Journal ArticleDOI
Soccer server: A tool for research on multiagent systems
TLDR
The potential of Soccer Server is demonstrated by reporting an experiment that uses the system to compare the performance of a neural network architecture and a decision tree algorithm at learning the selection of soccer play plans.Abstract:
This article describes Soccer Server, a simulator of the game of soccer designed as a benchmark for evaluating multiagent systems and cooperative algorithms. In real life, successful soccer teams require many qualities, such as basic ball control skills, the ability to carry out strategies, and teamwork. We believe that simulating such behaviors is a significant challenge for computer science, artificial intelligence, and robotics technologies. It is to promote the development of such technologies, and to help define a new standard problem for research, that we have developed Soccer Server. We demonstrate the potential of Soccer Server by reporting an experiment that uses the system to compare the performance of a neural network architecture and a decision tree algorithm at learning the selection of soccer play plans. Other researchers using Soccer Server to investigate the nature of cooperative behavior in a multiagent environment will have the chance to assess their progress at RoboCup-97, an internatio...read more
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Journal ArticleDOI
Multiagent Systems: A Survey from a Machine Learning Perspective
Peter Stone,Manuela Veloso +1 more
TL;DR: This survey of MAS is intended to serve as an introduction to the field and as an organizational framework, and highlights how multiagent systems can be and have been used to build complex systems.
Journal ArticleDOI
Reinforcement learning for RoboCup soccer keepaway
TL;DR: The application of episodic SMDP Sarsa(λ) with linear tile-coding function approximation and variable λ to learning higher-level decisions in a keepaway subtask of RoboCup soccer results in agents that significantly outperform a range of benchmark policies.
Journal ArticleDOI
Reinforcement learning for robot soccer
TL;DR: Several variants of the general batch learning framework are discussed, particularly tailored to the use of multilayer perceptrons to approximate value functions over continuous state spaces, which are successfully used to learn crucial skills in soccer-playing robots participating in the RoboCup competitions.
Proceedings Article
Layered learning in multiagent systems
TL;DR: Ultimately, this dissertation demonstrates that by learning portions of their cognitive processes, selectively communicating, and coordinating their behaviors via common knowledge, a group of independent agents can work towards a common goal in a complex, real-time, noisy, collaborative, and adversarial environment.
Journal ArticleDOI
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
TL;DR: This article compares learning on a complex task with three function approximators, a cerebellar model arithmetic computer (CMAC), an artificial neural network (ANN), and a radial basis function (RBF), and empirically demonstrates that directly transferring the action-value function can lead to a dramatic speedup in learning with all three.
References
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C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
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Proceedings ArticleDOI
RoboCup: The Robot World Cup Initiative
TL;DR: The Robot World Cup Initiative (R, oboCup) is attempt to foster AI and intelligent rohoties research by providing a standard problem where wide range of technologies especially concerning multi-agent research can be integrated and examined.