Language Evolution and Computation Bibliography

This paper reports recent progress on modeling the grounded co-acquisition of syntax and semantics of locative spatial language in developmental robots. We show how a learner robot can learn to produce and interpret spatial utterances in guided-learning interactions with a tutor robot (equipped with a system for producing English spatial phrases). The tutor guides the learning process by simplifying the challenges and complexity of utterances, gives feedback, and gradually increases the complexity of the language to be learnt. Our experiments show promising results towards long-term, incremental acquisition of natural language in a process of codevelopment of syntax and semantics.

In this paper we offer arguments for why modeling in the field of artificial language evolution can benefit from the use of real robots. We will propose that robotic experimental setups lead to more realistic and robust models, that real-word perception can provide the basis for richer semantics and that embodiment itself can be a driving force in language evolution. We will discuss these proposals by reviewing a variety of robotic experiments that have been carried out in our group and try to argue for the relevance of the approach.

How can we explain the enormous amount of creativity and flexibility in spatial language use? In this paper we detail computational experiments that try to capture the essence of this puzzle. We hypothesize that flexible semantics which allow agents to conceptualize reality in many different ways are key to this issue. We will introduce our particular semantic modeling approach as well as the coupling of conceptual structures to the language system. We will justify the approach and show how these systems play together in the evolution of spatial language using humanoid robots.