Abstract
In recent years the game of poker has created a high interest on researchers from the artificial intelligence area. Unlike board games, poker is an incomplete information game becoming a very complex game for a virtual agent. The main objective of this work is to create a data model enabling to apply data mining techniques to obtain a poker player model (pre-flop stage). To do that we used a database from a professional poker player where the data is stored in text files. The work used CRISP-DM, performing its stages. As ETL (Extract, Transform and Load) tool Talend was used and for running the data mining techniques Weka was used. As a final result, a player model was achieved with a very good ROC curve. This result, enable us to conclude that the approach is adequate for creating complete poker player models.

Abstract
The Setplay Framework (available from SourceForge as free software) is composed of a C++ library (Project name: fcportugalsetplays), a fully functional RoboCup Simulation 2D demonstration team (fcportugalsetplaysagent2d), and a complete graphical tool (SPlanner), that can be used to design and plan the collaborative behavior between the soccer player agents. In order to demonstrate the usage of the Setplay library, a complete 2D simulation team, based on Agent2D, was developed. This example team uses the framework to execute previously planned collaborative behavior. This framework can be used both within simulated environments, such as the Robocup Soccer Simulation leagues, and with real soccer playing robots. This paper presents the free software Setplay Framework, and provides the necessary information for any team to use the framework with the goal of providing collaborative behavior to a team of soccer playing robots.

Abstract
Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for real-time human-machine interaction. Its novelty is the integration of different tools for gesture spotting and the proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained with a centroid distance dataset composed of 2170 records, able to achieve a final accuracy of 99.4 %. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each one of the defined gestures that the system should recognize with a final average accuracy of 93.7 %. The datasets were built from four different users with a total of 25 gestures per user, totalling 1100 records for model construction. The proposed solution has the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real-time.

Abstract
Rehabilitation processes follow, as a rule, intensive training programs, composed of repetitive and monotonous tasks for patients. The introduction of serious games in these processes can help in motivating patients, increasing their interest in the exercises to perform. These games should be set taking into account characteristics that are able to stimulate and train cognitive functions as well as features that require some kind of motor activity in order to stimulate and attract patients' attention. This article presents a literature review on serious games for cognitive rehabilitation and a set of characteristics that are considered important for serious game development in this area which include the use of natural and multimodal interfaces and a social dimension such as collaboration, competition and the concept of handicap.

Abstract
The linear inverted pendulum model has been used predominantly to generate balanced humanoid walking. This model assumes that the hip height is fixed during the walk. In this paper, generating a fast walk is studied with the main focus on the effect of hip height movement. Our approach is based on modeling the hip height movement and learning its parameters in order to generate a fast walk. The hip height trajectory is generated using Fourier basis functions. The generated trajectory is the input to programmable Central Pattern Generators (CPGs) in order to modulate generated trajectories smoothly. The inverted pendulum model is utilized to model a balanced walking. A numerical approach is presented to control inverted pendulum dynamics. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is employed to search for appropriate hip height trajectory and walking parameters that optimize walking speed. This approach has been tested not only to obtain fast forward walk but also a fast side walk. Experiments are conducted on both simulated and real NAO robots. The results show that the change from the learned forward walk to learned side walk is performed stably, which confirm the important role of using CPGs. The comparison of the results of the proposed gait model (and development approach) with those obtained using fixed hip height also shows that fixed height walking is slower than variable height walking.

Abstract
Diplomacy is a multi-player strategic and zero-sum board game, free of random factors, and allowing negotiation among players. The majority of existing artificial players (bots) for Diplomacy do not exploit the strategic opportunities enabled by negotiation, instead trying to decide their moves through solution search and the use of complex heuristics. We present DipBlue, an approach to the development of an artificial player that uses negotiation in order to gain advantage over its opponents, through the use of peace treaties, formation of alliances and suggestion of actions to allies. A simple trust assessment approach is used as a means to detect and react to potential betrayals by allied players. DipBlue was built to work with DipGame, a multi-agent systems testbed for Diplomacy, and has been tested with other players of the same platform and variations of itself. Experimental results show that the use of negotiation increases the performance of bots involved in alliances, when full trust is assumed. In the presence of betrayals, being able to perform trust reasoning is an effective approach to reduce their impact. Copyright