513 research outputs found
Evolution of a robotic soccer player
Robotic soccer is a complex domain where, rather than hand-coding computer programs to control
the players, it is possible to create them through evolutionary methods. This has been successfully
done before by using genetic programming with high-level genes. Such an approach is, however,
limiting. This work attempts to reduce that limit by evolving control programs using genetic
programming with low-level nodes
Synthesized cooperative strategies for intelligent multi-robots in a real-time distributed environment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand
In the robot soccer domain, real-time response usually curtails the development of more complex Al-based game strategies, path-planning and team cooperation between intelligent agents. In light of this problem, distributing computationally intensive algorithms between several machines to control, coordinate and dynamically assign roles to a team of robots, and allowing them to communicate via a network gives rise to real-time cooperation in a multi-robotic team. This research presents a myriad of algorithms tested on a distributed system platform that allows for cooperating multi- agents in a dynamic environment. The test bed is an extension of a popular robot simulation system in the public domain developed at Carnegie Mellon University, known as TeamBots. A low-level real-time network game protocol using TCP/IP and UDP were incorporated to allow for a conglomeration of multi-agent to communicate and work cohesively as a team. Intelligent agents were defined to take on roles such as game coach agent, vision agent, and soccer player agents. Further, team cooperation is demonstrated by integrating a real-time fuzzy logic-based ball-passing algorithm and a fuzzy logic algorithm for path planning. Keywords Artificial Intelligence, Ball Passing, the coaching system, Collaborative, Distributed Multi-Agent, Fuzzy Logic, Role Assignmen
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
Event-driven Hybrid Classifier Systems and Online Learning for Soccer Game Strategies
The field of robot soccer is a useful setting for the study of artificial intelligence and machin
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Keyframe Sampling, Optimization, and Behavior Integration: A New Longest Kick in the RoboCup 3D Simulation League
Even with improvements in machine learning enabling robots to
quickly optimize and perfect their skills, developing a seed skill from
which to begin an optimization remains a necessary challenge for large
action spaces. This thesis proposes a method for creating and using
such a seed by i) observing the effects of the actions of another robot,
ii) further optimizing the skill starting from this seed, and iii) em-
bedding the optimized skill in a full behavior. Called KSOBI, this
method is fully implemented and tested in the complex RoboCup 3D
simulation domain. The main result is a kick that, to the best of
our knowledge, kicks the ball farther in this simulator than has been
previously documented.Computer Science
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