Abstract
The usage of data mining models has the main purpose of discovering new patterns from dataset analysis by extracting knowledge from data and converting it to information. The most challenging part of problem solving is not the generation of high number of instances in dataset, most often hard to understand, but the interpretation of all those instances to extrapolate information about it. Simulation of coastal ecosystems is used to replicate some real conditions related with physical, chemical and biological processes, and produces large datasets from which it could be deduced some information about attributes behaviors. This paper relates the use of Decision Tree models to analyze the growth of bivalve species in an ecosystem simulation. With a set of attributes that represents the water quality in certain modeled regions, the usage of Decision Tree is intended to identify the most significant attribute conditions, which could justify the growth behavior for each analyzed species. This approach aims the creation of new information about how water conditions should be to promote a healthy and fast growth of the analyzed species, being useful to know in which zones the bivalve should be seeded, and which are the conditions that aquaculture producers should afford to benefit the quality of its crops.

Abstract
This paper presents the architecture and algorithms developed for Dom Dinis, a simple compass-guided robot built by the authors. This includes environment exploration, task planning and task execution. Environment exploration, based on repeating a reactive goal search, enables a progressive construction of a grid-based map. Based on the (possibly incomplete) map, the robot is able to plan its tasks. The execution capabilities of the robot include exception handling. Essential to all these capabilities is the knowledge of the robot's position in the world. The position is computed based on tracking traversed distances and followed orientations. Orientation is given by a compass. Dom Dinis doesn't use wheel encoders at all.

Abstract

Abstract
Intelligent Wheelchair (IW) is a new concept aiming to allow higher autonomy to people with lower mobility such as disabled or elderly individuals. Some of the more recent IWs have a multimodal interface, enabling multiple command modes such as joystick, voice commands, head movements, or even facial expressions. In these IW it may be very useful to provide the user with the best way of driving it through an adaptive interface. This paper describes the foundations for creating a simple methodology for extracting user profiles, which can be used to adequately select the best IW command mode for each user. The methodology is based on an interactive wizard composed by a flexible set of simple tasks presented to the user, and a method for extracting and analyzing the user's execution of those tasks. The results achieved showed that it is possible to extract simple user profiles, using the proposed method. Thus, the approach may be further used to extract more complete user profiles, just by extending the set of tasks used, enabling the adaptation of the IW interface to each user's characteristics.

Abstract
RoboCup is an international cooperative research project aimed at promoting research in Artificial Intelligence and Robotics. It includes a simulation league where two teams of 11 players compete in a robotic soccer game very similar to real soccer. Teams exhibit very complex strategies in these games that are very difficult to analyze by conventional observation methods. This paper presents an approach to the visualization of simulated robotic soccer games using the RapidMiner software package. Various visualizations were developed using Andrew's Curves, Survey Plots, several types of Parallel Coordinate visualizations and Radial Coordinate Visualizations. These visualizations enabled to take some interesting conclusions about the differences between games of FC Portugal robotic soccer team using different formations and against distinct opponents.

Abstract
In soccer, the level of performance is determined by a number of a complex variables interrelated: technique, tactics, psychological factors and finally, fitness. Because of this, analyzing this information in a real-time, even for soccer experts like professional coaches has become an impossible task. Automatic event detection tools occupy an important role in this reality, although nowadays there isn't any tool capable of producing information capable of helping a professional coach choosing his team strategy for a specific game. In this research project an automatic event detection tool is purposed and, a set of game statistics defined by a group of sports researchers. All the teams present in the 2009 RoboCup tournament have a pass success rate superior to 65%. These statistics provide an interesting viewpoint on how to evaluate a team performance, such as the importance of dominating the opposing team field without losing the control of our own (this can be seen on the top 3 zone dominance statistics). In the future this project will serve as a base for building a Framework capable of simulating a match between two heterogeneous soccer teams and produce reliable information for optimizing the team performance.