Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
}
TY - CHAP
T1 - EA2: The winning strategy for the inaugural lemonade stand game tournament
AU - Sykulski, Adam M.
AU - Chapman, Archie C.
AU - Munoz De Cote, Enrique
AU - Jennings, Nicholas R.
PY - 2010
Y1 - 2010
N2 - We describe the winning strategy of the inaugural Lemonade Stand Game (LSG) Tournament. The LSG is a repeated symmetric 3–player constant–sum finite horizon game, in which a player chooses a location for their lemonade stand on an island with the aim of being as far as possible from its opponents. To receive a high utility in this game, our strategy, EA2, attempts to find a suit- able partner with which to coordinate and exploit the third player. To do this, we classify the behaviour of our opponents using the history of joint interactions in order to identify the best player to coordinate with and how this coordination should be established. This approach is designed to be adaptive to various types of opponents such that co- ordination is almost always achieved, which yields consistently high utilities to our agent, as evidenced by the Tournament results and our subsequent experimental analysis. Our strategy models behaviours of its opponents, rather than situations of the game (e.g. game theo- retic equilibrium or off equilibrium paths), which makes EA2 easy to generalize to many other games.
AB - We describe the winning strategy of the inaugural Lemonade Stand Game (LSG) Tournament. The LSG is a repeated symmetric 3–player constant–sum finite horizon game, in which a player chooses a location for their lemonade stand on an island with the aim of being as far as possible from its opponents. To receive a high utility in this game, our strategy, EA2, attempts to find a suit- able partner with which to coordinate and exploit the third player. To do this, we classify the behaviour of our opponents using the history of joint interactions in order to identify the best player to coordinate with and how this coordination should be established. This approach is designed to be adaptive to various types of opponents such that co- ordination is almost always achieved, which yields consistently high utilities to our agent, as evidenced by the Tournament results and our subsequent experimental analysis. Our strategy models behaviours of its opponents, rather than situations of the game (e.g. game theo- retic equilibrium or off equilibrium paths), which makes EA2 easy to generalize to many other games.
U2 - 10.3233/978-1-60750-606-5-209
DO - 10.3233/978-1-60750-606-5-209
M3 - Chapter
SN - 9781607506058
T3 - Frontiers in Artificial Intelligence and Applications
SP - 209
EP - 214
BT - Frontiers in Artificial Intelligence and Applications
PB - IOS Press
ER -