Game-theoretic approaches have been proposed for addressing the complex problem of assigning limited security resources to protect a critical set of targets. However, many of the standard assumptions fail to address human adversaries who security forces will likely face. To address this challenge, previous research has attempted to integrate models of human decision-making into the game-theoret...

This work presents a generalized theoretical framework that allows incorporation of opponent models into adversary search. We present the M∗ algorithm, a generalization of minimax that uses an arbitrary opponent model to simulate the opponent’s search. The opponent model is a recursive structure consisting of the opponent’s evaluation function and its model of the player. We demonstrate experim...

Information about the opponent is essential to improve automated negotiation strategies for bilateral multi-issue negotiation. In this paper we propose a negotiation strategy that combines a Bayesian technique to learn the preferences of an opponent during bidding and a Tit-for-Tat-like strategy to avoid exploitation by the opponent. The learned opponent model is used to achieve two important g...

This paper proposes a similarity-based approach for opponent modelling in multi-agent games. The classification accuracy is increased by adding derived attributes from imperfect domain theories to the similarity measure. The main contributions are to show how different forms of domain knowledge can be incorporated into similarity measures for opponent modelling, and to show that the situation s...

We summarize approaches to tactical diversity, mobility and field control developed over the recent years in team Gliders, ranging from tactic-dependent evaluation and selection of players’ actions, to dynamic tactics based on Voronoi Diagrams, to tactical analysis and opponent modeling with information dynamics, to a bio-inspired mechanism for dynamic repositioning. All these approaches are su...

This paper studies the learning behavior of self-interested users interacting in a two-user OR-channel interference game. We discuss how a strategic user should learn the behavior of its opponent, adapt its actions, and improve its own performance. Specifically, we investigate the tradeoff that can be made by a user between learning duration and performance, if the opponent plays a mixed strate...