0000000000060525
AUTHOR
Thomas Uthmann
Quantification and Characterization of Pulmonary Emphysema in Multislice-CT
The new technology of the Multislice-CT provides volume data sets with approximately isotropic resolution, which permits a non invasive measurement of diffuse lung diseases like emphysema in the 3D space. The aim of our project is the development of a full automatic 3D CAD (Computer Aided Diagnosis) software tool for detection, quantification and characterization of emphysema in a thoracic CT data set. It should supply independently an analysis of an image data set to support the physician in clinical daily routine. In this paper we describe the developed 3D algorithms for the segmentation of the tracheo-bronchial tree, the lungs and the emphysema regions. We present different emphysema des…
Evolution and Learning: Evolving Sensors in a Simple MDP Environment
Natural intelligence and autonomous agents face difficulties when acting in information-dense environments. Assailed by a multitude of stimuli they have to make sense of the inflow of information, filtering and processing what is necessary, but discarding that which is unimportant. This paper aims at investigating the interactions between evolution of the sensorial channel extracting the information from the environment and the simultaneous individual adaptation of agent-control. Our particular goal is to study the influence of learning on the evolution of sensors, with learning duration being the tunable parameter. A genetic algorithm governs the evolution of sensors appropriate for the a…
Learning competitive pricing strategies by multi-agent reinforcement learning
Abstract In electronic marketplaces automated and dynamic pricing is becoming increasingly popular. Agents that perform this task can improve themselves by learning from past observations, possibly using reinforcement learning techniques. Co-learning of several adaptive agents against each other may lead to unforeseen results and increasingly dynamic behavior of the market. In this article we shed some light on price developments arising from a simple price adaptation strategy. Furthermore, we examine several adaptive pricing strategies and their learning behavior in a co-learning scenario with different levels of competition. Q-learning manages to learn best-reply strategies well, but is e…
Analysis of motor control and behavior in multi agent systems by means of artificial neural networks
Abstract This article gives a short introduction to Self-Organizing Maps, a particular form of Artificial Neural Networks and shows by some examples, how these approaches can be used in order to analyze and visualize time series data originating from complex systems. The methods shown in this article have originally been developed for the analysis of RoboCup robot soccer games, a special kind of so-called Multi Agent Systems. Although this application has no direct connection to biomechanics, the examples shown here may give an impression of the abilities of Neural Networks in the field of Time Series Analysis in general. Because of the abstractness of the methods, it appears to be very lik…
Team Description Mainz Rolling Brains 2001
The Mainz Rolling Brains 2001 team is based on our last year’s team. Our modular design as described in [1] has proved to be efficient and flexible. Thus the team could easily be adopted to the soccerserver’s new features and some of the weak spots of our team could be eliminated.
A virtual testbed for analysis and design of sensorimotoric aspects of agent control
Abstract In this paper XRaptor is introduced, an object-oriented simulation tool. It provides a virtual multi-agent world which acts as testbed for agent control mechanisms. This environment encompasses a 3-dimensional space, in which the agents may move. Currently agents are realized modelling some abstract properties of flies and bats. XRaptor provides different levels of information flow and world manipulation capabilities from the agents' point of view. A further purpose of XRaptor is educational: Different teams of developers may design control units for agents which can then be subjected to a tournament.
Software zur automatischen Quantifizierung von Belüftungszuständen bei akutem Lungenversagen in dynamischen CT-Aufnahmen der Lunge1
Purpose: Density measurements in dynamic CT image series of the lungs allow one to quantify ventilated, hyperinflated, and atelectatic pulmonary compartments with high temporal resolution. Fast automatic segmentation of lung parenchyma and a subsequent evaluation of it's respective density values are a prerequisite for any clinical application of this technique. Material and Methods: For automatic lung segmentation in thoracic CT scans, an algorithm was developed which uses (a) different density masks, and (b) anatomic knowledge to differentiate heart, diaphragm and chest wall from ventilated and atelectatic lung parenchyma. With Animal Care Committee approval, the automated technique was t…
Self-organized modularization in evolutionary algorithms.
