No abstract

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.

Grounding language in sensorimotor spaces is an important and difficult task. In order, for robots to be able to interpret and produce utterances about the real world, they have to link symbolic information to continuous perceptual spaces. This requires dealing with inherent vagueness, noise and differences in perspective in the perception of the real world. This paper presents two case studies for spatial language and quantification that show how cognitive operations -the building blocks of grounded procedural semantics -can be efficiently grounded in sensorimotor spaces.

Human natural languages use quantifiers as ways to designate the number of objects of a set. They include numerals, such as "three", or circumscriptions, such as "a few". The latter are not only underdetermined but also context dependent. We provide a cultural-evolution explanation for the emergence of such quantifiers, focusing in particular on the role of environmental constraints on strategy choices. Through a series of situated interaction experiments, we show how a community of robotic agents can self-organize a quantification system. Different perceptions of the scene make underdetermined quantifiers useful and environments in which the distribution of objects exhibits some degree of predictability creates favorable conditions for contextdependent quantifiers.

The present paper proposes an operational semantic model of natural language quantifiers (e.g., many, some, three) and their use in quantified noun phrases. To this end we use embodied artificial agents that communicate in and interact with the physical world. We argue that existing paradigms such as Generalized Quantifiers (Barwise and Cooper 1981; Montague 1973) and Fuzzy Quantifiers (Zadeh 1983) do not provide a satisfactory models for our situated-interaction scenarios and propose a more adequate semantic model, based on fuzzy-quantification.