Intelligent agents

Summary

Software agents are widely used to control physical, economic and financial processes. The course presents practical methods for implementing software agents and multi-agent systems, supported by programming exercises, and the theoretical underpinnings including computational game theory.

Content

Learning Prerequisites

Recommended courses

Intelligence Artificielle or another introductory course to AI

Learning Outcomes

By the end of the course, the student must be able to:

• Choose and implement methods for rational decision making in software agents, based on decision processes and AI planning techniques
• Choose and implement methods for efficient rational decision making in teams of multiple software agents
• Model scenarios with multiple self-interested agents in the language of game theory
• Evaluate the feasibility of achieving goals with self-interested agents using game theory
• Design, choose and implement mechanisms for self-interested agents using game theory
• Implement systems of software agents using agent platforms

Teaching methods

Ex cathedra, practical programming exercises

Expected student activities

Lectures: 3 hours

Reading: 3 hours

Assignments/programming: 4 hours

Assessment methods

Midterm and quizzes 30%, final exam 70%

Resources

Bibliography

Michael Wooldridge : An Introduction to MultiAgent Systems - Second Edition, John Wiley & Sons, 2009

Stuart Russell and Peter Norvig: Artificial Intelligence: A Modern Approach (2nd/3rd/4th Edition), Prentice Hall Series in Artificial Intelligence, 2003/2009/2015.

Ressources en bibliothèque

• Artificial Intelligence: A Modern Approach / Russell
• An Introduction to MultiAgent Systems / Wooldridge

Moodle Link

• https://go.epfl.ch/CS-430