@inproceedings{0ab2b3c6a9c44719b5482372b0963c74,
title = "Markov games of incomplete information for multi-agent reinforcement learning",
abstract = "Partially observable stochastic games (POSGs) are an attractive model for many multi-agent domains, but are computationally extremely difficult to solve. We present a new model, Markov games of incomplete information (MGII) which imposes a mild restriction on POSGs while overcoming their primary computational bottleneck. Finally we show how to convert a MGII into a continuous but bounded fully observable stochastic game. MGIIs represents the most general tractable model for multi-agent reinforcement learning to date.",
author = "{Mac Dermed}, Liam and Isbell, {Charles L.} and Weiss, {Lora G.}",
year = "2011",
month = nov,
day = "2",
language = "English (US)",
isbn = "9781577355298",
series = "AAAI Workshop - Technical Report",
pages = "43--51",
booktitle = "Interactive Decision Theory and Game Theory - Papers from the 2011 AAAI Workshop, Technical Report",
note = "2011 AAAI Workshop ; Conference date: 08-08-2011 Through 08-08-2011",
}