Caos Online Coach 2006 Team Description

This paper describes the main features of the Caos Coach 2006 Simulation Team. This Coach focuses on the challenge of the opponent modelling using sequential events of the players, from observations of their main features. Also, it is able to translate observations of a dynamic and complex environment into a time-serie of recognized events. Finally, our coach implements a mechanism to compare different time-series.No publicad

CAOS Coach 2006 Simulation Team: An Opponent Modelling Approach

Agent technology represents a very interesting new means for analyzing, designing and building complex software systems. Nowadays, agent modelling in multi-agent systems is increasingly becoming more complex and significant. RoboCup Coach Competition is an exciting competition in the RoboCup Soccer League and its main goal is to encourage research in multii-agent modelling. This paper describes a novel method used by the team CAOS (CAOS Coach 2006 Simulation Team) in this competition. The objective of the team is to model successfully the behaviour of a multi-agent system

Desarrollo e implementación de una herramienta de análisis de secuencias de acciones

Este documento presenta el Trabajo de Fin de Grado que he realizado en la Universidad Carlos III de Madrid, en la Escuela Politécnica de Leganés. El proyecto que se ha desarrollado tiene como objetivo la automatización de la creación de una estructura arbórea (trie) que permite almacenar y analizar secuencias de acciones en dominio de datos secuenciales. En el documento se describe toda la realización y estructura del software implementado, así como la experimentación que se ha realizado como demostración del funcionamiento del mismo. Gracias a este software se permite de forma automática crear tries y analizarlos, con un enfoque gráfico y estadístico. La utilización del trie como estructura de representación de datos permite utilizar un enfoque estadístico para el análisis de secuencias de datos y la extracción de patrones. El software diseñado es genérico, por lo que puede ser utilizado en cualquier dominio, facilitando enormemente la labor de los investigadores.This document presents the Final Degree Work that I have done for the University Carlos III de Madrid, in the Polytechnic School of Leganés. The developed project is aimed to the automatic creation of a tree structure (trie) that allows to store and to analyze sequences of actions in a sequential data domain. In the document is described all of the work done and the structure of the implemented software, as well as the performed experimentation as demonstration of the functionality of the software system. Thanks to this software, automatic creation and analysis of tries are done with a statistical and graphical approach. The usage of tries as structure to represent data allows to create a statistical approach to analyze data sequences and to perform pattern recognition. The design software is generic, so it can be used in any domain, facilitating the work of researchers.Ingeniería Informátic

Hypernetworks Analysis of RoboCup Interactions

Robotic soccer simulations are controlled environments in which the rich variety of interactions among agents make them good candidates to be studied as complex adaptive systems. The challenge is to create an autonomous team of soccer agents that can adapt and improve its behaviour as it plays other teams. By analogy with chess, the movements of the soccer agents and the ball form ever-changing networks as players in one team form structures that give their team an advantage. For example, the Defender’s Dilemma involves relationships between an attacker with the ball, a team-mate and a defender. The defender must choose between tackling the player with the ball, or taking a position to intercept a pass to the other attacker. Since these structures involve more that two interacting entities it is necessary to go beyond networks to multidimensional hypernetworks. In this context, this thesis investigates (i) is it possible to identify patterns of play, that lead a team to obtain an advantage ?, (ii) is it possible to forecast with a good degree of accuracy if a certain game action or sequence of game actions is going to be successful, before it has been completed ?, and (iii) is it possible to make behavioural patterns emerge in the game without specifying the behavioural rules in detail ? To investigate these research questions we devised two methods to analyse the interactions between robotic players, one based on traditional programming and one based on Deep Learning. The first method identified thousands of Defender’s Dilemma configurations from RoboCup 2D simulator games and found a statistically significant association between winning and the creation of the defender’s dilemma by the attackers of the winning team. The second method showed that a feedforward Artificial Neural Network trained on thousands of games can take as input the current game configuration and forecast to a high degree of accuracy if the current action will end up in a goal or not. Finally, we designed our own fast and simple robotic soccer simulator for investigating Reinforcement Learning. This showed that Reinforcement Learning using Proximal Policy Optimization could train two agents in the task of scoring a goal, using only basic actions without using pre-built hand-programmed skills. These experiments provide evidence that it is possible: to identify advantageous patterns of play; to forecast if an action or sequence of actions will be successful; and to make behavioural patterns emerge in the game without specifying the behavioural rules in detail

Addressing the Issues of Coalitions and Collusion in Multiagent Systems

In the field of multiagent systems, trust and reputation systems are intended to assist agents in finding trustworthy partners with whom to interact. Earlier work of ours identified in theory a number of security vulnerabilities in trust and reputation systems, weaknesses that might be exploited by malicious agents to bypass the protections offered by such systems. In this work, we begin by developing the TREET testbed, a simulation platform that allows for extensive evaluation and flexible experimentation with trust and reputation technologies. We use this testbed to experimentally validate the practicality and gravity of attacks against vulnerabilities. Of particular interest are attacks that are collusive in nature: groups of agents (coalitions) working together to improve their expected rewards. But the issue of coalitions is not unique to trust and reputation; rather, it cuts across a range of fields in multiagent systems and beyond. In some scenarios, coalitions may be unwanted or forbidden; in others they may be benign or even desirable. In this document, we propose a method for detecting coalitions and identifying coalition members, a capability that is likely to be valuable in many of the diverse fields where coalitions may be of interest. Our method makes use of clustering in benefit space (a high-dimensional space reflecting how agents benefit others in the system) in order to identify groups of agents who benefit similar sets of agents. A statistical technique is then used to identify which clusters contain coalitions. Experimentation using the TREET platform verifies the effectiveness of this approach. A series of enhancements to our method are also introduced, which improve the accuracy and robustness of the algorithm. To demonstrate how this broadly-applicable tool can be used to address domain-specific problems, we focus again on trust and reputation systems. We show how, by incorporating our work into one such system (the existing Beta Reputation System), we can provide resistance to collusion. We conclude with a detailed discussion of the value of our work for a wide range of environments, including a variety of multiagent systems and real-world settings

Learning the Sequential Coordinated Behavior of Teams from Observations

The area of agent modeling deals with the task of observing other agents and modeling their behavior, in order to predict their future behavior, coordinate with them, assist them, or counter their actions