The complexity of analysing dynamical systems often lies in the difficulty to monitor each of their dynamic properties. In this article, we use qualitative models to present an exhaustive way of representing every possible state of a given system, and combine it with Bayesian networks to integrate quantitative information and reasoning under uncertainty. The result is a combined model able to give explanations relying on expert knowledge to predict the behaviour of a system. We illustrate our approach with a deterministic model to show how the combination is done, then extend this model to integrate uncertainty and demonstrate its benefits
Sustainable Experience Design Professor Frans Melissen dialogues about Sustainability Intelligence with Joseph Roevens. Topics include: Initial interest in Sustainability? https://bit.ly/2G1RTz4 How do you live Sustainably? When do you ‘sin’? https://bit.ly/2IdGFsZGaia Zoo & “Sustainable Customer Experience Design” https://bit.ly/2UgtzlY 50 Shades of Green https://bit.ly/2IeH3Yf Breda University’s Sustainable Travel Policy https://bit.ly/2WN2DqE How to stimulate Sustainable behavior? https://bit.ly/2Kb2Hiv Sustainability Intelligence: Naïve, Native & Narrative https://bit.ly/2ONUBv7 1. Naïve Intelligence https://bit.ly/2YQXeAL 2. Native Intelligence https://bit.ly/2uPBVSc 3. Narrative Intelligence, e.g.Zappos Delivering Happiness https://bit.ly/2YUbHMa The Powers-that-be vs the Grass Roots. https://bit.ly/2FKNAqw The Sharing Economy & its abuse https://bit.ly/2TTKWE8 Sustainability as the Goal, not as an Instrument to continue the old system https://bit.ly/2YRXqzB Projects at Breda University: SCITHOS https://bit.ly/2U0yG4x Sustainable Customer Experience Design https://bit.ly/2G1TB3y Improving Sustainability in the Hospitality Industry https://bit.ly/2Uw1vu3
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From the article: The ethics guidelines put forward by the AI High Level Expert Group (AI-HLEG) present a list of seven key requirements that Human-centered, trustworthy AI systems should meet. These guidelines are useful for the evaluation of AI systems, but can be complemented by applied methods and tools for the development of trustworthy AI systems in practice. In this position paper we propose a framework for translating the AI-HLEG ethics guidelines into the specific context within which an AI system operates. This approach aligns well with a set of Agile principles commonly employed in software engineering. http://ceur-ws.org/Vol-2659/
