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ABSTRACT: Team ISIS (ISI Synthetic) successfully participated in the first international RoboCup soccer tournament (RoboCup'97) held in Nagoya, Japan, in August 1997. ISIS won the third-place prize in over 30 teams that participated in the simulation league of RoboCup'97 (the most popular among the three RoboCup'97 leagues. In terms of research accomplishments, ISIS illustrated the usefulness of an explicit model of teamwork both in terms of reduced development time and improved teamwork flexibility. ISIS also took some initial steps towards learning of individual player skills. This paper discusses the design of ISIS in detail, with particular emphasis on its novel approach to teamwork.
11/2006: pages 123-131;
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ABSTRACT: 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. USC's 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.
04/2006: pages 295-304;
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Advances in Knowledge Discovery and Data Mining, 6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002, Proceedings; 01/2002
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Autonomous Agents and Multi-Agent Systems. 01/2001; 4:115-129.
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Principles of Data Mining and Knowledge Discovery, 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001, Proceedings; 01/2001
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ABSTRACT: 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.
12/1999: pages 59-64;
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ABSTRACT: . Team ISIS #ISI Synthetic# successfully participated in the #rst international RoboCup soccer tournament #RoboCup'97# held in Nagoya, Japan, in August 1997. ISIS won the third-place prize in over 30 teams that participated in the simulation league of RoboCup'97 #the most popular among the three RoboCup'97 leagues#. In terms of research accomplishments, ISIS illustrated the usefulness of an explicit model of teamwork both in terms of reduced development time and improved teamwork #exibility. ISIS also took some initial steps towards learning of individual player skills. This paper discusses the design of ISIS in detail, with particular emphasis on its novel approach to teamwork. 1 Introduction The ISIS #ISI Synthetic# team of synthetic soccer-players won the third-place prize in the RoboCup'97 simulation league tournament. Developed at the University of Southern California's Information Sciences Institute #ISI#, ISIS was also the top US simulation team. In terms of research ...
10/1999;
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ABSTRACT: Multi-agent collaboration or teamwork and learning are two critical research challenges in a large number of multi-agent applications. These research challenges are highlighted in RoboCup, an international project focused on robotic and synthetic soccer as a common testbed for research in multi-agent systems. This article describes our approach to address these challenges, based on a team of soccer-playing agents built for the simulation league of RoboCup --- the most popular of the RoboCup leagues so far.
01/1999;
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Artif. Intell. 01/1999; 110:215-239.
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01/1999
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ABSTRACT: 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 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 individual’s 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.
12/1998: pages 286-298;
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ABSTRACT: 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 individual's 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, and this raises a set of challenges that are new to individual research disciplines. This paper describes our experience (problems and solutions) in these aspects. Our robots share the same general architecture and basic hardware, but they have integrated abi...
03/1998;
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AI Magazine. 01/1998; 19:56.
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AI Magazine. 01/1998; 19:79-85.
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RoboCup-98: Robot Soccer World Cup II; 01/1998
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[show abstract]
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ABSTRACT: 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 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 individual's 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 gen...
12/1997;
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ABSTRACT: ayer contains modules for mission (re)planning. The lower layer is a combination of production rules and behavior-based systems [3] with each behavior represented as a set of rules. These rules are different from traditional productions in that they have associated probabilities and make predictions. We believe the probabilities are important for robust behaviors in a real environment, and the predictions (i.e., the expected consequences of the robot's actions) are the key for self-organized learning and discovery. Given a set of goals delivered from the higher layer, the behavior rules will compete with each other based on the current sensor readings. Actions associated with the winning rules are then executed (these actions are in some sense collaborating with each other). This cycle of perception, decision, and action repeats itself until the goals are accomplished. In the case where the goals are impossible to reach, the lower layer will pass that failure to the higher layer for re
07/1997;
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ABSTRACT: The Robocup 97 competition provides an opportunity to demonstrate the techniques and methods of artificial intelligence, autonomous agents and computer vision. In this competition each of the robots (or agents) must know how to dribble, shoot, pass, and recover the ball from an opponent. Each agent must also be able to evaluate its position with respect to its teammates and opponents, and then decide whether to wait for a pass, run for the ball, cover an opponent's attack, or go to help a teammate; while at the same time following the rules of the game. The most important feature of the USC/ISI robot soccer team is that every robot is autonomous and self-contained with all of its essential capabilities on-board. There are three types of soccer playing position that a robot can be: goal keeper, defender and forward. The role that a robot plays on the field influences the robot's perception of the environment and its playing strategy on the field. While each robot has different motivatio...
05/1997;
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RoboCup-97: Robot Soccer World Cup I; 01/1997
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ABSTRACT: Increasingly, multi-agent systems are being designed for a variety of complex, dynamic domains. Effective agent interactions in such domains raise some of the most fundamental research challenges for agent-based systems, in teamwork, multi-agent learning and agent modelling. The RoboCup research initiative, particularly the simulation league, has been proposed to pursue such multi-agent research challenges, using the common testbed of simulation soccer. Despite the significant popularity of RoboCup within the research community, general lessons have not often been extracted from participation in RoboCup. This is what we attempt to do here. We have fielded two teams, ISIS97 and ISIS98, in RoboCup competitions. These teams have been in the top four teams in these competitions. We compare the teams, and attempt to analyze and generalize the lessons learned. This analysis reveals several surprises, pointing out lessons for teamwork and for multi-agent learning. 1 Introduction Increasingl...
02/1970;
