Poker-AI.org

PROPERTIES

type = "inproceedings"
title = "Rating Players in Games with Real-Valued Outcomes(Extended Abstract)"
author = "BOWLING, Michael and BURCH, Neil and ARCHIBALD, Christopher and RUTHERFORD, Matthew"
year = "2013"
organization = "University of Alberta - Department of Computing Science; University of Denver - Department of Computer Science"
url = "http://poker.cs.ualberta.ca/publications/AAMAS13-ratings.pdf"
language = "en"
size = "2"
rights holder = "International Foundation for Autonomous Agents and Multiagent Systems"


SUBJECT

abstract = "Game-theoretic models typically associate outcomes with real valued utilities, and rational agents are expected to maximize their expected utility. Currently fielded agent rating systems, which aim to order a population of agents by strength, focus exclusively on games with discrete outcomes, e.g., win-loss in two-agent settings or an ordering in the multi-agent setting. These rating systems are not well-suited for domains where the absolute magnitude of utility rather than just the relative value is important. We introduce the problem of rating agents in games with real-valued outcomes and survey applicable existing techniques for rating agents in this setting. We then propose a novel rating system and an extension for all of these rating systems to games with more than two agents, showing experimentally the advantages of our proposed system."

categories = "ACM | I.2.1 Applications and Expert Knowledge-Intensive Systems > f. Games and infotainment; ACM | I.2.11 Distributed Artificial Intelligence > d. Multiagent systems"

general terms = "Experimentation; Economics; Measurement"

keywords = "Player Ratings; Real-valued Games; Least Squares; Ridge Regression"