DIAUTIS III: A Fuzzy and Affective Platform for Obtaining Autism Mental Models and Learning Aids

• Published in: International journal of advanced computer science & applications Vol. 15; no. 8
• Main Authors: Alami, Mohamed El, khabbazi, Sara El, Arriaga, Fernando de
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2024
• Subjects: Affect (Psychology); Affective computing; Autism; Autistic children; Children; Cognitive maps; Diagnosis; Games; Instructional aids; Software
• DOI: 10.14569/IJACSA.2024.0150835
• ISSN: 2158-107X

Autism spectrum disorders (ASD) are conditions characterized by social interaction and communication difficulties, atypical patterns of activities, and unusual reactions to sensations. Char-acteristics of autism may be detected in early childhood, but diagnosis is often delayed. The diagnosis of autistic children typically aligns with medical and psychological recommenda-tions, but it does not evaluate all the problems, intensity, or changes in symptoms over time. It also does not identify the affective states associated with these deficiencies, making aid less effective. The mental model of autistic children contains their deficits, tasks, and intensities, beyond diagnostics. That why we enhance DIAUTIS platform for achieve our objectives related to helping children with ASD. DIAUTIS I is a platform that aim to diagnosing autism and identifying its severity using cognitive, fuzzy, and affective computing. It presents tests, eval-uates results, and presents a final model. Then, we implemented DIAUTIS II by adding KASP methodology, a new methodology of designing serious games, based on knowledge, affect, sensory, and pedagogy, this tool allows to DIAUTIS II agents to design-ing over 80 games, considering a child's background. In this paper, we will present a new tool of formalization of the autism mental model based on fuzzy and affective computing. DIAUTIS III is the extension of DIAUTIS II platform aim to represent a cognitive fuzzy mental model with using the metrics of category theory. So far, no mental model has been developed for autism. Our mental model, can be obtained anytime as a fuzzy cognitive map or fuzzy graph and the use of affective computing. In addi-tion, the mathematical theory of categories represent this men-tal model of an autistic child from a fuzzy graph, and it allows for operations like CONS and TRA to evaluate the difference between two mental models. Fuzzy cognitive mental model can be used to develop new techniques for improving the learning and integration of autistic children into social life, which is the focus of our immediate future.