PENERAPAN PREDIKDI BOLA PADA STRATEGI PENYERANGAN ROBOT SEPAKBOLA UPN VETERAN YOGYAKARTA

Strategi menyerang merupakan dasar strategi dalam robot sepak bola. Keberhasilan suatu tim robot sepak bola dipengaruhi oleh keberhasilan pemain robot untuk memasukkan bola ke dalam gawang lawan. Strategi menyerangan merupakan faktor utama penentu keberhasilan tim robot sepak bola untuk memenangkan setiap pertandingan. Dalam penelitian ini kami akan membuat perancangan strategi penyerangan yang dipergunakan dan diimplementasikan pada robot MiroSot UPN V Yogyakarta. Pada penelitian menghasilkan fungsi membawa bola ke gawang dan fungsi prediksi bola. Dalam fungsi penyerangan terdiri dari beberapa tahapan yaitu pergi ke belakang bola setelah robot berada di belakang bola maka tahapan selanjutnya yaitu mengahadapkan robot ke arah gawang. Tahap terakhir yaitu membawa bola ke gawang. Hasil pengujian pada Robot MiroSot UPN “Veteran” Yogyakarta adalah 53,3%. Hasil pengujian menggunakan robot yang telah dilengkapi dengan sensor gyroscope adalah 63,3%. Hasil pengujian fungsi prediksi bola adalah 15,55 % sedangkan kemampuan robot untuk memotong bola sebesar 14,44%

Application of Fuzzy State Aggregation and Policy Hill Climbing to Multi-Agent Systems in Stochastic Environments

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually even as the operating environment changes. Applying this learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF). The combination of fast policy hill climbing (PHC) and fuzzy state aggregation (FSA) function approximation is tested in two stochastic environments; Tileworld and the robot soccer domain, RoboCup. The Tileworld results demonstrate that a single agent using the combination of FSA and PHC learns quicker and performs better than combined fuzzy state aggregation and Q-learning lone. Results from the RoboCup domain again illustrate that the policy hill climbing algorithms perform better than Q-learning alone in a multi-agent environment. The learning is further enhanced by allowing the agents to share their experience through a weighted strategy sharing

Abstracting Multidimensional Concepts for Multilevel Decision Making in Multirobot Systems

Multirobot control architectures often require robotic tasks to be well defined before allocation. In complex missions, it is often difficult to decompose an objective into a set of well defined tasks; human operators generate a simplified representation based on experience and estimation. The result is a set of robot roles, which are not best suited to accomplishing those objectives. This thesis presents an alternative approach to generating multirobot control algorithms using task abstraction. By carefully analysing data recorded from similar systems a multidimensional and multilevel representation of the mission can be abstracted, which can be subsequently converted into a robotic controller. This work, which focuses on the control of a team of robots to play the complex game of football, is divided into three sections: In the first section we investigate the use of spatial structures in team games. Experimental results show that cooperative teams beat groups of individuals when competing for space and that controlling space is important in the game of robot football. In the second section, we generate a multilevel representation of robot football based on spatial structures measured in recorded matches. By differentiating between spatial configurations appearing in desirable and undesirable situations, we can abstract a strategy composed of the more desirable structures. In the third section, five partial strategies are generated, based on the abstracted structures, and a suitable controller is devised. A set of experiments shows the success of the method in reproducing those key structures in a multirobot system. Finally, we compile our methods into a formal architecture for task abstraction and control. The thesis concludes that generating multirobot control algorithms using task abstraction is appropriate for problems which are complex, weakly-defined, multilevel, dynamic, competitive, unpredictable, and which display emergent properties

Soccer Team based on Agent-Oriented Programming

In this paper the analysis, design and implementation of a soccer team of micro-robots is explained. Besides the technical difficulties to develop these micro-robots, this paper also shows how to develope a multi-agent co-operative system by means of Matlab/Simulink+ a widely known Computer Aided Control System Design framework. Agent-Oriented Paradigms formalise interactions between multiple agents in terms of changing their mental states by communication between agents. Their practical implementations are usually conceived by means of Object-Oriented Paradigms. Nevertheless, the implementation of Agent-Oriented Paradigms in Matlab/Simulink is not straightforward. Thus, the obtained real implementation is an integrated system that includes several programming paradigms so as hardware platforms. Finally, the proposal of the integrated framework for the micro-robots soccer team is shown. 1. INTRODUCTION Multi-agent based mobile robotics require new examples from application and call fo..

Soccer team based on agent-oriented programming

In this paper the analysis, design and implementation of a soccer team of micro-robots is explained. Besides the technical difficulties to develop these micro-robots, this paper also shows how to develop a multi-agent co-operative system by means of Matlab/Simulink (MATLAB and SIMULINK are Trade Marks of Math Works. Windows 95 is a Trade Mark of Microsoft Corporation.) a widely known computer aided control system design framework. Agent-oriented paradigms (AOPs) formalise interactions between multiple agents in terms of changing their mental states by communication between agents. Their practical implementations are usually conceived by means of object-oriented paradigms. Nevertheless, the implementation of agent-oriented paradigms in Matlab/Simulink is not straightforward. Thus, the obtained real implementation is an integrated system that includes several programming paradigms so as hardware platforms. Finally, the proposal of the integrated framework for the micro-robots soccer team is shown.This work has been partially funded by the TAP96-1114-C03-03 project Plataformas Integradas de CAD de Supervisión y Metodologías of CICYT program from the Spanish government.Peer Reviewe