Auflistung nach Schlagwort "Software Models"
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- KonferenzbeitragChristian Doppler Laboratory on Security and Quality Improvement in the Production Systems Life Cycle(Software Engineering 2020, 2020) Winkler, Dietmar; Biffl, StefanThe size and complexity of software components in production systems engineering, such as manufacturing plants or automation systems, requires effective and efficient approaches for security and quality improvement. In industrial practice, engineers from different disciplines, such as electrical, mechanical, and software disciplines typically follow a plan-driven and sequential engineering process approach with parallel engineering activities within a heterogeneous set of methods and tools. Therefore, major challenges concern (a) insufficient data exchange capabilities between disciplines, (b) a lack of consistency evaluation capabilities across disciplines, tools, and engineering phases, (c) insufficient knowledge representation and exchange between disciplines and project stakeholders and (d) limited security considerations. The goal of the Christian Doppler Laboratory on Security and Quality Improvement in the Production Systems Life Cycle (CDL-SQI) is to address these challenges in cooperation with industry partners in the production systems domain. We build on requirements and use case explorations at industry partners and on best-practices from Business Informatics to develop concepts and prototype solutions for the target domain and evaluate these concepts and prototypes in close collaboration with industry partners We derive requirements, use cases, and test data from industry and provide concepts and prototypes to the industry partner and to related scientific communities.
- KonferenzbeitragMachine trainable software models towards a cognitive thinking AI with the natural language processing platform NLX(Modellierung 2022 Satellite Events, 2022) Schaller, FelixSince the last decade, machine learning, especially with artificial neural networks, has triggered a new quantum leap in computer science. Despite the considerable achievements, these applications still lack a general purpose approach for artificial intelligence (AI). The main reason is the absence of the ability for cognitive reflection or self-awareness. They are mainly highly specialized trained patterns that can solve intricate problems but cannot describe themselves. I would like to contrast this with a new method of trainable software models that shall be capable for self-awareness. Implemented in the project Natural Language Platform NLX it shall be demonstrated that self-aware AI is key for human-like cognitive tasks. The hypothesis claims that to reach this goal, machines require to describe its system context semantically by a formal model. Neuronal networks are good at specific tasks, but the trained patterns cannot derive a reasoning for the trained solution. Only that it satisfies its intended functionality - but not why. Creating formal models instead of patterns has turned out, that the formal nature of natural language is the best to reach that goal of a self-aware AI. Certainly there are other AI’s that do natural language processing with neuronal networks. But most of the models try to resolve the content with too rigid constraints and with little attention to the context. For this project context plays a key role to resolve the meaning of natural language. If the context is resolved correctly, such AI can be used for general purpose tasks resolving anything imaginable.