Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 145–154 (2020)
Abstract. We propose a new logic-based language called LogDL (Log Description Language) that is designed to be a medium for the knowledge discovery workflows conducted over multimodal process-related and spatio-temporal data sets. It makes it possible to operate with the original data along with machine-learning-driven insights expressed as facts, rules and formulas, regarded as higher-level descriptive logs reflecting knowledge about the observed processes in real or virtual environments. LogDL is inspired by the research at the border of AI and games, precisely by GDL (Game Description Language) that was developed for General Game Playing. We compare LogDL to GDL, emphasizing that formal frameworks for analyzing gameplay data sets are a good prerequisite for the case of real,``not digital'' processes. As LogDL is a logic-based language, we present its syntax and semantics. We also discuss how to design its high-performance interpreter that is a must for commercial scenarios.
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