KNOWLEDGE BASED SYSTEMS
Lecture hours: Tuesdays 12.00-2.00pm
Practical hours: To be announced in class

COURSE OUTLINE
1. Introduction: 
Definition of KBS, Categories of KBS,  Applications of KBS, Nature of KBS, Difficulties with KBS, Advantages and limitations of KBS, Types of KBS, Definition of Expert systems, Neural networks (NNS),  Case-based reasoning (CBR), Genetic algorithms, Intelligent agents and Data mining.
    Download lecture slides
   Watch example of  a Knowledge Based System - Akinator on Youtube

2. KBS Architecture: 
Types of Knowledge, Components of Knowledge, Inference Engines

3. Introduction to Expert Systems: 
Knowledge engineering,  Knowledge acquisition, Knowledge representation and reasoning

4. Developing KBS: KBS development model,  Knowledge acquisition, Sharing knowledge, Updating knowledge, Introduction to Knowledge representation, Factual knowledge

5. Special topics in KBS:  Knowledge Management,  Agent-Based Systems, Fuzzy Logic, Genetic Algorithms, Natural Language Processing

6. Case  studies: Diagnostic Expert Systems,  Intelligent Tutoring Systems, Intelligent Online Recommendation Systems

COURSE TEXT: Knowledge-Based Systems, By Rajendra Akerkar and Priti Sajja. Publisher: Jones & Bartlett Learning

                            Wikipedia resources:   http://en.wikipedia.org/wiki/Knowledge-based_systems
                                                                    http://en.wikipedia.org/wiki/Knowledge_base
                                                                    http://en.wikipedia.org/wiki/Knowledge_engineering
                                                                    http://en.wikipedia.org/wiki/Knowledge-based_engineering