Artificial life models, swarm intelligent and evolutionary computation algorithms are usually built on fixed size populations. Some studies indicate however that varying the population size can increase the adaptability of these systems and their capability to react to changing environments. In this paper we present an extended model of an artificial ant colony system designed to evolve on digital image habitats. We will show that the present swarm can adapt the size of the population according to the type of image on which it is evolving and reacting faster to changing images, thus converging more rapidly to the new desired regions, regulating the number of his image foraging agents. Finally, we will show evidences that the model can be associated with the Mathematical Morphology Watershed algorithm to improve the segmentation of digital grey-scale images. KEYWORDS: Swarm Intelligence, Perception and Image Processing, Pattern Recognition, Mathematical Morphology, Social Cognitive Maps, Social Foraging, Self-Organization, Distributed Search.

We propose a new method for the estimation of parameters of hidden diffusion processes. Based on parametrization of the transition matrix, the Baum-Welch algorithm is improved. The algorithm is compared to the particle filter in application to the noisy periodic systems. It is shown that the modified Baum-Welch algorithm is capable of estimating the system parameters with better accuracy than particle filters.

Combining a set of existing constraint solvers into an integ rated system of cooperating solvers is a useful and economic principle to solve hybrid constraint problems. In this paper we show that this approach can also be used to integrate differ ent language paradigms into a unified framework. Furthermore, we study the syntacti c, semantic and operational impacts of this idea for the amalgamation of declarative and constraint programming. To appear in Theory and Practice of Logic Programming (TPLP).

This paper presents an'elitist approach' for extracting au tomat-ically well-realized speech sounds with high confidence. Th e elitist approach uses a speech recognition system based on H id-den Markov Models (HMM). The HMM are trained on speech sounds which are systematically well-detected in an iterat ive procedure. The results show that, by using the HMM models defined in the training phase, the speech recognizer detects reliably specific speech sounds with a small rate of errors.

We consider the problem of on-line prediction of real-valued labels, assumed bounded in absolute value by a known constant, of new objects from known labeled objects. The prediction algorithm's performance is measured by the squared deviation of the predictions from the actual labels. No stochastic assumptions are made about the way the labels and objects are generated. Instead, we are given a benchmark class of prediction rules some of which are hoped to produce good predictions. We show that for a wide range of infinite-dimensional benchmark classes one can construct a prediction algorithm whose cumulative loss over the first N examples does not exceed the cumulative loss of any prediction rule in the class plus O(sqrt(N)); the main differences from the known results are that we do not impose any upper bound on the norm of the considered prediction rules and that we achieve an optimal leading term in the excess loss of our algorithm. If the benchmark class is "universal" (dense in the class of continuous functions on each compact set), this provides an on-line non-stochastic analogue of universally consistent prediction in non-parametric statistics. We use two proof techniques: one is based on the Aggregating Algorithm and the other on the recently developed method of defensive forecasting.

Research on integrated neural-symbolic systems has made si gnificant progress in the recent past. In particular the understanding of ways t o deal with symbolic knowledge within connectionist systems (also cal led artificial neural networks) has reached a critical mass which enables the c ommunity to strive for applicable implementations and use cases. Recen t work has covered a great variety of logics used in artificial intelligenc e and provides a multitude of techniques for dealing with them within the con text of artificial neural networks. Already in the pioneering days of computational models of ne ural cognition, the question was raised how symbolic knowledge can be r epresented and dealt with within neural networks. The landmark paper [M cCulloch and Pitts, 1943] provides fundamental insights how propositional logic can be processed using simple artificial neural networks. Within the following decades, however, the topic did not receive much attention as research in artifi cial intelligence initially focused on purely symbolic approaches. The power of machine learning using artificial neural networking was not recogni zed until the 80s, when in particular the backpropagation algorithm [Rumelha rt et al., 1986] made connectionist learning feasible and applicable in pra ctice. These advances indicated a breakthrough in machine learnin g which quickly led to industrial-strength applications in areas s uch as image analysis, speech and pattern recognition, investment analysis, engine monitoring, fault diagnosis, etc. During a training process from raw dat a, artificial neural networks acquire expert knowledge about the problem dom ain, and the ability to generalize this knowledge to similar but previou sly unencountered situations in a way which often surpasses the abilities of hu man experts.

Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main components all evolutionary algorithms (EA), sketch the differences between different types of EAs and survey application areas ranging from optimization, modeling and simulation to entertainment.

This paper presents an artificial evolution-based method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the "flies", which are evolved following a biologically inspired scheme and give a fast, low-cost solution to the obstacle detection problem in mobile robotics.

The aim of our study is to detect balance disorders and a tendency towards the falls in the elderly, knowing gait parameters. In this paper we present a new tool for gait analysis based on markerless human motion capture, from camera feeds. The system introduced here, recovers the 3D positions of several key points of the human body while walking. Foreground segmentation, an articulated body model and particle filtering are basic elements of our approach. No dynamic model is used thus this system can be described as generic and simple to implement. A modified particle filtering algorithm, which we call Interval Particle Filtering, is used to reorganise and search through the model's configurations search space in a deterministic optimal way. This algorithm was able to perform human movement tracking with success. Results from the treatment of a single cam feeds are shown and compared to results obtained using a marker based human motion capture system.

Automobile has become the dominant transport mode in the world in the last century. In order to meet a continuously growing demand for transport, one solution is to change the control approach for vehicle to full driving automation, which removes the driver from the control loop to improve efficiency and reduce accidents. Recent work shows that there are several realistic paths towards this deployment : driving assistance on passenger cars, automated commercial vehicles on dedicated infrastructures, and new forms of urban transport (car-sharing and cybercars). Cybercars have already been put into operation in Europe, and it seems that this approach could lead the way towards full automation on most urban, and later interurban infrastructures. The European project CyberCars has brought many improvements in the technology needed to operate cybercars over the last three years. A new, larger European project is now being prepared to carry this work further in order to meet more ambitious objectives in terms of safety and efficiency. This paper will present past and present technologies and will focus on the future developments.