Department Events
ARTIFICIAL INTELLIGENCE
The main aim of this module is to make the computer work as efficiently as humans do
Introduction to Artificial Intelligence: Definition, Components of AI Systems, Intelligent Agents, Agents and Environments, Good Behavior, The Nature of Environments, Structure of Agents, Problem Solving, Problem Solving Agents, Example Problems, Searching for Solutions, Uniformed Search Strategies, Avoiding Repeated States, Searching with Partial Information.
Searching Techniques: Informed Search and Exploration, Informed Search Strategies, Heuristic function, Local Search Algorithms and Optimistic Problems, Constraint Satisfaction Problems (CSP).
Knowledge Representation: Issues, First Order Logic, Syntax and Semantics for First Order Logic, Prepositional Versus First Order Logic, Unification and Lifting, Forward Chaining, Backward Chaining, Resolution, Knowledge Representation using rules.
Learning: Learning from Observations, Forms of Learning, Rote learning, Learning by taking advice, Learning in problem solving, Induction, Explanation based learning, Discovery, Analogy, Formal Learning theory, Introduction to Neural Net and Genetic learning.
Applications: Introduction to Expert systems, Representing and using domain knowledge, Expert system shells, Explanation, Knowledge Acquisition, Perception and Actions.
- Stuart Russell, Peter Norvig, “Artificial Intelligence, A Modern Approach”, 2nd Edition, Pearson Education / Prentice Hall of India, 2004.
- Elaine Rich and Kevin Knight, “Artificial Intelligence”, 2nd Edition, Tata McGraw Hill, 2003.
- Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd., 2000.
- George F. Luger, “Artificial Intelligence, Structures and Strategies for Complex Problem Solving”, Pearson Education / PHI, 2002
