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Showing 1 to 4 of 4 for “"Opponent Modeling"”.

  1. Building a computer poker agent with emphasis on opponent modeling

    … players. We explore this problem from an opponent modeling perspective, using data mining to build a database of player styles that allows our agent to quickly model the strategy of any new opponent. The opponent model is then used to develop a robust counter strategy. A simpler version of …

    mit Repository record for Building a computer poker agent with emphasis on opponent modeling (opens in a new tab)

  2. Risk-aware navigation for UAV digital data collection

    … Finally, we consider the risk in the form of an opponent and limited energy capacity of UAV, for which we resort to the Data-collection Game (DCG) model. DCG is a grid-based two-player stochastic game where the opponent may have different strategies. We propose opponent modeling to improve …

    syracuse-diss Repository record for Risk-aware navigation for UAV digital data collection (opens in a new tab)

  3. Robust and Scalable Multiagent Reinforcement Learning in Adversarial Scenarios

    … information to both agents is critical for modeling this deceptive behavior. This thesis addresses this intent recognition problem using a game-theoretic opponent modeling approach based on a new diversity-driven belief-space ensemble training technique that is used to achieve robustness …

    mit Repository record for Robust and Scalable Multiagent Reinforcement Learning in Adversarial Scenarios (opens in a new tab)

  4. Reasoning and decisions in partially observable games

    … This thesis presents a complete system for modeling and reasoning about such games in a general way under practical computational restrictions. We address three key challenges related to reasoning about extensive form games: (1) compact representation of the game rules, (2) reasoning about …

    uiuc Repository record for Reasoning and decisions in partially observable games (opens in a new tab)