COURSE SUMMARY
Course Title: 
Game Theory
Course Code: 
19CCE336
Year Taught: 
2019
Type: 
Elective
Degree: 
Undergraduate (UG)
School: 
School of Engineering
Campus: 
Chennai
Coimbatore

Game Theory is an elective course offered in the B. Tech. in Computer and Communication Engineering program at School of Engineering, Amrita Vishwa Vidyapeetham.

Objectives

  • To introduce the characteristics of natural agents and building blocks involved in biological processes
  • To provide an understanding on the application of bio inspired algorithms to solve complex problems
  • To provide insights into the implementation of bio inspired algorithms

Course Outcomes

  • CO1: To understand phenomena guiding biological processes through self-organization and adaptability
  • CO2: To visualize the effect of low-level interactions on high-level phenomena
  • CO3: To analyze complex engineering problems and solve them by adapting biological processes suitably
  • CO4: To design and implement simple bio-inspired algorithms

CO – PO Mapping

PO/PSO/CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
CO1 2 2 - - - - - - - - - - - -
CO2 3 2 - - - - - - - - - - 2 -
CO3 2 3 2 2 - - - - - - - - 2 2
CO4 2 2 3 3 - - - - - - - - 3 3

Unit 1

Artificial Neural Networks – Pattern classification – Single and Multilayer perceptrons – Backpropagation – Pattern Association – Hebbian learning – Hopfield networks – Bidirectional Associative Memory Networks – Competitive learning – Kohenen’s Self Organizing Maps.

Unit 2

Genetic algorithms – Representation – Reproduction - Crossover and Mutation Operators – Crossover and Mutation rates – Selection mechanisms – Fitness proportionate - ranking and tournament selection – Building Block - Hypothesis and Schema Theorem

Unit 3 

Swarm Intelligence – Stigmergy – Competition and Cooperation – Particle Swarm Optimization – Anatomy of a particle – Velocity and Position updation– PSO topologies – Control parameters –Ant Colony Optimization – Pheromone updation and evaporation.

Textbook(s)

  • Leandro Nunes De Castro, Fernando Jose Von Zuben, “Recent Developments in Biologically Inspired Computing”, Idea Group Publishing, 2005.
  • LaureneFausett, “Fundamentals of neural networks: architectures, algorithms, and applications”, Prentice-Hall, 1994.

Reference(s)

  • Goldberg, , ” Genetic algorithms in search optimization and machine learning”, Addison Wesley, 1999.
  • Xin-She Yang, “Recent Advances in Swarm Intelligence and Evolutionary Computation”, Springer International Publishing, Switzerland, 2015.

Evaluation Pattern

Assessment Internal External
Periodical 1 (P1) 15 -
Periodical 2 (P2) 15 -
*Continuous Assessment (CA) 20 -
End Semester - 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.