2012
DOI: 10.1007/978-1-4614-3064-3
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Language Grounding in Robots

Abstract: except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are to proprietary rights.

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Cited by 36 publications
(1 citation statement)
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References 62 publications
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“…Searle's argument is that even though such a system seems to satisfy the Turing test, we would be hardpressed to call such a system intelligent because the grounding problem (the ability to establish symbolic meaning) is not addressed. It can be argued that systems must start to learn using mathematics that are both continuous and discrete or symbolic in nature (Steels and Hild 2012). If successful, AI will transition from problems of state estimation to the interpretation of meaning.…”
Section: Groundingmentioning
confidence: 99%
“…Searle's argument is that even though such a system seems to satisfy the Turing test, we would be hardpressed to call such a system intelligent because the grounding problem (the ability to establish symbolic meaning) is not addressed. It can be argued that systems must start to learn using mathematics that are both continuous and discrete or symbolic in nature (Steels and Hild 2012). If successful, AI will transition from problems of state estimation to the interpretation of meaning.…”
Section: Groundingmentioning
confidence: 99%