Course Details
Subject {L-T-P / C} : CS6412 : Artificial Intelligence { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Anup Nandy
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.
Course Objectives
- 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 <br />Robotics.
- To cover theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and Robotics.
Course Outcomes
After completing this course the student must demonstrate the knowledge and ability to: <br />1. The students will have a thorough understanding of the fundamental concepts and techniques used in AI-based Systems. <br />2. To learn AI agents that receive percepts from the environment and perform actions in reactive agents, real-time planners, and decision-theoretic systems. <br />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. <br />4. To solve complex problems using connectionist AI and symbolic AI. <br />5. To design and implement AI techniques in NLP. Robotics, web search, machine translation etc.
Essential Reading
- E. Rich and K. Knight, Artificial Intelligence, Tata McGraw Hill
- N. J. Nilsson, Principles of Artificial Intelligence, Narosa
Supplementary Reading
- 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