The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenber…
Experiments in Value Function Approximation with Sparse Support Vector Regression
We present first experiments using Support Vector Regression as function approximator for an on-line, sarsa-like reinforcement learner. To overcome the batch nature of SVR two ideas are employed. The first is sparse greedy approximation: the data is projected onto the subspace spanned by only a small subset of the original data (in feature space). This subset can be built up in an on-line fashion. Second, we use the sparsified data to solve a reduced quadratic problem, where the number of variables is independent of the total number of training samples seen. The feasability of this approach is demonstrated on two common toy-problems.
Prinzipien der Selbstorganisation beim Einsatz in Spracherkennungssystemen
In diesem Artikel werden neue Verfahren zur Selbstorganisation vorgestellt und analysiert, deren Grundlagen auf der von [Kohonen 89] vorgestellten Feature Map und dem Self-Organizing Discrete Manifolds-Verfahren von [Martinetz/Schulten 91] basieren.
Guest editors' introduction: special issue on sensor evolution.
Artificial life researchers, in their attempts to create life-as-it-could-be, have widely studied both the behavior of animals and artifacts. Early precursors of life-like artificial systems such as Grey Walter’s tortoises [4] or Valentino Braitenberg’s vehicles [1] were already demonstrating that ALife research is strongly motivated by the desire to understand and create life-like behavior and (neural) control. Creating life-like behavior in simulations or robots has increased our understanding of the design and evolution of controllers for artificial systems. Despite the interrelationship between behavior, sensors, and other morphological characteristics of animal systems, the evolution o…
Konzept und Einsatz komplexer Informationssysteme im Sportbereich
Fur Systeme zur Erfassung, Auswertung und Bereitstellung von Information gibt es allgemeine Anforderungen und allgemeine Losungsmoglichkeiten, die relativ unabhangig vom konkreten Einsatzbereich sind. Die Orientierung an solchen Standardsystemen reduziert den notwendigen Entwicklungsaufwand und gibt andererseits mehr Raum, sich auf die Losung der spezifischen Probleme zu konzentrieren. Der im folgenden dargestellte Ansatz des “komplexen Informationssystems” ist ein Schritt in diese Richtung.
The Role of Artificial Intelligence Concepts in System Modelling and Simulation: An Overview
The impact of modelling and simulation methodology research on the daily practice of the simulation community is becoming clearly perceivable. The same applies to the application of the artificial intelligence research in a diversity of computer related fields. As both fields are strongly based on models as the main way they convey their knowledge, the fact of synergy caused by combining these two computer related areas comes as a more or less logical consequence. There is an increasing interest of incorporating methods and techniques developed by and for the artificial intelligence community into modelling and simulation methodology and practice. This paper addresses common aspects and dif…
On a Quantitative Measure for Modularity Based on Information Theory
The concept of modularity appears to be crucial for many questions in the field of Artificial Life research. However, there have not been many quantitative measures for modularity that are both general and viable. In this paper we introduce a measure for modularity based on information theory. Due to the generality of the information theory formalism, this measure can be applied to various problems and models; some connections to other formalisms are presented.
Automatische Berechnung des Milzvolumens aus Spiral-CT-Daten mit Hilfe neuronaler Netze und „Fuzzy Logik”∗
PURPOSE To assess spleen segmentation and volumentry in spiral CT scans with and without pathological changes of splenic tissue. METHODS The image analysis software HYBRIKON is based on region growing, self-organized neural nets, and fuzzy-anatomic rules. The neural nets were trained with spiral CT data from 10 patients, not used in the following evaluation on spiral CT scans from 19 patients. An experienced radiologist verified the results. The true positive and false positive areas were compared in terms to the areas marked by the radiologist. The results were compared with a standard thresholding method. RESULTS The neural nets achieved a higher accuracy than the thresholding method. Cor…
Behavior Classification with Self-Organizing Maps
We describe a method that applies Self-Organizing Maps for direct clustering of spatio-temporal data. We use the method to evaluate the behavior of RoboCup players. By training the Self-Organizing Map with player data we have the possibility to identify various clusters representing typical agent behavior patterns. Thus we can draw certain conclusions about their tactical behavior, using purely motion data, i.e. logfile information. In addition, we examine the player-ball interaction that give information about the players' technical capabilities.
