Peter Stone's Selected Publications

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Defining and Using Ideal Teammate and Opponent Models

Peter Stone, Patrick Riley, and Manuela Veloso. Defining and Using Ideal Teammate and Opponent Models. In Proceedings of the Twelfth Annual Conference on Innovative Applications of Artificial Intelligence, 2000.
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Abstract

A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its current action options is most likely to achieve its goals. There is a long history in adversarial game playing of using a model of an opponent which assumes that it always acts optimally. Our research extends this strategy to adversarial domains in which the agents have incomplete information, noisy sensors and actuators, and a continuous action space. We introduce ``ideal-model-based behavior outcome prediction'' (IMBBOP) which models the results of other agents' future actions in relation to their optimal actions based on an ideal world model. Our technique also includes a method for relaxing this optimality assumption. IMBBOP was a key component of our successful CMUnited-99 simulated robotic soccer application. In this paper, we define IMBBOP and illustrate its use within the simulated robotic soccer domain. We include empirical results demonstrating the effectiveness of IMBBOP.

BibTeX Entry

@InProceedings{IAAI2000,
  author =       "Peter Stone and Patrick Riley and Manuela Veloso",
  title =        "Defining and Using Ideal Teammate and Opponent Models",
  BookTitle=     "Proceedings of the Twelfth Annual Conference on Innovative Applications of Artificial Intelligence",
  year =         2000,
  abstract =     {A common challenge for agents in multiagent systems
                  is trying to predict what other agents are going to
                 do in the future. Such knowledge can help an agent
                  determine which of its current action options is
                  most likely to achieve its goals. There is a long
                  history in adversarial game playing of using a model
                  of an opponent which assumes that it always acts
                  optimally. Our research extends this strategy to
                  adversarial domains in which the agents have
                 incomplete information, noisy sensors and actuators,
                  and a continuous action space. We introduce
                  ``ideal-model-based behavior outcome prediction''
                  (IMBBOP) which models the results of other agents'
                  future actions in relation to their optimal actions
                  based on an ideal world model. Our technique also
                 includes a method for relaxing this optimality
                  assumption. IMBBOP was a key component of our
                  successful CMUnited-99 simulated
                  robotic soccer application. In this paper, we define
                  IMBBOP and illustrate its use within the simulated
                  robotic soccer domain. We include empirical results
                  demonstrating the effectiveness of IMBBOP.},
  wwwnote =      {<a href=http://www.aaai.org>AAAI Homepage</a>},
}

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