A framework that fuses uncertain human advice, encoded as subjective logic opinions, into the policy of a reinforcement learning agent improves learning of model transformation sequences, but only convincingly at low-to-moderate advice uncertainty.
Bridging the Silos of Digitalization and Sustainability by Twin Transition: A Multivocal Literature Review
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abstract
Twin transition is the method of parallel digital and sustainability transitions in a mutually supporting way or, in common terms, "greening of and by IT and data." Twin transition reacts to the growing problem of unsustainable digitalization, particularly in the ecological sense. Ignoring this problem will eventually limit the digital adeptness of society and the problem-solving capacity of humankind. Information systems engineering must find ways to support twin transition journeys through its substantial body of knowledge, methods, and techniques. To this end, we systematically survey the academic and gray literature on twin transition, clarify key concepts, and derive leads for researchers and practitioners to steer their innovation efforts.
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Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance
A framework that fuses uncertain human advice, encoded as subjective logic opinions, into the policy of a reinforcement learning agent improves learning of model transformation sequences, but only convincingly at low-to-moderate advice uncertainty.