Rogelio Adobbati

GameBots: a flexible test bed for multiagent team research

GameBots [1] is a virtual reality platform that allows the creation and evaluation of intelligent agents that interact with a rich 3D continuous dynamic environment. As opposed to previous test beds that focus on a single task and environment (such as soccer simulation [4]), GameBots does not define a single benchmark task. Instead, the GameBots platform comes with a wide variety of predefined tasks and environments and allows anyone to extend these in various ways, or create new challenges. This enables multiagent systems (MAS) and artificial intelligence researchers to explore a wide variety of algorithms and techniques, in areas such as spatial navigation, learning, dynamic resource allocation, multiagent planning, plan-recognition, collaboration, distributed adversarial planning, and human-machine teamwork. GameBots is composed of two components. The first of these is a freely-available open source extension of the commercial Unreal Tournament game engine [3]. It defines a socket-based API allowing anyone to create agents that can participate in any Unreal Tournament games. The second component is a set of development tools, sample source code, and nonviolent graphics (replacements for the default graphics) that form a basic development environment to help users get started in using GameBots. Gal A. Kaminka, Manuela M. Veloso, Steve Schaffer,

Building integrated mobile robots for soccer competition

Robot soccer competition provides an excellent opportunity for robotics research. In particular, robot players in a soccer game must perform real-time visual recognition, navigate in a dynamic field, track moving objects, collaborate with teammates, and strike the ball in the correct direction. All these tasks demand robots that are autonomous (sensing, thinking, and acting as independent creatures), efficient (functioning under time and resource constraints), cooperative (collaborating with each other to accomplish tasks that are beyond individuals capabilities), and intelligent (reasoning and planing actions and perhaps learning from experience). Furthermore, all these capabilities must be integrated into a single and complete system. To build such integrated robots, we should use different approaches from those employed in separate research disciplines. This paper describes our experience (problems and solutions) in this aspect for building soccer robots. Our robots share the same general architecture and basic hardware, but they have integrated abilities to play different roles and utilize different strategies in their behavior. Our philosophy in building these robots is to use the least possible sophistication to make them as robust as possible. In RoboCup97, our Dreamteam robots performed well (scored 8 of 9 goals of all teams in the league) and won the world championship in the middle-sized robot league.

Toward Integrated Soccer Robots

Robot soccer competition provides an excellent opportunity for integrated robotics research. In particular, robot players in a soccer game must recognize and track objects in real time, navigate in a dynamic field, collaborate with teammates, and strike the ball in the correct direction. All these tasks demand robots that are autonomous (sensing, thinking, and acting as independent creatures), efficient (functioning under time and resource constraints), cooperative (collaborating with each other to accomplish tasks that are beyond an individuals capabilities), and intelligent (reasoning and planning actions and perhaps learning from experience). Furthermore, all these capabilities must be integrated into a single and complete system, which raises a set of challenges that are new to individual research disciplines. This article describes our experience (problems and solutions) in these aspects. Our robots share the same general architecture and basic hardware, but they have integrated abilities to play different roles (goalkeeper, defender, or forward) and use different strategies in their behavior. Our philosophy in building these robots is to use the least sophistication to make them as robust and integrated as possible. At RoboCup-97, held as part of the Fifteenth International Joint Conference on Artificial Intelligence, these integrated robots performed well, and our DREAMTEAM won the world championship in the middle-size robot league.

Integrated Reactive Soccer Agents

Robot soccer competition provides an excellent opportunity for robotics research. In particular, robot players in a soccer game must perform realtime visual recognition, navigate in a dynamic field, track moving objects, collaborate with teammates, and hit the ball in the correct direction. All these tasks demand robots that are autonomous (sensing, thinking, and acting as independent creatures), efficient (functioning under time and resource constraints), cooperative (collaborating with each other to accomplish tasks that are beyond individuals capabilities), and intelligent (reasoning and planing actions and perhaps learning from experience). To build such integrated robots, we should use different approaches from those employed in separate research disciplines. In the 1997 RoboCup competition, the USC/ISI robot team, called Dreamteam, fought hard and won the world championship in the middle-sized robot league. These robots all share the same general architecture and basic hardware, but they have integrated abilities to play different roles (goal-keeper, defender or forward) and utilize different strategies in their behavior. Our philosophy in building these robots is to use the least possible sophistication to make them as robust as possible. This paper describes our experiences during the competition as well as our new improvements to the team.

Autonomous Soccer Robots

The Robocup 97 competition provides an excellent opportunity to demonstrate the techniques and methods of artificial intelligence, autonomous agents and computer vision. On a soccer field the core capabilities a player must have are to navigate the field, track the ball and other agents, recognize the difference between agents, collaborate with other agents, and hit the ball in the correct direction. USCs Dreamteam of robots can be described as a group of mobile autonomous agents collaborating in a rapidly changing environment. The key characteristic of this team is that each soccer robot is an autonomous agent, self-contained with all of its essential capabilities on-board. Our robots share the same general architecture and basic hardware, but they have integrated abilities to play different roles (goalkeeper, defender or forward) and utilize different strategies in their behavior. Our philosophy in building these robots is to use the least possible sophistication to make them as robust as possible. In the 1997 RoboCup competition, the Dreamteam played well and won the world championship in the middle-sized robot league.

Building integrated robots for soccer competition

Middle sized robot soccer competition provides an excellent opportunity for distributed robotic systems. In particular, a team of dog sized robot players must perform real time visual recognition, navigate in a dynamic field, track moving objects and collaborate with teammates (R.C. Arkin, 1987). Our design philosophy for the system architecture is that we view each robot as a complete and active physical entity, who can intelligently maneuver and perform in realistic and challenging surroundings. In order to survive the rapidly changing environment in a soccer game, each robot must be physically strong, computationally fast, and behaviorally accurate. Considerable importance is given to an individual robots ability to perform on its own without any off-board resources such as global, birds eye view cameras or remote computing processors. Each robots behavior must base on its own sensor data, decision making software, and eventually communication with teammates.

DREAMTEAM 99: Team Description Paper

The annual Robocup soccer competition is an excellent opportunity for our robotics and agent research. We view the competition as a rigorous testbed for our methods and a unique way of validating our ideas. After two years of competition, we have begun to understand what works (we won the competition in Tokyo 97) and what does not work (we failed to advance to the second round in Paris 98). This paper presents an overview of our goals in Robocup, our philosophy in building soccer playing robots and the methods we are employing in our efforts.

PESCE: a visual generator for software understanding

We present a short overview of PESCE, a system that addresses the problem of automatically generating consistent visual explanations of software.

Purposeful Behavior in Robot Soccer Team Play

The annual robot soccer competition (RoboCup) provides an excellent opportunity for research in distributed robotic systems. A robotic soccer team demands integrated robots that are autonomous, efficient, cooperative, and intelligent. In this paper, we introduce the concept of Purposeful Behavior, to tackle the problem of achieving reactive and coordinated behavior in a team of autonomous robots. We are building a new control framework for autonomous robots to reason about goals and actions, react to unexpected situations, learn from humans and experience, and collaborate with teammates. Building such robots may require techniques that are different from those employed in separate research disciplines. We describe our experience in building these soccer robots and highlights problems and solutions that are unique to such multi-agent robotic systems in general. These problems include a framework for multi-agent programming, agent modeling and architecture, evaluation of multi-agent systems, and decentralized skill composition.