Adaptation to other initially unknown agents often requires computing an effective counter-
strategy. In the Bayesian paradigm, one must find a good counter-strategy to the inferred …

Coordinated action for a team of robots is a challenging problem, especially in dynamic,
unpredictable environments. Robot soccer is an instance of a domain where well defined …

ABSTRACT We present our Small Size League (SSL) robot soccer team, CMDragons, which
performed strongly at the RoboCup'13 competition, placing second out of twenty teams after …

Abstract 6D SLAM (Simultaneous Localization and Mapping) or 6D Concurrent Localization
and Mapping of mobile robots considers six dimensions for the robot pose, namely, the x, y …

This paper reviews the proposed opponent modeling algorithms within the soccer simulation
domain. RoboCup soccer simulation 2D is a rich multi agent environment where opponent …

Learning to act in a multiagent environment is a challenging problem. Optimal behavior for
one agent depends upon the behavior of the other agents, which are learning as well …

In multiagent adversarial domains, team agents should adapt to the environment and
opponent. We introduce a model representation as part of a planning process for a …

In many negotiation and bargaining scenarios, a particular agent may need to interact
repeatedly with another agent. Typically, these interactions take place under incomplete …

Research in opponent modelling has shown success, but a fundamental question has been
overlooked: what happens when a modeller is faced with an opponent that cannot be …

Opponent models are necessary in games where the game state is only partially known to
the player, since the player must infer the state of the game based on the opponents actions …