WrightEagle 2D soccer simulation team has been participating in annual RoboCup competitions since 1999 and won 4 champions and 5 runners-up at RoboCup world championships in the past 9 years. In this paper, we briefly present our current research efforts and some newly introduced techniques since the last year competition.

WrightEagle 2D soccer simulation team has been participating in annual RoboCup competitions since 1999 and won 5 champions and 5 runners-up at RoboCup world championships in the past 10 years. In this paper, we briefly present our current research efforts and some newly introduced techniques since the last year competition.

 This document describes a simulated robotic soccer team (GPR-2D). The team is implemented using an Artificial Intelligence (AI) strategy to modify the behavior of players of one team of soccer robots of the RoboCup 2D simulation category. This strategy used is a Reinforcement Learning (RL) algorithm, Q-learning. This algorithm define how each agent should act according to the characteristics ...

In this description document, we outline the main ideas behind our entry to the RoboCup 2011 2D Soccer Simulation League competition. This simulated soccer team, EdInferno.2D, represents our debut in this league. The major research issue that drives our effort is that of cooperative sequential decision making and online learning in environments that continually change. We outline a framework fo...

We summarise the results of RoboCup 2D Soccer Simulation League in 2016 (Leipzig), including the main competition and the evaluation round. The evaluation round held in Leipzig confirmed the strength of RoboCup-2015 champion (WrightEagle, i.e. WE2015) in the League, with only eventual finalists of 2016 competition capable of defeating WE2015. An extended, post-Leipzig, round-robin tournament wh...

Un método simple para la toma de decisiones en ambientes 3D de simulación de fútbol RoboCup Abstract—In th is p ap er n ew hier ar ch ical hybr id fuzzy­cr isp methods for decision making and action selection of an agent in soccer simulation 3D environment are presented. First, the skills of an agent are introduced, imp lemented and classified in two layers, the basic­skills and the high­level ...

The RoboCup soccer simulation 2D domain is a very large testbed for the research of planning and machine learning. It has competed in the annual world championship tournaments in the past 15 years. However it is still unclear that whether more principled techniques such as decision-theoretic planning take an important role in the success for a RoboCup 2D team. In this paper, we present a novel ...

RoboCup offers a set of benchmark problems for Artificial Intelligence in form of official world championships since 1997. The most tactical advanced and richest in terms of behavioural complexity of these is the 2D Soccer Simulation League, a simulated robotic soccer competition. BetaRun is a new attempt combining both machine learning and manual programming approaches, with the ultimate goal ...

Despite significant progress in the development of virtual soccer games, the affective behavior of virtual soccer players, both in software and hardware applications, is still limited. To improve this situation, this paper presents a generic model for decision making of virtual agents in relation to emotions and trust. The model takes the BDI framework as point of departure, and extends this wi...

HELIOS2014 is a 2D soccer simulation team which has been participating in the RoboCup competition since 2000. We recently focus on an online multiagent planning using tree search methodology. This paper describes the overview of our search framework and an evaluation method to select the best action sequence.