Ivan Mutis1

Ph.D., Assistant Professor, Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL, United States.

Abstract: In engineering design processes, participants (architects and builders) depend on sharing information practices to communicate meanings through artifacts (3D visual representations). However, there are multiple instances of ambiguities and inconsistencies in these artifacts that produce workflow disruptions. For example in design review engineering activities, while some participants assume that others will have full familiarity with the artifacts (familiarity with some instances from the designs), other participants do not recognize some aspects of what a particular representation implies (some aspects of the artifacts are invisible to other project participants). The observer’s perception is a reaction towards some aspects of the artifacts but not necessarily the understanding of their intended meaning, such as design instances conditions in context. Therefore, the observer’s awareness and ability to sensorially experience some aspects of designs, including symmetries, geometries, patterns, and connections, cannot be explicitly determined. The current mediation technologies (CAD systems, Parametric Models) enhance the designers’ ability to manipulate data and observe information related to the designs. But they are not built to assert intended interpretations or to arrive effectively at implied conclusions. Building on Semiotics this research explores the cognitive agents’ (design interpreters) ability to efficiently arrive at less ambiguous interpretation of artifacts’ meanings in sharing information practices. The challenge is to enhance the observer’s experiences as they are directed towards some aspects of the artifacts by virtue of meaning and sensory-enabling-conditions. The focus is on improving cognitive agents’ reasoning-efficiency-ability through the use of mediating technologies. This investigation proposes intelligent geometric topologies for semantic interpretation of design components (iCon) to streamline the communication of designs. iCon is an artificial language that uses symbolic vocabulary based on a semiotic framework. It has extensions for basic operations that indicate actions (e.g, process, read) and for meanings to support intentionality (e.g., definition of interactions of two objects).

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Bibliographical Reference:
Ivan Mutis. “ARTIFICIAL SYMBOLIC VOCABULARY TO ENABLE EFFECTIVE SEMANTIC INTERPRETATION OF SHARED INFORMATION.” In Proceedings of International Conference on Civil and Building Engineering Informatics (ICCBEI 2015), 126. Tokyo, Japan, 2015.