity of knowledge increases rapidly. Knowledge might get lost if not captures.