Peter Stone's Selected Publications

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Targeted Opponent Modeling of Memory-Bounded Agents

Doran Chakraborty, Noa Agmon, and Peter Stone. Targeted Opponent Modeling of Memory-Bounded Agents. In Proceedings of the Adaptive Learning Agents Workshop (ALA), May 2013.

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Abstract

In a repeated game, a memory-bounded agent selects its next action by basing its policy on a fixed window of past L plays. Traditionally, approaches that attempt to model memory-bounded agents, do so by modeling them based on the past L joint actions. Since the number of possible L sized joint actions grows exponentially with L, these approaches are restricted to modeling agents with a small L. This paper explores an alternative, more efficient mechanism for modeling memory-bounded agents based on high-level features derived from the past L plays. Called Targeted Opponent Modeler against Memory-Bounded Agents, or TOMMBA, our approach successfully models memory-bounded agents, in a sample efficient manner, given a priori knowledge of a feature set that includes the correct features. TOMMBA is fully implemented, with successful empirical results in a couple of challenging surveillance based tasks.

BibTeX Entry

@InProceedings{ALA13-chakrado,
  author = {Doran Chakraborty and Noa Agmon and Peter Stone},
  title = {Targeted Opponent Modeling of Memory-Bounded Agents},
  booktitle = {Proceedings of the Adaptive Learning Agents Workshop (ALA)},
  location = {St. Paul, Minnesota, USA},
  month = {May},
  year = {2013},
  abstract = {
              In a repeated game, a memory-bounded agent selects its
              next action by basing its policy on a fixed window of
              past L plays. Traditionally, approaches that attempt to
              model memory-bounded agents, do so by modeling them
              based on the past L joint actions. Since the number of
              possible L sized joint actions grows exponentially with
              L, these approaches are restricted to modeling agents
              with a small L. This paper explores an alternative, more
              efficient mechanism for modeling memory-bounded agents
              based on high-level features derived from the past L
              plays. Called Targeted Opponent Modeler against
              Memory-Bounded Agents, or TOMMBA, our approach
              successfully models memory-bounded agents, in a sample
              efficient manner, given a priori knowledge of a feature
              set that includes the correct features.  TOMMBA is fully
              implemented, with successful empirical results in a
              couple of challenging surveillance based tasks.
  },
}

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