Abstract
The application of optimization algorithms to parameter driven simulations and agents has been thoroughly explored in literature. However, classical optimization algorithms do not take into account the fact that simulations normally have dynamic scenarios. This paper analyzes the possibility of using the classical optimization methods, combined with clustering techniques, in order to optimize parameter driven agents, in simulations having dynamic scenarios. This will be accomplished by optimizing the agents in several random static scenarios and clustering the optimum results of each of these optimizations in order to find a set of typical solutions for the agent parametrization problem. These typical solutions can then be used in dynamic scenario simulations as references that will help the agents adapt to scenario changes. The results of this approach show that, in some cases, it is possible to improve the outcome of simulations in dynamic environments while still using the classical methods developed for static scenarios.

Abstract
In this paper we describe our work on computational mind model for temperamental decision algorithms using Fuzzy Logic and an implementation of an emotional-behavioral multi-agent system for analysis and evaluation of different strategies based on temperamental behaviors. We describe our approach to emotional model using temperamental decision system based on theory about general types of superior nervous systems in humans and animals and we explain how we can apply Fuzzy Logic on temperamental decision system. We describe the simulation environment used in this work to test and evaluate the strategies. We have conducted a set of robotic experiments in order to test the performance of the system on its first implementation phase. The results achieved showed that the different set of temperamental characteristics influences significantly the performance of the agents and give us very positive feedback to proceed with this research and implementation in new directions.

Abstract
Multi-agent systems (MAS) are a research topic with ever-increasing importance. This is due to their inherently distributed organization that copes more naturally with real-life problems whose solution requires people to coordinate efforts. One of its most prominent challenges consists on the creation of efficient coordination methodologies to enable the harmonious operation of teams of agents in adversarial environments. This challenge has been promoted by the Robot World Cup (RoboCup) international initiative every year since 1995. RoboCup provides a pragmatic testbed based on standardized platforms for the systematic evaluation of developed MAS coordination techniques. This initiative encompasses a simulated robotic soccer league in which 11 against 11 simulated robots play a realistic soccer game that is particularly suited for researching coordination methodologies. This paper presents a comprehensive overview of the most relevant coordination techniques proposed up till now in the simulated robotic soccer domain.

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This article describes the use of learning design to contextualise learning resources in a repository through the use of pre-defined templates. We advocate a close connection between a resource and its pattern of use in order to improve learning objects reuse. Moreover, we present the rationale for adopting and using learning design templates in the TREE repository.