Konnektionismus als Methode, Modellvorstellung, aber auch als Weltbild dringt immer mehr in die Domänen der AI (KI) und Kognitionsforschung vor. Obwohl es eine Reihe von Konferenzen gibt, die künstliche neuronale Netzwerke - die Basis des Konnektionismus - zum Inhalt haben, scheint dem Bereich der Intelligenzforschung und -modellierung mit Hilfe solcher Netzwerke vergleichsweise noch wenig Raum geboten zu werden. Die Konferenz, die vom 18. bis 21. September 1990 in Salzburg stattgefunden hat und deren Proceedings in diesem Band vorliegen, sollte diese Lücke schließen. Sie hat in Form von Vorträgen, Workshops und Panels Arbeiten zusammengebracht, die sich mit Anwendungen neuronaler Netzwerke auf Probleme der AI, mit grundlegenden Aspekten von massivem Parallelismus und Netzwerkverarbeitung in der AI sowie mit theoretischen Implikationen der Modelle auf die AI und die Kognitionsforschung im allgemeinen beschäftigen. Die vorliegenden Beiträge geben einen guten Einblick in den Stand der Forschung - besonders im deutschsprachigen Raum, aber auch darüber hinausgehend.

The shipping industry handles over 90% of the global trade volume and is responsible for approximately 3% of global CO2 emissions. Meanwhile, trade by the shipping industry is expected to increase by up to 130% by 2050 compared to 2008. At the same time, the goal is to reduce Green House Gas (GHG) emissions from the shipping industry to half of the 2008 level by 2050. In support of this goal, this thesis is concerned with a comprehensive approach for optimizing the ship's operation, i.e., an optimization approach that simultaneously involves route selection, energy management, propeller pitch, and engine control. In addition, this thesis also analyses the application of wind propulsion systems. The optimization of the ship's operation is implemented in the form of Reinforcement Learning (RL) methods. The use of RL-based methods to simultaneously optimize various aspects of the ship's trajectory and controls is a novel approach compared to the current state-of-art and embodies this thesis' inherent innovation. The results specifically highlight the importance of parallelizing route optimization with the optimization of other control aspects. Ultimately, it is found that the solution emanating from a purely RL-based approach can be further enhanced when the optimized route, speed, and power profiles are used to perform individual DP-based optimizations on the energy management in a post-processing step.

The knowledge-based management of medical acts in NUCLEUS -- Knowledge Acquisition, Representation & Learning -- Knowledge Representation and Modelling in HYBRIKON -- Knowledge Organisation in Medical KBS Construetion -- A Framework for Modular Knowledge Bases in the Domain of Hypertension Diseases -- KAVAS-2: Knowledge Acquisition, Visualisation and Assessment System -- KAVAS's Framework for quality assessment of medical knowledge -- KAVAS's Conditioning of the Induction Algorithm -- Clinical decision-support in the field of TETANUS serology using an associative storage model implemented in LISP -- Model based learning support to knowledge acquisition: A clinical case study -- MODELS FOR MEDICAL KNOWLEDGE REPRESENTATION AND MEDICAL REASONING IN A C.A.I SYSTEM -- Case Based Reasoning in Clinical Evaluation -- Object-oriented mentality: the most suited paradigm for medical knowledge-based systems -- Applications Based on Neural Nets -- Classification of protein patterns using neural networks: pixel based versus feature based approach -- Evaluation of an epiderniological data set as an example of the application of neural networks to the analysis of large medical data sets -- A Neural Network Modular System for Object Classification in Brain MR Images -- A Neural Network Identifies Faces with Morphological Syndromes -- Grading of Gliomas in Stereotactic Biopsies with Neural Networks -- Self Organizing Maps for the Evaluation of High Resolution ECG -- AUTHOR INDEX

This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.

This volume features the complete text of all regular papers, posters, and summaries of symposia presented at the 14th annual meeting of the Cognitive Science Society.

The complexity of the brain and the protean nature of behavior remain the most elusive area of science, but also the most important. van Hemmen and Sejnowski invited 23 experts from the many areas--from evolution to qualia--of systems neuroscience to formulate one problem each. Although each chapter was written independently and can be read separately, together they provide a useful roadmap to the field of systems neuroscience and will serve as a source of inspirations for future explorers of the brain.