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Vision, Strategy, and Localization Using the Sony Robots at RoboCup-98

AI Magazine

Sony has provided a robot platform for research and development in physical agents, namely, fully autonomous legged robots. In this article, we describe our work using Sony's legged robots to participate at the RoboCup-98 legged robot demonstration and competition. Robotic soccer represents a challenging environment for research in systems with multiple robots that need to achieve concrete objectives, particularly in the presence of an adversary. Furthermore, RoboCup offers an excellent opportunity for robot entertainment. We introduce the RoboCup context and briefly present Sony's legged robot. We developed a vision-based navigation and a Bayesian localization algorithm. Team strategy is achieved through predefined behaviors and learning by instruction.


Agent Oriented Design of a Soccer Robot Team

AAAI Conferences

The multi-agent paradigm is widely used to provide solutions to a variety of organizational problems related to the collective achievement of one or more tasks. All these problems share a common difficulty of design: how to proceed from a global specification of the collective task to the specification of the local behaviors, which are to be provided to the agents? We have defined the Cassiopeia method whose specificity is to articulate the design of a multi-agent system around the notion of organization. This paper reports the use of this method for designing and implementing the organization of a soccer robot team. We show why we chose this application and how we designed it, we discuss its interest and inherent difficulties, in order to clearly express the needs for a design methodology dedicated to DAI. Introduction The multi-agent paradigm is widely used to provide solutions to a variety of organizational problems related to the collective achievement of one or more tasks: computer supported cooperative work, flexible workshop or network management, distributed process control, or coordination of patrols of drones (Avouris & Gasser 1992) (Werner Demazeau 1992) (Demazeau & Muller 1991). All these problems share a common difficulty of design: how to proceed from a global specification of the collective task to the specification of the individual behaviors, which are to be provided to the agents that achieve the task. A problem of organization has to be solved, most of the time in a dynamic fashion, so as to obtain the collective achievement of the considered task.


RoboCup: A Challenge Problem for AI

AI Magazine

The Robot World-Cup Soccer (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where a wide range of technologies can be integrated and examined. The first RoboCup competition will be held at the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. A robot team must actually perform a soccer game, incorporating various technologies, including design principles of autonomous agents, multiagent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor fusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup. This article describes technical challenges involved in RoboCup, rules, and the simulation environment.


Autonomous Agents Research in Robotics: A Report from the Trenches

AAAI Conferences

This paper surveys research in robotics in the AAMAS (Au- tonomous Agents and Multi-Agent Systems) community. It argues that the autonomous agents community can, and has, impact on robotics. Moreover, it argues that agents re- searchers should proactively seek to impact the robotics com- munity, to prevent independent re-discovery of known results, and to benefit autonomous agents science. To support these claims, I provide evidence from my own research into multi- robot teams, and from others’.


FS97-02-030.pdf

AAAI Conferences

Teamwork in these complex, dynamic domains is more than a simple union of simultaneous coordinated activity. An illustrative example provided by Cohen and Levesque(Cohen & Levesque 1991) -- worth repeating, given that the difference between simple coordination and teamwork is often unacknowledged in the literature -- focuses on the distinction between ordinary traffic and driving in a convoy. Ordinary traffic is simultaneous and coordinated by traffic signs, but it is not considered teamwork. Driving in a convoy, however, is an example of teamwork. The difference in the two situations is that while teamwork does involve coordination, in addition, it at least involves a common team goal and cooperation among team members. This short note focuses on the development of a general model of teamwork to enable a team to act coherently, overcoming the uncertainties of complex, dynamic environments. In particular, in these environments, team members often encounter differing, incomplete and possibly inconsistent views of the world and (mental) state of other agents.