----- Forwarded message from "Michael L. Littman" <[EMAIL PROTECTED]> -----
Date: Mon, 16 May 2005 22:12:44 -0400 (EDT)
From: "Michael L. Littman" <[EMAIL PROTECTED]>
Reply-To: [EMAIL PROTECTED]
Subject: MLJ Special Issue on Learning and Computational Game Theory
Learning and Computational Game Theory:
Special Issue of the Machine Learning Journal
http://www.cs.rutgers.edu/~mlittman/topics/mlj05-gametheory/
Amy Greenwald and Michael L. Littman, guest editors
Game theory is concerned with the decisions made by utility-maximizing
individuals in their interactions with other decision makers and their
environment. From its earliest days of study, researchers have
recognized the important relationship between game theory and
learning---using experience from past play to guide future decisions.
Recently, there has been a tremendous increase in research that
applies a computational perspective to learning and games. Exciting
results showing, for example, convergence of learning algorithms to
game-theoretic equilibria and computationally efficient
representations for strategic interactions, are helping to shape our
understanding of learning in the multiagent setting. 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:
http://pages.stern.nyu.edu/~fprovost/MLJ/.)
* 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
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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