Learning and Computational Game Theory:
Special Issue of the Machine Learning Journal

The purpose of this special issue on learning in games is to give active researchers an opportunity to share significant contributions in this rapidly growing area at the intersection of machine learning and economics.

The emphasis of the special issue will be on how decision makers with individual utility functions learn to behave in interactive situations. It is critical that all submissions clearly state the problem setting in which the results apply---what information is available to the individual players (their own actions, the actions of others, utilities of all players, identity of the other players, etc.), how their performance is to be judged (utility of the player, social utility, convergence to equilibrium, stability of learned behavior, etc.), the model of uncertainty (randomized payoffs, noise in action perception, stochastic action effects, etc.), the information structure of the game and the permissible equilibrium concepts, and any computational complexity concerns.

Submissions are expected to represent high-quality, significant contributions in the area of machine learning and computational game theory. Authors are encouraged to follow formatting guidelines for Machine Learning manuscripts.

Administrative notes

• Authors retain the copyrights to their papers. (See publication agreement on the MLJ website.)
• Submissions and reviewing will be handled electronically using standard procedures for Machine Learning (http://mach.edmgr.com).
• Authors must register with the system before they can submit their manuscripts.
• Authors must select the appropriate Article Type -- Learning and Computational Game Theory -- when submitting their manuscripts.
• Accepted papers will be published electronically and citable immediately (before the print version appears).

Schedule

This page maintained by Michael Littman mlittman@cs.rutgers.edu