ELECTIVE – I : ARTIFICIAL INTELLIGENCE
Prerequisite: programming language like JAVA or Python
Objective: This course will introduce the basic ideas and techniques underlying the
design of intelligent computer systems. Students will develop a basic understanding of
the building blocks of AI as presented in terms of intelligent agents. This course will
attempt to help students understand the main approaches to artificial intelligence such as
heuristic search, game search, logical inference, decision theory, planning, machine
learning, neural networks and natural language processing. Students will be able to
recognize problems that may be solved using artificial intelligence and implement
artificial intelligence algorithms for hands-on experience.
1. Artificial Intelligence: Introduction to AI, History of AI, Emergence Of Intelligent
2. Intelligent Agents: PEAS Representation for an Agent, Agent Environments,
Concept of Rational Agent, Structure of Intelligent agents, Types of Agents.
3. Problem Solving: Solving problems by searching, Problem Formulation, Uninformed
Search Techniques- DFS, BFS, Iterative Deepening, Comparing Different
Techniques, Informed search methods – heuristic Functions, Hill Climbing,
Simulated Annealing, A*, Performance Evaluation.
4. Constrained Satisfaction Problems: Constraint Satisfaction Problems like, map
Coloring, Crypt Arithmetic, Backtracking for CSP, Local Search.
5. Adversarial Search: Games, Minimax Algorithm, Alpha Beta pruning.
6. Knowledge and Reasoning: A knowledge Based Agent, Introduction To Logic,
Propositional Logic, Reasoning in Propositional logic, First Order Logic: Syntax and
Semantics, Extensions and Notational Variation, Inference in First Order Logic,
Unification, Forward and backward chaining, Resolution.
7. Knowledge Engineering: Ontology, Categories and Objects, Mental Events and
8. Planning: Planning problem, Planning with State Space Search, Partial Order
Planning, Hierarchical Planning, Conditional Planning.
9. Uncertain Knowledge and Reasoning: Uncertainty, Representing knowledge in an
Uncertain Domain, Overview of Probability Concepts, Belief Networks, Simple
Inference in Belief Networks
10. Learning: Learning from Observations, General Model of Learning Agents,
Inductive learning, learning Decision Trees, Introduction to neural networks,
Perceptrons, Multilayer feed forward network, Application of ANN, Reinforcement
learning: Passive & Active Reinforcement learning.
11. Agent Communication: Communication as action, Types of communicating agents,
A formal grammar for a subset of English
1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd
Edition, Pearson Publication.
1. George Lugar, “AI-Structures and Strategies for Complex Problem Solving”, 4/e,
2002, Pearson Educations
2. Robert J. Schalkolf, Artificial Inteilligence: an Engineering approach, McGraw Hill,
3. Patrick H. Winston, Artificial Intelligence, 3rd edition, Pearson.
4. Nils J. Nilsson, Principles of Artificial Intelligence, Narosa Publication.
5. Dan W. Patterson, Introduction to Artificial Intelligence and Expert System, PHI.
6. Efraim Turban Jay E.Aronson, “Decision Support Systems and Intelligent Systems”
7. M. Tim Jones, Artificial Intelligence – A System Approach, Infinity Science Press –
8. Christopher Thornton and Benedict du Boulay, “Artificial Intelligence – Strategies,
Applications, and Models through Search, 2nd Edition, New Age International
9. Elaine Rich, Kevin Knight, Artificial Intelligence, Tata McGraw Hill, 1999.
10. David W. Rolston, Principles of Artificial Intelligence and Expert System
Development, McGraw Hill, 1988.