03285 am 22005773u 450 991013640300332120230807211432.03-946234-01-1(CKB)3710000000601052(oapen)https://directory.doabooks.org/handle/20.500.12854/39332(EXLCZ)99371000000060105220170206d2015uuuu fy| 0engurm|#---||u||txtrdacontentcrdamediacrrdacarrierHow mobile robots can self-organise a vocabulary[electronic resource] /Paul VogtLanguage Science Press2015Berlin, Germany :Language Science Press,2015.©20151 online resource (xii, 270 pages) illustrations; digital, PDF file(s)Computational Models of Language Evolution ;volume 2Title from OAPEN webpage (viewed on 23 November 2017).Originally presented as the author's thesis (doctoral)--Vrije Universiteit Brussel, Belgium, 2000.3-946234-00-3 Includes bibliographical references and index.Preface --Acknowledgements --1. Introduction --2. The sensorimotor component --3. Language games --4. Experimental results --5. Varying methods and parameters --6. The optimal games --7. Discussion --Appendix A: Glossary --Appendix B: PDL code --Appendix C: Sensory data distribution --Appendix D: Lexicon and ontology --References --Indexes.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.Symbol groundingLanguage acquisitionData processingArtificial intelligencelanguage in robotsartificial intelligenceFeature extractionFeature vectorJoint attentionLexiconReferenceSymbol grounding problemTalking HeadsSymbol grounding.Language acquisitionData processing.Artificial intelligence.402.85Vogt Paul444854UkMaJRUBOOK9910136403003321How mobile robots can self-organise a vocabulary2092703UNINA