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 likely that they can easily be adapted to Time Series Analysis problems within the biomechanics context.

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 Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularityas an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularityin comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).

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.

RoboCup is a most challenging project in the field of Artificial Intelligence and Multi-Agent Technology research. In this article, we will discuss some examples showing that RoboCup Simulation League scenarios can also be interesting in the interdisciplinary cooperation between computer science and sport science. One part of the paper concentrates on the generation of surrogate data which can be used in order to test data analysis methods. Furthermore, we will discuss RoboCup scenarios as testbed for sport theory as a future perspective.

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.

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.

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 agent solving a simple grid world task. The performance of the agent is taken as fitness; ‘sensors’ are conceived as a map from environmental states to agent observations, and individual adaptation is modeled by Q-learning. Our experimental results show that due to the principles of cognitive economy learning and varying the degree thereof actually transforms the fitness landscape. In particular we identify a trade-off between learning speed (load) and sensor accuracy (error). These results are further reinforced by theoretical analysis: we derive an analytical measure for the quality of sensors based on the mutual entropy between the system of states and the selection of an optimal action, a concept recently proposed by Polani, Martinetz, and Kim.

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 describing indices.

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.

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.

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 expensive to train.

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.

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.

In this paper we study the emergence and the effects of a possession norm in an artificial society. We link the study of norms and the concept of memes as put forward by Richard Dawkins. Normative behaviour is modelled using memes as carriers for certain behaviours. For our simulations we extend the sugarscape model from Epstein and Axtell (1996) and give the agents the possibility to claim possession of a "plot" of land. Memes regulate the behaviour of the agents regarding the land claims of others. It turns out that the probability for the survival of the population is much higher when possession claims of others are respected. However, there exist short term disadvantages for agents respecting the possessions of others. Thus, the need for a possession norm arises. The introduction of sanctions provides a good possibility to enforce the norm as long as no costs arise for sanctioning agents. We also investigate different kinds of meme propagation and their effects on the establishment of the norm.

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.