Syllabus

Introduction: What is AI, The Foundations of Artificial Intelligence, A brief history of AI, The State of the Art.

Intelligent Agents: Intelligent machines, or what machines can do, Types of Agents and Environments, The concept of Rationality, The Structure of Agents.

Problem Solving by searching: Searching for Solutions, Uninformed Search Strategies, Informed (Heuristic) Search Strategies, Local Searching.

Adversarial Search: Games, Optimal Decisions in Game, Alpha-Beta Pruning.

Knowledge, reasoning and Planning: Knowledge-Based Agents, Logic, Propositional Logic, First Order Logic Inferences in First Order Logic: Forward Channing, Backward Chaining, Propositional Vs First-Order Inference, Unification, Resolution

Classical Planning: Planning Vs Search, Algorithms for Planning as State Space Search, Planning Games.

Uncertain Knowledge and reasoning: Basic Probability Notation, Concept of Joint Probability Distributions, Bayes' Rule and its Use

Probabilistic Reasoning: Representing Knowledge in an Uncertain Domain, Bayesian Networks, Inference in Bayesian Networks.

To explore the full breadth of the field, which encompasses logic, perception, reasoning, learning, and action.

To introduce core AI Ideas and its coverage of Web search and information extraction, and of techniques for learning from very large data sets.

To discuss important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles, and household
Robotics.

To cover theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and Robotics.

After completing this course the student must demonstrate the knowledge and ability to:
1. The students will have a thorough understanding of the fundamental concepts and techniques used in AI-based Systems.
2. To learn AI agents that receive percepts from the environment and perform actions in reactive agents, real-time planners, and decision-theoretic systems.
3. The students will be able to understand the role of learning into unknown environments and to know about knowledge representation and its manipulation to get desired results.
4. To solve complex problems using connectionist AI and symbolic AI.
5. To design and implement AI techniques in NLP. Robotics, web search, machine translation etc.

E. Rich and K. Knight, Artificial Intelligence, Tata McGraw Hill

N. J. Nilsson, Principles of Artificial Intelligence, Narosa

S. Russel and P. Norvig, Artificial Intelligence: a Modern Approach, Pearson

D. W. Patterson, Introduction to Artificial Intelligence and Expert Systems, Prentice Hall of India