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Jeffrey Long

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

IJCAI Conference 2011 Conference Paper

Real-Time Opponent Modeling in Trick-Taking Card Games

  • Jeffrey Long
  • Michael Buro

As adversarial environments become more complex, it is increasingly crucial for agents to exploit the mistakes of weaker opponents, particularly in the context of winning tournaments and competitions. In this work, we present a simple post processing technique, which wecall Perfect Information Post-Mortem Analysis (PIPMA), that can quickly assess the playing strength of an opponent in certain classes of game environments. We apply this technique to skat, a popular German card game, and show that we can achieve substantial performance gains against not only players weaker than our program, but against stronger players as well. Most importantly, PIPMA can model the opponent after only a handful of games. To our knowledge, this makes our work the first successful example of an opponent modelling technique that can adapt its play to a particular opponent in real time in a complex game setting.

AAAI Conference 2010 Conference Paper

Understanding the Success of Perfect Information Monte Carlo Sampling in Game Tree Search

  • Jeffrey Long
  • Nathan Sturtevant
  • Michael Buro
  • Timothy Furtak

Perfect Information Monte Carlo (PIMC) search is a practical technique for playing imperfect information games that are too large to be optimally solved. Although PIMC search has been criticized in the past for its theoretical deficiencies, in practice it has often produced strong results in a variety of domains. In this paper, we set out to resolve this discrepancy. The contributions of the paper are twofold. First, we use synthetic game trees to identify game properties that result in strong or weak performance for PIMC search as compared to an optimal player. Second, we show how these properties can be detected in real games, and demonstrate that they do indeed appear to be good predictors of the strength of PIMC search. Thus, using the tools established in this paper, it should be possible to decide a priori whether PIMC search will be an effective approach to new and unexplored games.