LEADER 03285 am 22005773u 450 001 9910136403003321 005 20230807211432.0 010 $a3-946234-01-1 035 $a(CKB)3710000000601052 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/39332 035 $a(EXLCZ)993710000000601052 100 $a20170206d2015uuuu fy| 0 101 0 $aeng 135 $aurm|#---||u|| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHow mobile robots can self-organise a vocabulary$b[electronic resource] /$fPaul Vogt 210 $cLanguage Science Press$d2015 210 1$aBerlin, Germany :$cLanguage Science Press,$d2015. 210 4$dİ2015 215 $a1 online resource (xii, 270 pages) $cillustrations; digital, PDF file(s) 225 1 $aComputational Models of Language Evolution ;$vvolume 2 300 $aTitle from OAPEN webpage (viewed on 23 November 2017). 300 $aOriginally presented as the author's thesis (doctoral)--Vrije Universiteit Brussel, Belgium, 2000. 311 1 $a3-946234-00-3 320 $aIncludes bibliographical references and index. 327 $aPreface --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. 330 3 $aOne 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. 606 $aSymbol grounding 606 $aLanguage acquisition$xData processing 606 $aArtificial intelligence 610 $alanguage in robots 610 $aartificial intelligence 610 $aFeature extraction 610 $aFeature vector 610 $aJoint attention 610 $aLexicon 610 $aReference 610 $aSymbol grounding problem 610 $aTalking Heads 615 0$aSymbol grounding. 615 0$aLanguage acquisition$xData processing. 615 0$aArtificial intelligence. 676 $a402.85 700 $aVogt$b Paul$0444854 801 2$bUkMaJRU 906 $aBOOK 912 $a9910136403003321 996 $aHow mobile robots can self-organise a vocabulary$92092703 997 $aUNINA