Game Theory detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Machine Learning 6th Sem scheme and its subjects, do visit AI&ML 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&ML Professional Elective-VII syllabus scheme. The detailed syllabus of game theory is as follows.
Course Objectives:
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Unit I
INTRODUCTION
Introduction — Making rational choices: basics of Games — strategy — preferences — payoffs — Mathematical basics — Game theory — Rational Choice — Basic solution concepts-non-cooperative versus cooperative games — Basic computational issues — finding equilibria and learning in games- Typical application areas for game theory (e.g. Google’s sponsored search, eBay auctions, electricity trading markets).
Unit II
GAMES WITH PERFECT INFORMATION
Games with Perfect Information — Strategic games — prisoner’s dilemma, matching pennies -Nash equilibria —mixed strategy equilibrium — zero-sum games
Unit III
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Unit IV
NON-COOPERATIVE GAME THEORY
Non-cooperative Game Theory — Self-interested agents — Games in normal form — Analyzing games: from optimality to equilibrium — Computing Solution Concepts of Normal — Form Games — Computing Nash equilibria of two-player, zero-sum games —Computing Nash equilibria of two-player, general- sum games — Identifying dominated strategies
Unit V
MECHANISM DESIGN
Aggregating Preferences — Social Choice — Formal Model — Voting — Existence of social functions — Ranking systems — Protocols for Strategic Agents: Mechanism Design — Mechanism design with unrestricted preferences
Course Outcomes:
Upon Completion of the course, the students will be able to
- Discuss the notion of a strategic game and equilibria and identify the characteristics of main applications of these concepts.
- Discuss the use of Nash Equilibrium for other problems.
- Identify key strategic aspects and based on these be able to connect them to appropriate game theoretic concepts given a real world situation.
- Identify some applications that need aspects of Bayesian Games.
- Implement a typical Virtual Business scenario using Game theory.
Laboratory Exercises
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Text Books:
- M. J. Osborne, An Introduction to Game Theory. Oxford University Press, 2012.
- M. Machler, E. Solan, S. Zamir, Game Theory, Cambridge University Press, 2013.
- N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani, Algorithmic Game Theory. Cambridge University Press, 2007.
- A.Dixit and S. Skeath, Games of Strategy, Second Edition. W W Norton & Co Inc, 2004.
- YoavShoham, Kevin Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press 2008.
- Zhu Han, DusitNiyato, WalidSaad, TamerBasar and Are Hjorungnes, “Game Theory in Wireless and Communication Networks”, Cambridge University Press, 2012.
- Y.Narahari, “Game Theory and Mechanism Design”, IISC Press, World Scientific.
- William Spaniel, “Game Theory 101: The Complete Textbook”, CreateSpace Independent Publishing, 2011.
For detailed syllabus of all the other subjects of Artificial Intelligence & Machine Learning 6th Sem, visit AI&ML 6th Sem subject syllabuses for 2021 regulation.
For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.