Patrik Zajec

TL;DR: This work proposes an architecture that integrates Active Learning, Forecasting, Explainable Intelligence, simulated reality, decision-making, and users’ feedback, focusing on synergies between humans and machines, and aligns with the Big Data Value Association Reference Architecture Model.

TL;DR: In this article, the authors propose an ontology and knowledge graph to support collecting feedback regarding forecasts, forecast explanations, recommended decision-making options, and user actions, and provide means to improve forecasting models, explanations, and recommendations of decision making options.

TL;DR: In this article, a general approach for building a smart assistant that provides users with machine learning forecasts and a sequence of decision-making options is presented in this work, where active learning can be used to get data labels for most data instances expected to be most informative.

TL;DR: This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets’ metadata, and entries from the Google knowledge graph to compare two approaches (embeddings-based and semantic-based) regarding demand forecasting.

TL;DR: This work proposes an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users’ feedback and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations and the architecture security is addressed as a general concern.