1391

AI Magazine

The algorithm successfully detects and tracks 11 objects (5 teammates, 5 opponents, and 1 ball) at 30 frames a second. The algorithm determines the position and orientation for the robots. In addition, a Kalman-Bucy filter (Kalman and Bucy 1961) is used as a predictor of the ball's trajectory. This prediction is an integral factor in our robots' control and strategic decisions. Before developing strategic behaviors, the robots need a general control mechanism.


Articles

AI Magazine

The simulator, acting as a server, accepts action commands from fully distributed clients (agents) throughout a 100-millisecond cycle and then updates the world state all at once at the end of the cycle. Agents receive sensory perceptions from the simulator asynchronously and at unpredictable intervals. We view robotic soccer as an example of a periodic team synchronization (PTS) domain. We define PTS domains as domains with the following characteristics: There is a team of autonomous agents A that collaborate toward the achievement of a joint long-term goal G. Periodically, the team can synchronize with no restrictions on communication: The agents can in effect inform each other of their entire internal states and decision-making mechanisms with no adverse effects on the achievement of G. These periods of full communication can be thought of as times at which the team is "offline."


CMUNITED-98 Simulator Team

AI Magazine

The CMUNITED-98 simulator team became the 1998 RoboCup simulator league champion by winning all 8 of its games, outscoring opponents by a total of 66-0. CMUNITED-98 builds on the successful cmunited-97 implementation but also improves on it in many ways. This article gives an overview of the cmunited-98 agent skill and multiagent coordination strategies, emphasizing the recent improvements.


1294

AI Magazine

Robotic soccer is a challenging research domain that involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. Robotic soccer is an example of such complex tasks for which multiple agents need to collaborate in an adversarial environment to achieve specific objectives. Robotic soccer offers a challenging research domain to investigate a large spectrum of issues relevant to the development of complete autonomous agents (Asada et al. 1998; Kitano, Tambe, et al. 1997). The fast-paced nature of the domain necessitates real-time sensing coupled with quick behaving and decision making. The behaviors and decision-making processes can range from the most simple reactive behaviors, such as moving directly toward the ball, to arbitrarily complex reasoning procedures that take into account the actions and perceived strategies of teammates and opponents.


CMUNITED-97: RoboCup-97 Small-Robot World Champion Team

AI Magazine

Robotic soccer is a challenging research domain that involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this article, we describe CMUNITED, the team of small robotic agents that we developed to enter the RoboCup-97 competition. We designed and built the robotic agents, devised the appropriate vision algorithm, and developed and implemented algorithms for strategic collaboration between the robots in an uncertain and dynamic environment. The robots can organize themselves in formations, hold specific roles, and pursue their goals. In game situations, they have demonstrated their collaborative behaviors on multiple occasions. We present an overview of the vision-processing algorithm that successfully tracks multiple moving objects and predicts trajectories. The article then focuses on the agent behaviors, ranging from low-level individual behaviors to coordinated, strategic team behaviors. CMUNITED won the RoboCup-97 small-robot competition at the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan.