MF4018 Artificial Intelligence Syllabus:
MF4018 Artificial Intelligence Syllabus – Anna University PG Syllabus Regulation 2021
OBJECTIVES:
The objective of this course is to enable the students to
(1) Understand the basic concepts of intelligent agents
(2) Develop general-purpose problem-solving agents, logical reasoning agents, and agents that reason under uncertainty
(3) To learn to represent knowledge in solving AI problems
(4) To understand the different ways of designing software agents
(5) Employ AI techniques to solve some of today’s real-world problems.
UNIT I INTELLIGENT AGENTS
Introduction to AI –Agents and Environments –Concept of rationality –Nature of environments –Structure of agents Problem solving agents –search algorithms –uninformed search strategies
UNIT II PROBLEM SOLVING
Heuristic search strategies –heuristic functions Local search and optimization problems –local search in continuous space –search with non-deterministic actions –search in partially observable environments –online search agents and unknown environments
UNIT III GAME PLAYING AND CSP
Game theory –optimal decisions in games –alpha-beta search –monte-carlo tree search –stochastic games –partially observable games Constraint satisfaction problems –constraint propagation – backtracking search for CSP –local search for CSP –structure of CSP
UNIT IV LOGICAL AGENTS
Knowledge-based agents –propositional logic –propositional theorem proving –propositional model checking –agents based on propositional logic First-order logic –syntax and semantics –knowledge representation and engineering –inferences in first-order logic –forward chaining –backward chaining – resolution
UNIT V KNOWLEDGE REPRESENTATION AND PLANNING
Ontological engineering –categories and objects –events –mental objects and modal logic – reasoning systems for categories –reasoning with default information Classical planning –algorithms for classical planning –heuristics for planning –hierarchical planning –non deterministic domains –time, schedule, and resources –analysis
COURSE OUTCOMES:
On successful completion of this course, the students will be able to
1. Explain autonomous agents that make effective decisions in fully informed, partially observable, and adversarial settings
2. Choose appropriate algorithms for solving given AI problems
3. Design and implement logical reasoning agents
4. Design and implement agents that can reason under uncertainty
5. Apply AI for real world problems
TEXT BOOKS:
1. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach‖, Prentice Hall, Third Edition, 2009.
2. I. Bratko, ―Prolog: Programming for Artificial Intelligence‖, Fourth edition, Addison-Wesley Educational Publishers Inc., 2011.
REFERENCES
1. M. Tim Jones, ―Artificial Intelligence: A Systems Approach (Computer Science) ‖, Jones and Bartlett Publishers, Inc.; First Edition, 2008
2. Nils J. Nilsson, ―The Quest for Artificial Intelligence‖, Cambridge University Press, 2009.
3. William F. Clocksin and Christopher S. Mellish, ‖ Programming in Prolog: Using the ISO Standard‖, Fifth Edition, Springer, 2003.
4. Gerhard Weiss, ―Multi Agent Systems‖, Second Edition, MIT Press, 2013.
5. David L. Poole and Alan K. Mackworth, ―Artificial Intelligence: Foundations of Computational Agents‖, Cambridge University Press, 2010.