Abstract
Strategic planning and multi-agent coordination are major research topics in the domain of RoboCup. Research was, in the first years, directed towards development of low level skills and positional co-ordination. The competitive level has in between risen to new standards, which makes the development of high-level co-operation necessary. The importance of the concept of Setplay, i.e., small multi-robot plans to deal with particular situations, to structure a robotic soccer team behaviour, has been acknowledged by many researchers, but no general framework for the development and execution of generic Setplays has been introduced in the context of RoboCup. This paper presents such a framework for high-level Setplay definition and execution in the 2D simulation league, though applicable to any RoboCup co-operative league and similar domains. The framework is built upon a standard, flexible and league-independent language, which defines Setplays that are interpreted and executed at run-time, using inter-robot communication. A major step in the development of the Setplay framework is its usage and testing in the scope of the FCPortugal team, which participates in the RoboCup 2D-simulation league, where it won several titles. After this successful implementation, described in this paper, the framework will be used in the mid-size league, and possibly in other new environments. Recent developments have made it possible to use Setplays in play-on situations, which had not been possible before. Also, a graphical tool for Setplay definition has been developed, and used in the context of this team. ©2010 IEEE.

Abstract
The most common cause of traffic accidents is arguably the driver error due to lack of attention. And it is very unlikely this is going to change soon thanks to increasingly cell-phone usage, in-car entertainment systems, and naturally the more frequent traffic jams in highly populated areas. Autonomous vehicles, such as driverless cars, are a promising approach to decrease traffic accidents, as well as congestions. To test this approach, simulations are a safer, more efficient, and cheaper way than live testing. This paper presents an approach to implement a simulator to test such vehicles. It includes a study of the state of the art in driverless car simulation and discusses on the specific objectives that this particular simulator aims to achieve in order to aid testing the interactions of multiple driverless cars in urban networks.

Abstract
In this paper we propose the integration of an online audio beat tracking system into the general framework of robot audition, to enable its application in musically-interactive robotic scenarios. To this purpose, we introduced a state-recovery mechanism into our beat tracking algorithm, for handling continuous musical stimuli, and applied different multi-channel preprocessing algorithms (e. g., beamforming, ego noise suppression) to enhance noisy auditory signals lively captured in a real environment. We assessed and compared the robustness of our audio beat tracker through a set of experimental setups, under different live acoustic conditions of incremental complexity. These included the presence of continuous musical stimuli, built of a set of concatenated musical pieces; the presence of noises of different natures (e. g., robot motion, speech); and the simultaneous processing of different audio sources on-the-fly, for music and speech. We successfully tackled all these challenging acoustic conditions and improved the beat tracking accuracy and reaction time to music transitions while simultaneously achieving robust automatic speech recognition.

Abstract
In a soccer match, a cooperative behavior emerges from the combined execution of simple actions by players. A cooperative behavior can be planned if players are previously committed to its execution prior to its start or unplanned otherwise. The ability to reproduce some of these behaviors can be useful to help a team achieve better performances. This work presents an approach to identify and extract cooperative behaviors that start from set-pieces and lead to a goal while ball possession is kept. The representation of these behaviors is abstracted using a set-play definition language to promote their reusability. A set of game log files generated with the FC Portugal team and collected from the RoboCup 2010 2D simulated soccer competition were analyzed. The results achieved showed that 25% of the total goals scored originated from set-pieces which attests to the importance of performing this analysis. Several guidelines for the definition of future set-plays were also inferred. In the future, these behaviors shall be tested to infer which are capable of neutralizing an opponent's team strategy and maximize the creation of goal opportunities.

Abstract
This paper presents the architecture that supports the collaborative model ACEM (Advanced Collaborative Educational Model) to assist educators in the collaborative design of learning activities, supported by a high-level graphical tool. ACEM embraces the research areas of Computer Supported Cooperative Work (CSCW) and Learning Design (LD). Some facilities are considered in order to implement the online interactions between educators, namely a shared whiteboard and a conversation room. A workflow descriptive model of the educators' teamwork is also introduced.

Abstract
Brain Computer Interfaces (BCI) enables interaction between users and hardware systems, through the recognition of brainwave activity. However, the current BCI systems still have a very low accuracy on the recognition of facial expressions and thoughts. This makes it very difficult to use these devices to enable safe and robust commands of complex devices such as an Intelligent Wheelchair. This paper presents an approach to expand the use of a brain computer interface for driving an intelligent wheelchair by patients suffering from cerebral palsy. The approach was based on appropriate signal preprocessing based on Hjorth parameters, a forward approach for variable selection and several data mining algorithms for classification such as naive Bayes, neural networks and support vector machines. Experiments were performed using 30 individuals suffering from IV and V degrees of cerebral palsy on the Gross Motor Function (GMF) measure. The results achieved showed that the preprocessing and variable selection methods were effective enabling to improve the results of a commercial BCI product by 57%. With the developed system it was also possible for users to perform a circuit in a simulated environment using just facial expressions and thoughts.