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@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={<a href="http://www.ijcai-07.org/">IJCAI-07</a>},
)