Mgu University S8 Electrical & electronics engineering Syllabus
Module 1
Introduction to AI and problem solving concepts: Definition- pattern recognition-production systems – problem and production system characteristics – two-pail problem-analysis of AI techniques – criteria for success
Introduction to AI and problem solving concepts: Definition- pattern recognition-production systems – problem and production system characteristics – two-pail problem-analysis of AI techniques – criteria for success
Module 2
Knowledge representation – formal and non-formal logic: Representation evaluation criteria -level of representation -formal logic schemes -resolutions -predicate and prepositional logic -conversion to clause form -semantic networks-frames-scripts-production system
Knowledge representation – formal and non-formal logic: Representation evaluation criteria -level of representation -formal logic schemes -resolutions -predicate and prepositional logic -conversion to clause form -semantic networks-frames-scripts-production system
Module 3
Problem solving strategies dealing with uncertainty: Defining the problem – control strategies – exhaustive search – generate and test-space transformation models- forward versus backward reasoning -matching – weak methods – hill climbing -breadth and depth first searches – search algorithms.
Problem solving strategies dealing with uncertainty: Defining the problem – control strategies – exhaustive search – generate and test-space transformation models- forward versus backward reasoning -matching – weak methods – hill climbing -breadth and depth first searches – search algorithms.
Module 4
Expert system development process and knowledge acquisition: Definition – analysis of expert system problem solving – role and analysis of knowledge – architecture of the expert system – problem selection – formalization -implementation –evaluation.
Expert system development process and knowledge acquisition: Definition – analysis of expert system problem solving – role and analysis of knowledge – architecture of the expert system – problem selection – formalization -implementation –evaluation.
Module 5
Knowledge acquisition techniques- cognitive behavior – knowledge representation development.
Expert system tools: Expert system shells -narrow tools -large hybrid expert system tools -PC based expert system tools knowledge acquisition tools.
Knowledge acquisition techniques- cognitive behavior – knowledge representation development.
Expert system tools: Expert system shells -narrow tools -large hybrid expert system tools -PC based expert system tools knowledge acquisition tools.
References
Introduction to AI & Expert System – D. W. Patterson, Prentice hall of India
Principles of Artificial Intelligence& Expert Systems Development – David W.Rolston, Tata McGraw Hill
Artificial Intelligence – Elaine Rich, McGraw Hill
Principles of Artificial Intelligence – Nils J. Nilsson, Springer Verlag
Introduction to Artificial Intelligence – Charnaik & McDermott, Addison Wesley
Principles of Artificial Intelligence& Expert Systems Development – David W.Rolston, Tata McGraw Hill
Artificial Intelligence – Elaine Rich, McGraw Hill
Principles of Artificial Intelligence – Nils J. Nilsson, Springer Verlag
Introduction to Artificial Intelligence – Charnaik & McDermott, Addison Wesley