Abstract
The development of competitive artificial Poker players is a challenge to Artificial Intelligence (AI) because the agent must deal with unreliable information and deception which make it essential to model the opponents to achieve good results. In this paper we propose the creation of an artificial Poker player through the analysis of past games between human players, with money involved. To accomplish this goal, we defined a classification problem that associates a given game state with the action that was performed by the player. To validate and test the defined player model, an agent that follows the learned tactic was created. The agent approximately follows the tactics from the human players, thus validating this model. However, this approach alone is insufficient to create a competitive agent, as generated strategies are static, meaning that they can't adapt to different situations. To solve this problem, we created an agent that uses a strategy that combines several tactics from different players. By using the combined strategy, the agentgreatly improved its performance against adversaries capable of modeling opponents.

Abstract
Researching into the incomplete information games (IIG) field requires the development of strategies which focus on optimizing the decision making process, as there is no unequivocal best choice for a particular play. As such, this paper describes the development process and testing of an agent able to compete against human players on Poker - one of the most popular IIG. The used methodology combines pre-defined opponent models with a reinforcement learning approach. The decision-making algorithm creates a different strategy against each type of opponent by identifying the opponent's type and adjusting the rewards of the actions of the corresponding strategy. The opponent models are simple classifications used by Poker experts. Thus, each strategy is constantly adapted throughout the games, continuously improving the agent's performance. In light of this, two agents with the same structure but different rewarding conditions were developed and tested against other agents and each other. The test results indicated that after a training phase the developed strategy is capable of outperforming basic/intermediate playing strategies thus validating this approach. © 2012 Springer-Verlag.

Abstract
Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. This paper presents a comparative study of four classification algorithms for static hand gesture classification using two different hand features data sets. The approach used consists in identifying hand pixels in each frame, extract features and use those features to recognize a specific hand pose. The results obtained proved that the ANN had a very good performance and that the feature selection and data preparation is an important phase in the all process, when using low-resolution images like the ones obtained with the camera in the current work.

Abstract
Many of the physically injured use electric wheelchairs as an aid to locomotion. Usually, for commanding this type of wheelchair, it is required the use of one's hands and this poses a problem to those who, besides being unable to use their. legs, are also unable to properly use their hands. The aim of the work described here, is to create a prototype of a wheelchair command interface that do not require hand usage. Facial expressions. were chosen instead, to provide the necessary visual information for the interface to recognize user commands. The facial expressions are captured by means of a digital camera and interpreted by an application running on a laptop computer on the wheelchair. The software includes digital image processing algorithms for feature detection, such as colour segmentation and edge detection, followed by the application of a neural network that uses these features to detect the desired facial expressions. The results obtained from the framework interface provide strong evidence that it is possible to comfortably drive an intelligent wheelchair using facial expressions.

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