Mainz Rolling Brains
Our agent team is the result of a development which had to take place under tight time limitations. The total development time available was slightly less than three months where over most of the time the team developers could invest no more than a few hours per week. The code was developed from scratch to improve over the design and quality of last year’s code. Thus one of the challenges was to keep a smooth development line and to avoid dead ends in the development, as well as to maintain a development environment in which a larger number of developers could work productively.
A Knowledge-Based System for the Diagnosis of Alzheimer’s Disease
Therapies to slow down the progression of Alzheimer’s disease are most effective when applied in its initial stages. Therefore it is important to develop methods to diagnose the disease as early as possible. It is also desirable to establish standards which can be used generally by physicians who may not be experts in diagnosis of the disease. One possible method to obtain an early diagnosis is the evaluation of the glucose metabolism of the brain. In this paper we present a prototype of an expert system that automatically diagnoses Alzheimer’s disease on the basis of positron emission tomography images displaying the metabolic activity in the brain.
A Direct Approach to Robot Soccer Agents: Description for the Team Mainz Rolling rains Simulation League of RoboCup ’98
In the team described in this paper we realize a direct approach to soccer agents for the simulation league of the RoboCup '98- tournament. Its backbone is formed by a detailed world model. Based on information which is reconstructed on the world model level, the rule-based decision levels chose a relevant action. The architecture for the goalie is different from the regular players, introducing heterogeneousness into the team, which combines the advantages of the different control strategies.
A Study of the Simulated Evolution of the Spectral Sensitivity of Visual Agent Receptors
In this article we study a model for the evolution of the spectral sensitivity of visual receptors for agents in a continuous virtual environment. The model uses a genetic algorithm (GA) to evolve the agent sensors along with the control of the agents by requiring the agents to solve certain tasks in the simulation environment. The properties of the evolved sensors are analyzed for different scenarios. In particular, it is shown that the GA is able to find a balance between sensor costs and agent performance in such a way that the spectral sensor sensitivity reflects the emission spectrum of the target objects and that the capability of the sensors to evolve can help the agents significantl…
Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask.
We compared multiple neural networks with a density mask for the automatic detection and quantification of ground-glass opacities on high-resolution CT under clinical conditions.Eighty-four patients (54 men and 30 women; age range, 18-82 years; mean age, 49 years) with a total of 99 consecutive high-resolution CT scans were enrolled in the study. The neural network was designed to detect ground-glass opacities with high sensitivity and to omit air-tissue interfaces to increase specificity. The results of the neural network were compared with those of a density mask (thresholds, -750/-300 H), with a radiologist serving as the gold standard.The neural network classified 6% of the total lung a…
Analyse von Bronchien in der Multislice-CT
Es wird eine Methode zum objektiven Bestimmen der Wanddicke und des Gesamt durchmessers von Bronchien in der 3-dimensionalen Computertomographie vorgestellt. Die Methode wurde erfolgreich an Phantomen evaluiert. Erste Studien sowohl an tierischen als auch an menschlichen Bronchien verliefen erfolgversprechend.
Some Effects of Individual Learning on the Evolution of Sensors
In this paper, we present an abstract model of sensor evolution, where sensor development is only determined by artificial evolution and the adaptation of agent reactions is accomplished by individual learning. With the environment cast into a MDP framework, sensors can be conceived as a map from environmental states to agent observations and Reinforcement Learning algorithms can be utilised. On the basis of a simple gridworld scenario, we present some results of the interaction between individual learning and evolution of sensors.