Natural Language is evidence for the ingenious ways humans conceptualize reality. English, for instance, provides various ways for talking about objects using many spatial relations such as front, back, up etc. Importantly, English allows to use these different spatial relations in many different conceptualization strategies. For instance, spatial relations can be used in group-based reference (adjectives), or to denote regions (prepositional use). Another type of evidence for the complexity of Natural language semantics comes from the analysis of cross-cultural variation which shows that languages other than English have found radically different ways for conceptualizing reality. E.g. Tzeltal speakers exclusively use absolute spatial relations such as uphill/downhill for talking about objects. These findings point to semantics as an evolutionary system in itself with wide-ranging impact on the evolution of language as a whole. This talk will introduce a computational system that allows to model complex conceptualizations underlying natural language, and enables robots to automatically conceptualize the world. In a second part we show how the system can be used to investigate the evolution of rich, open-ended semantics similar to those found in natural language. The talk primarily uses examples from investigations into spatial language, but other areas of language are also discussed.
language evolution, language change, robotics, semantics