Abstract
The game of Poker is an excellent test bed for studying opponent modeling methodologies applied to non-deterministic games with incomplete information. The most known Poker variant, Texas Hold'em Poker, combines simple rules with a huge amount of possible playing strategies. This paper is focused on developing algorithms for performing simple online opponent modeling in Texas Hold'em. The opponent modeling approach developed enables to select the best strategy to play against each given opponent. Several autonomous agents were developed in order to simulate typical Poker player's behavior and one other agent, was developed capable Of using simple opponent modeling techniques in order to select the best playing strategy against each of the other opponents. Results achieved in realistic experiments using eight distinct poker playing agents showed the usefulness of the approach. The observer agent developed is clearly capable of outperforming all its counterparts in all the experiments performed.

Abstract
As sensors become ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including disaster preparedness and management, adaptability to climate change, national or homeland security, and the management of critical infrastructures. This book presents innovative solutions in offline data mining and real-time analysis of sensor or geographically distributed data. It discusses the challenges and requirements for sensor data based knowledge discovery solutions in high-priority application illustrated with case studies. It explores the fusion between heterogeneous data streams from multiple sensor types and applications in science, engineering, and security. © 2009 by Taylor & Francis Group, LLC.

Abstract
In this paper, an evaluation of several methods allowing the estimation of the Harmonic-to-Noise Ratio (HNR) of sustained vowels was conducted. The HNR estimation methods are mainly based on time, spectral, and cepstral signal representations. An algorithm was implemented for each method and was tested with synthesized voice sounds in order to evaluate their accuracy. Tests were also conducted with real pathological voice sounds in order to evaluate the behaviour of the different methods under real conditions. © 2008 Division of Signal Processin.

Abstract
If we measure the position of a point particle, then we will come about with an interval [a(n), b(n)] into which the point falls. We make use of a Gedankenexperiment to find better and better values of a. and b(n), by reducing their relative distance, in a succession of intervals [a(1), b(1)] superset of [a(2),b(2)] superset of ... superset of [a(n), b(n)] that contain the point. We then use such a point as an oracle to perform relative computation in polynomial time, by considering the succession of approximations to the point as suitable answers to the queries in an oracle Turing machine. We prove that, no matter the precision achieved in such a Gedankenexperiment, within the limits studied, the Turing Machine, equipped with such an oracle, will be able to compute above the classical Turing limit for the polynomial time resource, either generating the class P/poly either generating the class BPP//log*, if we allow for an arbitrary precision in measurement or just a limited precision, respectively. We think that this result is astonishingly interesting for Classical Physics and its connection to the Theory of Computation, namely for the implications on the nature of space and the perception of space in Classical Physics. (Some proofs are provided, to give the flavor of the subject. Missing proofs can be found in a detailed long report at the address http://fgc.math.ist.utl.pt/papers/sm.pdf).

Abstract

Abstract
In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP( ) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP( ) programs. An implementation of CLP( ) is publicly available as part of YAP Prolog at http://www.ncc.up.pt/~vsc/Yap . © 2008 Springer-Verlag Berlin Heidelberg.