Integrative (Synchronisations-)Mechanismen der (Neuro-)Kognition vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisationsparadigmas

Der Gegenstand der vorliegenden Arbeit besteht darin, aufbauend auf dem (Haupt-)Thema, der Darlegung und Untersuchung der Lösung des Bindungsproblems anhand von temporalen integrativen (Synchronisations-)Mechanismen im Rahmen der kognitiven (Neuro-)Architekturen im (Neo-)Konnektionismus mit Bezug auf die Wahrnehmungs- und Sprachkognition, vor allem mit Bezug auf die dabei auftretende Kompositionalitäts- und Systematizitätsproblematik, die Konstruktion einer noch zu entwickelnden integrativen Theorie der (Neuro-)Kognition zu skizzie-ren, auf der Basis des Repräsentationsformats einer sog. „vektoriellen Form“, u.z. vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisations-Paradigmas

Automated observation scheduling for the VLT

It is becoming increasingly evident that, in order to optimize the observing efficiency of large telescopes, some changes will be required in the way observations are planned and executed. Not all observing programs require the presence of the astronomer at the telescope: for those programs which permit service observing it is possible to better match planned observations to conditions at the telescope. This concept of flexible scheduling has been proposed for the VLT: based on current and predicted environmental and instrumental observations which make the most efficient possible use of valuable time. A similar kind of observation scheduling is already necessary for some space observatories, such as Hubble Space Telescope (HST). Space Telescope Science Institute is presently developing scheduling tools for HST, based on the use of artificial intelligence software development techniques. These tools could be readily adapted for ground-based telescope scheduling since they address many of the same issues. The concept are described on which the HST tools are based, their implementation, and what would be required to adapt them for use with the VLT and other ground-based observatories

MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH

Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and is asked to put their relationship in words his answer will probably reveal a fuzzy nature like: "IF the inflation rate in the EURO-Zone is low and the GDP growth rate is larger than in the US THEN the EURO will rise against the USD". æLowÆ and ælargerÆ are fuzzy terms which give a gradual linguistic meaning to crisp intervalls in the respective universes of discourse. In order to learn a Fuzzy Fuzzy Rule base from examples we introduce Genetic Algorithms and Artificial Neural Networks as learning operators. These examples can either be empirical data or originate from an economic simulation model. The software GENEFER (GEnetic NEural Fuzzy ExplorER) has been developed for designing such a Fuzzy Rule Base. The design process is modular and comprises Input Identification, Fuzzification, Rule-Base Generating and Rule-Base Tuning. The two latter steps make use of genetic and neural learning algorithms for optimizing the Fuzzy Rule-Base.

Neuronale Netze für betriebliche Anwendungen:Anwendungspotentiale und existierende Systeme

Der vorliegende Arbeitsbericht zeigt eine Auswahl neuronaler Netze für betriebliche Anwendungen. Aufbauend auf der Vorstellung einiger Systeme wird sowohl vom konkreten Anwendungsgebiet als auch von der konkreten Architektur des neuronalen Netzes abstrahiert, um so ein Übertragen der Erkenntnisse auf andere, ähnlich gelagerte Anwendungsprobleme zu ermöglichen. Anhand der abstrahierten Beschreibung ist es möglich, neue betriebliche Anwendungspotentiale neuronaler Netze aufzudecken. Dazu wird überprüft, inwieweit eine neue, potentielle Anwendung denselben Kriterien genügt. Aufgrund der Analogien erhält man neben einer „Machbarkeitsstudie“ ggf. bereits Hinweise auf die geeignete Wahl eines Netzwerktyps und der zugehörigen Netzwerkparameter für das neue Anwendungsproblem

Hybride Wissensverarbeitung in der präventivmedizinischen Diagnostik

von Andreas KollerPaderborn, Univ.-GH, Diss., 199

Bridging Symbolic and Sub-Symbolic AI: Towards Cooperative Transfer Learning in Multi-Agent Systems

Cooperation and knowledge sharing are of paramount importance in the evolution of an intelligent species. Knowledge sharing requires a set of symbols with a shared interpretation, enabling effective communication supporting cooperation. The engineering of intelligent systems may then benefit from the distribution of knowledge among multiple components capable of cooperation and symbolic knowledge sharing. Accordingly, in this paper, we propose a roadmap for the exploitation of knowledge representation and sharing to foster higher degrees of artificial intelligence. We do so by envisioning intelligent systems as composed by multiple agents, capable of cooperative (transfer) learning—Co(T)L for short. In CoL, agents can improve their local (sub-symbolic) knowledge by exchanging (symbolic) information among each others. In CoTL, agents can also learn new tasks autonomously by sharing information about similar tasks. Along this line, we motivate the introduction of Co(T)L and discuss benefits and feasibility