From Affect Theoretical Foundations to Computational Models of Intelligent Affective Agents

Autor: Bexy Alfonso, Joaquín Taverner, Emilio Vivancos, Vicente Botti
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Applied Sciences
Volume 11
Issue 22
Applied Sciences, Vol 11, Iss 10874, p 10874 (2021)
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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ISSN: 2076-3417
DOI: 10.3390/app112210874
Popis: [EN] The links between emotions and rationality have been extensively studied and discussed. Several computational approaches have also been proposed to model these links. However, is it possible to build generic computational approaches and languages so that they can be "adapted " when a specific affective phenomenon is being modeled? Would these approaches be sufficiently and properly grounded? In this work, we want to provide the means for the development of these generic approaches and languages by making a horizontal analysis inspired by philosophical and psychological theories of the main affective phenomena that are traditionally studied. Unfortunately, not all the affective theories can be adapted to be used in computational models; therefore, it is necessary to perform an analysis of the most suitable theories. In this analysis, we identify and classify the main processes and concepts which can be used in a generic affective computational model, and we propose a theoretical framework that includes all these processes and concepts that a model of an affective agent with practical reasoning could use. Our generic theoretical framework supports incremental research whereby future proposals can improve previous ones. This framework also supports the evaluation of the coverage of current computational approaches according to the processes that are modeled and according to the integration of practical reasoning and affect-related issues. This framework is being used in the development of the GenIA(3) architecture.
This work is partially supported by the Spanish Government projects PID2020-113416RB-I00, GVA-CEICE project PROMETEO/2018/002, and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.
Databáze: OpenAIRE