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            How mobile robots can self-organise a vocabulary

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            Author(s)
            Vogt, Paul
            Collection
            Knowledge Unlatched (KU)
            Language
            English
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            Abstract
            One of the hardest problems in science is the symbol grounding problem, a question that has intrigued philosophers and linguists for more than a century. With the rise of artificial intelligence, the question has become very actual, especially within the field of robotics. The problem is that an agent, be it a robot or a human, perceives the world in analogue signals. Yet humans have the ability to categorise the world in symbols that they, for instance, may use for language. This book presents a series of experiments in which two robots try to solve the symbol grounding problem. The experiments are based on the language game paradigm, and involve real mobile robots that are able to develop a grounded lexicon about the objects that they can detect in their world. Crucially, neither the lexicon nor the ontology of the robots has been preprogrammed, so the experiments demonstrate how a population of embodied language users can develop their own vocabularies from scratch.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/199240
            Keywords
            language in robots; artificial intelligence; Feature extraction; Feature vector; Joint attention; Lexicon; Reference; Symbol grounding problem; Talking Heads; thema EDItEUR::C Language and Linguistics::CF Linguistics; thema EDItEUR::U Computing and Information Technology
            DOI
            10.26530/OAPEN_603358
            ISBN
            9783946234012
            Publisher
            Language Science Press
            Publisher website
            http://langsci-press.org/
            Publication date and place
            2015
            Grantor
            • Knowledge Unlatched
            Series
            Computational Models of Language Evolution,
            Pages
            270
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            • logo Investir l'avenirInvestir l'avenir
            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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