LEADER 02685nam 2200421 450 001 9910136258403321 005 20230328181852.0 035 $a(CKB)3710000000590861 035 $a(PPN)199152748 035 $a(NjHacI)993710000000590861 035 $a(EXLCZ)993710000000590861 100 $a20230328d2015 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHow mobile robots can self-organise a vocabulary /$fPaul Vogt 210 1$aBerlin, Germany :$cLanguage Science Press,$d[2015] 210 4$dİ2015 215 $a1 online resource (xii, 270 pages) $cillustrations 225 1 $aComputational models of language evolution ;$v2 320 $aIncludes bibliographical references and indexes. 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 $aOne of the hardest problems in science is the symbol grounding problem, a question that has intrigued philosophers and linguists for more than a century. With the rise of artificial intelligence, the question has become very actual, especially within the field of robotics. The problem is that an agent, be it a robot or a human, perceives the world in analogue signals. Yet humans have the ability to categorise the world in symbols that they, for instance, may use for language. This book presents a series of experiments in which two robots try to solve the symbol grounding problem. The experiments are based on the language game paradigm, and involve real mobile robots that are able to develop a grounded lexicon about the objects that they can detect in their world. Crucially, neither the lexicon nor the ontology of the robots has been preprogrammed, so the experiments demonstrate how a population of embodied language users can develop their own vocabularies from scratch. 410 0$aComputational models of language evolution ;$v2. 606 $aArtificial intelligence 606 $aLanguage acquisition$xData processing 615 0$aArtificial intelligence. 615 0$aLanguage acquisition$xData processing. 676 $a006.3 700 $aVogt$b Paul$f1967-$01348177 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910136258403321 996 $aHow mobile robots can self-organise a vocabulary$93085343 997 $aUNINA