@COMMENT This file was generated by bib2html.pl version 0.90
@COMMENT written by Patrick Riley
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@InProceedings(IJCAI07-bikram,
author="Bikramjit Banerjee and Peter Stone",
title="General Game Learning using Knowledge Transfer",
BookTitle="The 20th International Joint Conference on Artificial Intelligence",
month="January",year="2007",
pages="672--677",
abstract="
We present a reinforcement learning game player that
can interact with a General Game Playing system and
transfer knowledge learned in one game to expedite
learning in many other games. We use the technique
of value-function transfer where general features
are extracted from the state space of a previous
game and matched with the completely different state
space of a new game. To capture the underlying
similarity of vastly disparate state spaces arising
from different games, we use a game-tree lookahead
structure for features. We show that such
feature-based value function transfer learns
superior policies faster than a reinforcement
learning agent that does not use knowledge
transfer. Furthermore, knowledge transfer using
lookahead features can capture opponent-specific
value-functions, i.e. can exploit an opponent's
weaknesses to learn faster than a reinforcement
learner that uses lookahead with minimax
(pessimistic) search against the same
opponent.
",
wwwnote={IJCAI-07},
)