Pyrus Base: An Open Source Python Framework for the RoboCup 2D Soccer Simulation

Soccer, also known as football in some parts of the world, involves two teams of eleven players whose objective is to score more goals than the opposing team. To simulate this game and attract scientists from all over the world to conduct research and participate in an annual computer-based soccer world cup, Soccer Simulation 2D (SS2D) was one of the leagues initiated in the RoboCup competition. In every SS2D game, two teams of 11 players and one coach connect to the RoboCup Soccer Simulation Server and compete against each other. Over the past few years, several C++ base codes have been employed to control agents' behavior and their communication with the server. Although C++ base codes have laid the foundation for the SS2D, developing them requires an advanced level of C++ programming. C++ language complexity is a limiting disadvantage of C++ base codes for all users, especially for beginners. To conquer the challenges of C++ base codes and provide a powerful baseline for developing machine learning concepts, we introduce Pyrus, the first Python base code for SS2D. Pyrus is developed to encourage researchers to efficiently develop their ideas and integrate machine learning algorithms into their teams. Pyrus base is open-source code, and it is publicly available under MIT License on GitHu

Gliders2d: Source Code Base for RoboCup 2D Soccer Simulation League

We describe Gliders2d, a base code release for Gliders, a soccer simulation team which won the RoboCup Soccer 2D Simulation League in 2016. We trace six evolutionary steps, each of which is encapsulated in a sequential change of the released code, from v1.1 to v1.6, starting from agent2d-3.1.1 (set as the baseline v1.0). These changes improve performance by adjusting the agents' stamina management, their pressing behaviour and the action-selection mechanism, as well as their positional choice in both attack and defense, and enabling riskier passes. The resultant behaviour, which is sufficiently generic to be applicable to physical robot teams, increases the players' mobility and achieves a better control of the field. The last presented version, Gliders2d-v1.6, approaches the strength of Gliders2013, and outperforms agent2d-3.1.1 by four goals per game on average. The sequential improvements demonstrate how the methodology of human-based evolutionary computation can markedly boost the overall performance with even a small number of controlled steps.Comment: 12 pages, 1 figure, Gliders2d code releas

OMBO: An opponent modeling approach

In competitive domains, some knowledge about the opponent can give players a clear advantage. This idea led many people to propose approaches that automatically acquire models of opponents, based only on the observation of their input–output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the behavior of the opponent. However, that is not the case in the RoboCup domain where an agent does not have direct access to the opponent inputs and outputs. Rather, the agent sees the opponent behavior from its own point of view and inputs and outputs (actions) have to be inferred from observation. In this paper, we present an approach to model low-level behavior of individual opponent agents. First, we build a classifier to infer and label opponent actions based on observation. Second, our agent observes an opponent and labels its actions using the previous classifier. From these observations, machine learning techniques generate a model that predicts the opponent actions. Finally, the agent uses the model to anticipate opponent actions. In order to test our ideas, we have created an architecture called OMBO (Opponent Modeling Based on Observation). Using OMBO, a striker agent can anticipate goalie actions. Results show that in this striker-goalie scenario, scores are significantly higher using the acquired opponent's model of actions.This work has been partially supported by the Spanish MCyT under projects TRA2007-67374- C02-02 and TIN-2005-08818-C04.Also, it has been supported under MEC grant by TIN2005-08945- C06-05. We thank anonymous reviewers for their helpful comments.Publicad

A Hybrid Multi-Robot Control Architecture

Multi-robot systems provide system redundancy and enhanced capability versus single robot systems. Implementations of these systems are varied, each with specific design approaches geared towards an application domain. Some traditional single robot control architectures have been expanded for multi-robot systems, but these expansions predominantly focus on the addition of communication capabilities. Both design approaches are application specific and limit the generalizability of the system. This work presents a redesign of a common single robot architecture in order to provide a more sophisticated multi-robot system. The single robot architecture chosen for application is the Three Layer Architecture (TLA). The primary strength of TLA is in the ability to perform both reactive and deliberative decision making, enabling the robot to be both sophisticated and perform well in stochastic environments. The redesign of this architecture includes incorporation of the Unified Behavior Framework (UBF) into the controller layer and an addition of a sequencer-like layer (called a Coordinator) to accommodate the multi-robot system. These combine to provide a robust, independent, and taskable individual architecture along with improved cooperation and collaboration capabilities, in turn reducing communication overhead versus many traditional approaches. This multi-robot systems architecture is demonstrated on the RoboCup Soccer Simulator showing its ability to perform well in a dynamic environment where communication constraints are high

Comparing behavior in agent modelling task

Proceeding of: IADIS International Conference Applied Computing 2006. February 25-28, 2006, San Sebastian, Spain.Reprint from a paper published in the Proceedings of the IADIS International Conference AC 2006In multi-agent system, agents have to analyze several features in order to adapt their behavior to the current situation. This extracted information is usually related to the environment and other agents influence. In this paper we present a method that compare two different agent models in order to extract the qualitative differences between them. This proposed comparative method captures several features of the two agent models and model them considering its behavior.Publicad