This course introduces the basic concepts and techniques of Artificial Intelligence. Artificial intelligence is the sub area of computer science devoted to creating software and hardware to get computers to do things that would be considered intelligent as if people did them. Artificial intelligence has had an active and exciting history and is now a reasonably mature area of computer science.
A. Outline of the course Unit No. Title of the unit 1. Introduction 2. Game playing 3. Natural Language Processing & Learning 4. Expert System, Fuzzy Logic and Genetic Algorithm B. Detailed syllabus Unit Details 1. Introduction • Introduction to Unit • History of AI • Intelligent agents • Structure of agents and its functions • Problem Solving by Searching - Agents, Formulating Problems, Example Problems, Searching for Solutions, • Search Strategies, Avoiding Repeated States, Constraints Satisfaction Search. • Informed Search Methods Best-First Search, Heuristic Functions, Memory Bounded Search, Interactive • Improvement Algorithms • Conclusion of Unit 2. Game playing • Introduction to Unit • Game Playing Introduction Games as Search Problems, Perfect Decision in Two-Person Games, Imperfect • Decisions, Alpha-Beta Pruning, Games that include an element of chance, State-of-the-Art Game Programs. • Knowledge Based Agent, The Wumpus World Environment, Representation, Reasoning and Logic, • Propositional Logic. • Properties of Good and Bad Knowledge Bases, Knowledge Engineering, Electronic Circuits Domain, and • General Ontology. • Inference Rules involving Quantifiers, An example Proof, Generalized Modus Ponens, Forward and • Backward Chaining. • Conclusion of Unit 3. Natural Language Processing & Learning • Introduction to Unit • Natural Language Processing Syntactic Processing, Semantic Analysis, Discourse and Pragmatic • Processing, Statistical Natural Language Processing, Spell Checking. • Learning Rote Learning, Learning in Problem Solving, Explanation-based Learning, Formal Learning • Theory, Neural Net Learning and Genetic Learning. • Machine Learning Perceptron, Checker Playing example, Learning Automata, Genetic Algorithm, • Intelligent Editors. • Conclusion of Unit 4. Expert System, Fuzzy Logic and Genetic Algorithm • Introduction to Unit • Expert System Representing and using domain knowledge, Expert System shells, Explanation, Knowledge • Acquisition. • Perception and action Real time search, perception, action, robot architectures. • Introduction to Fuzzy Logic Systems & Genetic Algorithm • Conclusion of Unit