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    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
    (Massey University, 2009) Lin, Caoyun
    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 Assignment
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    Action-selection in RoboCup keepaway soccer : experimenting with player confidence : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science at Massey University
    (Massey University, 2006) Neilson, Samara Ann
    Through the investigation of collaborative multi-agent domains, in particular those of robot soccer and robot rescue, and the examination of many popular action-selection methodologies, this study identifies some of the issues surrounding entropy, action-selection and performance analysis. In order to address these issues, a meaningful method of on-field player evaluation, the confidence model, was first proposed then implemented as an action-selection policy. This model represented player skill through the use of percentages signifying relative strength and weakness and was implemented using a combination of ideas taken from Bayesian Theory. Neural Networks. Reinforcement Learning, Q-Learning and Potential Fields. Through the course of this study, the proposed confidence model action-selection methodology was thoroughly tested using the Keepaway Soccer Framework developed by Stone, Kuhlmann, Taylor and Liu and compared with the performance of its peers. Empirical test results were also presented, demonstrating both the viability and flexibility of this approach as a sound, homogeneous solution, for a team wishing to implement a quickly trainable performance analysis solution.
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    Autonomous control of a humanoid soccer robot : development of tools and strategies using colour vision : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University
    (Massey University, 2007) Rielly, Baden
    Humanoid robots research has been an ongoing area of development for researchers due to the benefits that humanoid robots present, whether for entertainment or industrial purposes because of their ability to move around in a human environment, mimic human movement and being aesthetically pleasing. The RoboCup is a competition designed to further the development of robotics, with the humanoid league being the forefront of the competition. A design for the robot platform to compete at an international level in the RoboCup competition will be developed. Along with the platform, tools are created to allow the robot to function autonomously, effectively and efficiently in this environment, primarily using colour vision as its main sensory input. By using a 'point and follow' approach to the robot control a simplistic A.I. was formed which enables the robot to complete the basic functionality of a striker of the ball. Mathematical models are then presented for the comparison of stereoscopic versus monoscopic vision, with the expansion on why monoscopic vision was chosen, due to the environment of the competition being known. A monoscopic depth perception mathematical model and algorithm is then developed, along with a ball trajectory algorithm to allow the robot to calculate a moving balls trajectory and react according to its motion path. Finally through analysis of the implementation of the constructed tools for the chosen platform, details on their effectiveness and their drawbacks are discussed.