2CSE60E14: Artificial Intelligence

Learning Outcomes: 
Upon Completion of the course, the students will be able to
apply artificial intelligence techniques, including search heuristics, knowledge representation, planning and reasoning
describe the key components of the artificial intelligence (AI) field
explain search strategies
solve problems by applying a suitable search method
analyse and apply knowledge representation
describe and list the key aspects of planning in artificial intelligence
analyse and apply probability theorem and Bayesian networks
describe the key aspects of intelligent agents
differentiate the key aspects of evolutionary computation, including genetic algorithms and genetic programming
describe the key aspects of machine learning
analyse problem specifications and derive appropriate solution techniques for them
design and implement appropriate solutions for search problems and for planning problems
Syllabus: 
Unit NoTopics
Introduction

What is intelligence? Foundations of artificial intelligence (AI). History of  AI; Problem Solving- Formulating problems, problem types, states and operators, state space, searchstrategies

Informed Search Strategies

Best first search, A* algorithm, heuristic functions, Iterative deepening A*(IDA), small memory A*(SMA); Game playing - Perfect decision game, imperfect decision game, evaluation function, alpha-beta pruning

Reasoning

Representation, Inference, Propositional Logic, predicate logic (first order logic), logical reasoning, forward chaining, backward chaining; AI languages and tools - Lisp, Prolog, CLIPS

Planning

Basic representation of plans, partial order planning, planning in the blocks world, hierarchical planning, conditional planning, representation of resource constraints, measures, temporal constraints

Uncertainty

Basic probability, Bayes rule, Belief networks, Default reasoning, Fuzzy sets and fuzzy logic; Decision making- Utility theory, utility functions, Decision theoretic expert systems

Inductive learning

decision trees, rule based learning, current-best-hypothesis search, least- commitment search , neural networks, reinforcement learning, genetic algorithms; Other learning methods - neural networks, reinforcement learning, genetic algorithms

Communication

Communication among agents, natural language processing, formal grammar, parsing, grammar

Text Books: 
Name : 
Artificial Intelligence – A Modern Approach
Author: 
Stuart Russell and Peter Norvig
Publication: 
Pearson Education
Name : 
Artificial Intelligence
Author: 
Kevin Knight, Elaine Rich, B. Nair
Publication: 
McGraw Hill, 2008
Reference Books: 
Name: 
Artificial Intelligence
Author: 
George F. Luger
Publication: 
Pearson Education, 2001.
Name: 
Artificial Intelligence: A New Synthesis
Author: 
Nils J. Nilsson
Publication: 
Morgan Kauffman, 2002
Syllabus PDF: 
AttachmentSize
PDF icon ELECTIVE IV (AI) .pdf146.7 KB
branch: 
BDA
Course: 
2014
2016
Stream: 
B.Tech