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 and 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
1

Introduction

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

2

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

3

Reasoning

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

4

Planning

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

5

Uncertainty

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

6

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

7

Communication

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

Text Books: 
Name : 
Artificial Intelligence – A Modern Approach
Author: 
Stuart Russell
Peter Norvig
Publication: 
Pearson Education Press
Edition: 
2001
Name : 
Artificial Intelligence
Author: 
Kevin Knight, Elaine Rich, B. Nair
Publication: 
McGraw Hill
Edition: 
2008
Reference Books: 
Name: 
Artificial Intelligence
Author: 
George F. Luger
Publication: 
Pearson Education
Edition: 
2001
Name: 
Artificial Intelligence: A New Synthesis
Author: 
Nils J. Nilsson
Publication: 
Morgan Kauffman,2002
Syllabus PDF: 
branch: 
BDA
Course: 
2018
Stream: 
B.Tech