Nardi, Daniele (Sapienza University of Rome) | Noda, Itsuk (National Institute of Advanced Industrial Science and Technology) | Ribeiro, Fernando (University of Minho) | Stone, Peter (Technische Universität Darmstadt) | Stryk, Oskar von (Carnegie Mellon University) | Veloso, Manuela
RoboCup was created in 1996 by a group of Japanese, American, and European artificial intelligence and robotics researchers with a formidable, visionary long-term challenge: By 2050 a team of robot soccer players will beat the human World Cup champion team. In this article, we focus on RoboCup robot soccer, and present its five current leagues, which address complementary scientific challenges through different robot and physical setups. Full details on the status of the RoboCup soccer leagues, including league history and past results, upcoming competitions, and detailed rules and specifications are available from the league homepages and wikis.
Transfer learning has recently gained popularity due to the development of algorithms that can successfully generalize information across multiple tasks. This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to (1) familiarize readers with the transfer learning problem in reinforcement learning domains, (2) explain why the problem is both interesting and difficult, (3) present a selection of existing techniques that demonstrate different solutions, and (4) provide representative open problems in the hope of encouraging additional research in this exciting area.
Karlgren, Jussi, Kanerva, Pentti, Gamback, Bjorn, Forbus, Kenneth D., Tumer, Kagan, Stone, Peter, Goebel, Kai, Sukhatme, Gaurav S., Balch, Tucker, Fischer, Bernd, Smith, Doug, Harabagiu, Sanda, Chaudri, Vinay, Barley, Mike, Guesgen, Hans, Stahovich, Thomas, Davis, Randall, Landay, James
The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2002 Spring Symposium Series, held Monday through Wednesday, 25 to 27 March 2002, at Stanford University. The nine symposia were entitled (1) Acquiring (and Using) Linguistic (and World) Knowledge for Information Access; (2) Artificial Intelligence and Interactive Entertainment; (3) Collaborative Learning Agents; (4) Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction; (5) Intelligent Distributed and Embedded Systems; (6) Logic-Based Program Synthesis: State of the Art and Future Trends; (7) Mining Answers from Texts and Knowledge Bases; (8) Safe Learning Agents; and (9) Sketch Understanding.
Veloso, Manuela M., Balch, Tucker, Stone, Peter, Kitano, Hiroaki, Yamasaki, Fuminori, Endo, Ken, Asada, Minoru, Jamzad, M., Sadjad, B. S., Mirrokni, V. S., Kazemi, M., Chitsaz, H., Heydarnoori, A., Hajiaghai, M. T., Chiniforooshan, E.
Stone, Peter, Asada, Minoru, Balch, Tucker, D'Andrea, Raffaelo, Fujita, Masahiro, Hengst, Bernhard, Kraetzschmar, Gerhard, Lima, Pedro, Lau, Nuno, Lund, Henrik, Polani, Daniel, Scerri, Paul, Tadokoro, Satoshi, Weigel, Thilo, Wyeth, Gordon
The Fourth Robotic Soccer World Championships (RoboCup-2000) was held from 27 August to 3 September 2000 at the Melbourne Exhibition Center in Melbourne, Australia. RoboCup-2000 showed dramatic improvement over past years in each of the existing robotic soccer leagues (legged, small size, mid size, and simulation) and introduced RoboCup Jr. competitions and RoboCup Rescue and Humanoid demonstration events. The RoboCup Workshop, held in conjunction with the championships, provided a forum for the exchange of ideas and experiences among the different leagues. This article summarizes the advances seen at RoboCup-2000, including reports from the championship teams and overviews of all the RoboCup events.
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.
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 article then focuses on the agent behaviors, ranging from low-level individual behaviors to coordinated, strategic team behaviors.