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Algorithmic Dependent-Type Theory of Situated Information and Context Assessments

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Distributed Computing and Artificial Intelligence, 19th International Conference (DCAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 583))

Abstract

The paper introduces an enriched formal language of information that establishes propositions dependent on situations and propositional types. The language supports structured data of situated information, which can be partial, parametric, and underspecified. Information can be associated with quantitative evaluations depending on situations. The formal terms can integrate propositional types of situated information with statistical and other quantitative evaluations. Structured content integrated with quantitative data facilitates development of new techniques for amalgamating logic representation of situated, propositional content with numerical data. Numerical data can be provided by techniques from mathematical statistics and probability.

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Correspondence to Roussanka Loukanova .

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Loukanova, R. (2023). Algorithmic Dependent-Type Theory of Situated Information and Context Assessments. In: Omatu, S., Mehmood, R., Sitek, P., Cicerone, S., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 583. Springer, Cham. https://doi.org/10.1007/978-3-031-20859-1_4

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