Game Theory and Multiagent Systems

This course is no longer offered


CMPE Degree: This course is for the CMPE degree.

EE Degree: This course is for the EE degree.

Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.

Technical Interest Group(s) / Course Type(s): Systems and Controls

Course Coordinator:

Prerequisites: (ECE 2040 [min C] or ECE 3710 or ISYE 3133) and (CEE/ISYE 3770 or MATH 3670 or ISYE 2027)

Corequisites: None.

Catalog Description

An introduction to game theory and its application to multiagent systems, including distributed routing, multivehicle control, and networked systems.

Course Outcomes

  1. understand the notion of an agent.
  2. discuss the key issues associated with constructing agents, building and implementing models.
  3. understand the types of game theoretic interactions possible in multiagent systems.
  4. be familiar with the main engineering application areas of multiagent systems.
  5. most importantly, be able to design meaningful agent-based systems.

Student Outcomes

In the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.

1. ( ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics

2. ( ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors

3. ( ) An ability to communicate effectively with a range of audiences

4. ( ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts

5. ( ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives

6. ( ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions

7. ( ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Strategic Performance Indicators (SPIs)

Not Applicable

Course Objectives

  1. develop an understanding of multiagent systems and their limitations [a]
  2. learn the skills needed to apply game theory to engineering applications involving a collection of decision making entities [a,c]

Topical Outline

Game theory:
- Pure strategy Nash equilibrium
- Rationalizability and dominance
- Probability review
- Expected utility
- Mixed strategy Nash equilibrium
- Zero sum games
- Bayesian games & imperfect information
- Extensive form games
- Repeated games
- Bargaining

Multagent systems:
- Coordination games
- Markov chains
- Distributed optimization
- Strategic learning