BCS515B Artificial Intelligence
Course Learning Objectives
● Learn the basic principles and theories underlying artificial intelligence, including machine learning, neural networks, natural language processing, and robotics.
● Apply AI techniques to solve real-world problems, including search algorithms, optimization, and decision-making processes.
● Understand the ethical, legal, and societal implications of AI, including topics such as bias, fairness, accountability, and the impact of AI on the workforce and privacy.
SYLLABUS COPY
MODULE - 1
Introduction
What Is AI? , The State of The Art.
Intelligent Agents
Agents and environment, Concept of Rationality, The nature of environment, The structure of agents.
MODULE - 2
Problem‐solving
Problem‐solving agents, Example problems, Searching for Solutions Uninformed Search Strategies
MODULE - 3
Problem‐solving
Informed Search Strategies, Heuristic functions
Logical Agents
Knowledge–based agents, The Wumpus world, Logic, Propositional logic, Reasoning patterns in Propositional Logic
MODULE - 4
First Order Logic
Representation Revisited, Syntax and Semantics of First Order logic, Using First Order logic, Knowledge Engineering In First-Order Logic
Inference in First Order Logic
Propositional Versus First Order Inference, Unification, Forward Chaining
MODULE - 5
Inference in First Order Logic
Backward Chaining, Resolution
Classical Planning
Definition of Classical Planning, Algorithms for Planning as State-Space Search, Planning Graphs
Course outcome
1. Explain the architecture and components of intelligent agents, including their interaction with the AI environment.
2. Apply problem-solving agents and various search strategies to solve a given problem.
3. Illustrate logical reasoning and knowledge representation using propositional and first-order logic.
4. Demonstrate proficiency in representing knowledge and solving problems using first-order logic.
5. Describe classical planning in the context of artificial intelligence, including its goals, constraints, and applications in problem-solving.
Suggested Learning Resources
Text Book
Stuart J. Russell and Peter Norvig, Artificial Intelligence, 3rd Edition, Pearson, 2015
Reference Books
1. Elaine Rich, Kevin Knight, Artificial Intelligence, 3rd edition, Tata McGraw Hill, 2013
2. George F Lugar, Artificial Intelligence Structure and strategies for complex, Pearson Education, 5th Edition, 2011
3. Nils J. Nilsson, Principles of Artificial Intelligence, Elsevier, 1980
4. Saroj Kaushik, Artificial Intelligence, Cengage learning, 2014