Abstract
We argue that computational modelling of perception, action, language, and cognition introduces several requirements on a formal semantic theory and its practical implementations. Using examples of semantic representations of spatial descriptions we show how Type Theory with Records (TTR) satisfies these requirements.
Highlights
We argue that computational modelling of perception, action, language, and cognition introduces several requirements of a formal semantic theory and its practical implementations in situated dialogue agents
We consider the treatment of spatial language from the perspective of a robot learning spatial concepts and classifying situations according to the spatial relations holding between objects while interacting with a human conversational partner
We start from our experience of building such agents and a conclusion that there is a need for a unified knowledge representation system that connects theories of meaning from formal semantics to practical implementations
Summary
Spatial language is central for situated agents as these must resolve their meaning and reference to visual scenes when being involved in conversations with humans. The preceding discussion demonstrates that the semantics of spatial descriptions involves meaning representations at three distinct levels none of which have been so far captured in a single representational framework which could be employed with situated conversational agents. While it would be possible to represent the referential semantics of on in a model by listing a set of all coordinates of locations where this spatial description applies, this referential representation of meaning is cumbersome as the model would have to include an assignment for every scale, for every spatial relation, for every pair of objects.
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