Explainable AI for Constraint-Based Expert Systems

The need to derive explanations from machine learning (ML)-based AI systems has been addressed in recent research due to the opaqueness of their processing.However, a significant amount of productive AI systems are not based on ML but are expert systems including strong opaqueness.A resulting lack of understanding causes massive inefficiencies in business processes that involve opaque expert systems. This work uses recent research interest in explainable AI (XAI) to generate knowledge for the design of explanations in constraint-based expert systems.Following the Design Science Research paradigm, we develop design requirements and design principles. Subsequently, we design an artifact and evaluate the artifact in two experiments. We observe the following phenomena. First, global explanations in a textual format were well-received. Second, abstract local explanations improved comprehensibility. Third, contrastive explanations successfully assisted in the resolution of contradictions. Finally, a local tree-based explanation was perceived as challenging to understand

The role of artificial intelligence in skilled work and consequences for vocational training

Artificial intelligence (AI) has long been a present-day topic and is having an impact on the economy, society, skilled work and the work environment. However, there are often very different assessments of the effects: On the one hand the loss of jobs and even professions has been predicted, on the other hand new support and shaping options for work are emerging. In addition, AI is treated as a powerful buzzword without considering the real technologies and requirements behind it. Nevertheless, consequences for the world of work and its employees can only be derived and vocational training concepts designed if the handling of AI in skilled work has been concretized beforehand. The impact of AI on vocational education and training and on the skilled worker has so far been discussed in a rather abstract way and only very rarely focused on research. At the same time, technological developments in certain areas (including expert systems, machine learning approaches, digital twins) have already proceeded to such an extent that the effects on skilled work are noticeable and are thus evident. Much will depend on the design of the human-machine interface. In order to evaluate how skilled labour and AI can successfully “cooperate” in manufacturing, a model is presented here that can support the evaluation process

The 'what' and 'how' of learning in design, invited paper

Previous experiences hold a wealth of knowledge which we often take for granted and use unknowingly through our every day working lives. In design, those experiences can play a crucial role in the success or failure of a design project, having a great deal of influence on the quality, cost and development time of a product. But how can we empower computer based design systems to acquire this knowledge? How would we use such systems to support design? This paper outlines some of the work which has been carried out in applying and developing Machine Learning techniques to support the design activity; particularly in utilising previous designs and learning the design process

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed