A comparing method of two team behaviours in the simulation coach competition

Proceeding of: Third International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2006, Tarragona, Spain, April 3-5, 2006.The main goal of agent modelling is to extract and represent the knowledge about the behaviour of other agents. Nowadays, modelling an agent in multi-agent systems is increasingly becoming more complex and significant. Also, robotic soccer domain is an interesting environment where agent modelling can be used. In this paper, we present an approach to classify and compare the behaviour of a multi-agent system using a coach in the soccer simulation domain of the RoboCup.Publicad

A Comparing Method of Two Team Behaviours in the Simulation Coach Competition

Proceeding of: Third International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2006, Tarragona, Spain, April 3-5, 2006.The main goal of agent modelling is to extract and represent the knowledge about the behaviour of other agents. Nowadays, modelling an agent in multi-agent systems is increasingly becoming more complex and significant. Also, robotic soccer domain is an interesting environment where agent modelling can be used. In this paper, we present an approach to classify and compare the behaviour of a multi-agent system using a coach in the soccer simulation domain of the RoboCup.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

CAOS Coach 2006 Simulation Team: an opponent modelling approach

Agent technology l'epresents a vel'Y intel'esting 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 Cornpetition is an exciting competition in the RoboCup Soccel' League and its main goal is to encourage reseal'ch in multiagent modelling. This papel' describes a novel method used by the team CAOS (CAOS Coach 2006 Simulation Tearn) in this competition. The objective of the team is to model successfully the behaviour of a multi-agent system.This work has been supported by the Spanish Ministry of Education and Science under project TRA-2007-67374-C02-02

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

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

Classifying efficiently the behavior of a Soccer team

Proceeding of: 10th International Conference on Intelligent Autonomous Systems (IAS 2008), Baden Baden, Germany, July 23-25th, 2008.In order to make a good decision, humans usually try to predict the behavior of others. By this prediction, many different tasks can be performed, such as to coordinate with them, to assist them or to predict their future behavior. In competitive domains, to recognize the behavior of the opponent can be very advantageous. In this paper, an approach for creating automatically the model of the behavior of a soccer team is presented. This approach is an effective and notable improvement of a previous work. As the actions performed by a soccer team are sequential, this sequentiality should be considered in the modeling process. Therefore, the observations of a soccer team in a dynamic, complex and continuous multi-variate world state are transformed into a sequence of atomic behaviors. Then, this sequence is analyzed in order to find out a model that defines the team behavior. Finally, the classification of an observed team is done by using a statistical test.This work has been supported by the Spanish Ministry of Education and Science under project TRA-2007-67374-C02-02.Publicad

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Exploiting opponent behavior in multi-agent systems

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201