Theories of Meaning for the Internet of Things

Färber M, Svetashova Y, Harth A (2021)


Publication Language: English

Publication Type: Book chapter / Article in edited volumes

Publication year: 2021

Publisher: Springer International Publishing

Edited Volumes: Concepts in Action: Representation, Learning, and Application

Series: Language, Cognition, and Mind

City/Town: Cham

Book Volume: 9

DOI: 10.1007/978-3-030-69823-2

Abstract

Färber, Svetashova, and Harth’s contribution “Theories of Meaning for the Internet of Things” (Chap. 3) is concerned with the representation of concepts in the context of the Internet of Things (IoT) from the perspective of artificial intelligence. They compare different representational frameworks from philosophy and computer science, taking a simple smart home setting as an application example. Overall, they consider four different approaches, namely model-theoretic semantics (which are based on first-order logic), possible world semantics (using modal logic), situation semantics, and cognitive and distributional semantics (i.e., spatial models of meaning). With the IoT application in mind, the authors assess whether these representational frameworks are able to represent intersubjectivity (i.e., multiple agents) and dynamics (i.e., changes in the state of the world) and to what extent they can be connected to perception. The authors conclude that none of the existing approaches is able to completely satisfy all three requirements. They propose to further investigate a combination between situational and distributional semantics as a promising avenue for future research.

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How to cite

APA:

Färber, M., Svetashova, Y., & Harth, A. (2021). Theories of Meaning for the Internet of Things. In Lucas Bechberger, Kai-Uwe Kühnberger and Mingya Liu (Eds.), Concepts in Action: Representation, Learning, and Application. Cham: Springer International Publishing.

MLA:

Färber, Michael, Yulia Svetashova, and Andreas Harth. "Theories of Meaning for the Internet of Things." Concepts in Action: Representation, Learning, and Application. Ed. Lucas Bechberger, Kai-Uwe Kühnberger and Mingya Liu, Cham: Springer International Publishing, 2021.

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