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.
|Submission Deadline:||September 1, 2005|
|Send Papers to Reviewers:||September 15, 2005|
|Reviews Due Back to Editors:||November 1, 2005|
|Decisions Announced:||November 15, 2005|
|Camera-Ready Due:||January 15, 2006|
|Print Publication:||early 2006|
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