Abstract
Over the last few years, research in Artificial Intelligence has focussed on games with incomplete information and non-deterministic moves. The game of Poker is a perfect theme for studying this subject. The best known Poker variant is Texas Hold'em that combines simple rules with a huge amount of possible playing strategies. This paper is focussed on developing algorithms for performing simple online opponent modelling in Texas Hold'em Poker enabling to select the best strategy to play against each given opponent. Several autonomous agents were developed in order to simulate typical Poker player's behaviour and an observer agent was developed, capable of using simple opponent modelling techniques, in order to select the best playing strategy against each opponent. The results obtained in realistic experiments using eight distinct poker playing agents showed the usefulness of the approach. The observer agent is clearly capable of outperforming all their counterparts in all tests performed.

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Intelligence is increasingly emerging in our ambients. Evidences of this emergence are the existence of smart homes, smart vehicles, intelligent manufacturing systems and most importantly, the appearance of the concept of intelligent cities. Humans are presently surrounded by technology that is intended to increase their quality of life and simplify their daily activities. Multi-Agent Systems are an example of technology that can be used in these activities. The concept of ubiquitous computing is implicit in these technologies and can generate an invisible ambient of interactivity. This paper presents a survey and a comparative analysis of some of the research projects concerning Ambient Intelligence (AmI). The main objective of this work was to understand the current necessities, devices and the main results in the development of these projects. By analysing these projects using several evaluation criteria one of the main conclusions is that most projects do not explore the potential of human profiles in the context of ambient adaptation. Thus, this may be a very intersting research area for future work.

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In this paper we propose an audio beat tracking system, IBT, for multiple applications. The proposed system integrates an automatic monitoring and state recovery mechanism, that applies (re-)inductions of tempo and beats, on a multi-agent-based beat tracking architecture. This system sequentially processes a continuous onset detection function while propagating parallel hypotheses of tempo and beats. Beats can be predicted in a causal or in a non-causal usage mode, which makes the system suitable for diverse applications. We evaluate the performance of the system in both modes on two application scenarios: standard (using a relatively large database of audio clips) and streaming (using long audio streams made up of concatenated clips). We show experimental evidence of the usefulness of the automatic monitoring and state recovery mechanism in the streaming scenario (i.e., improvements in beat tracking accuracy and reaction time). We also show that the system performs efficiently and at a level comparable to state-of-the-art algorithms in the standard scenario. IBT is multi-platform, open-source and freely available, and it includes plugins for different popular audio analysis, synthesis and visualization platforms.