We've talked with Kate, aka Miss Metaverse from BodAI before… now we're joined by Zak from BodAI to talk about the virtual lab where they're building their companion AI and how the future of virtual reality is going to open new worlds and possibilities for all of us. And a bit about SEX ROBOTS too, 'cuz they're just fun to talk about.

We are developing a system that aims to perf orm the same inferences as a human reader, on car-crash reports. More precisely, we expect it to determine the causes of the accident as they appear from the text. We describe the genera l semantic framework in which our study takes place, the linguistic and semantic levels of analysis, and the inference rules used by the system.

To understand texts written in natural language (LN), we use our knowledge about the norms of the domain. Norms allow to infer more implicit information from the text. This kind of information can, in general, be defeasible, but it remains useful and acceptable while the text do not contradict it explicitly. In this paper we describe a non-monotonic reasoning system based on the norms of the car crash domain. The system infers the cause of an accident from its textual description. The cause of an accident is seen as the most specific norm which has been violated. The predicates and the rules of the system are stratified: organized on layers in order to obtain an efficient reasoning.

After examining the simplest statements thaï can be based on a verb, a noun and an adjective, we go on to more complex statements involving articles, relative clauses and négations. Emphasis is laid on the systematic transformation ofa sentence into a semantic formula, h appears that such semantic formulae can be interpre ted correctly only in a logical system wit h three truth-values. Moreover one must suppose that the elementary relations associâted wit h the verb s, nouns and adjective s range not on individuals, but on sets of individuals.