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VDIFF. Step A calculates the amount by which the rich have been made poorer, and uses this to make the
ti-participant virtual environment (like the Multiverse environment EFTA00624280 134 26 Perceptual and Motor Hierarchies currently used with the PetBrain) or a small-scale robot simulator. Our experiments with virtual worlds so far have used the high-level approach described here; but we are also expe
29.7 Conclusion 189 flexibly interacting with a rich stream of data from the everyday human world. Thi
C teaching protocol is to give a brief snippet from a script that was created to guide the actual training of the virtual animals controlled by the PetBrain. This snippet involves only I and It; the C will be discussed afterwards. This snippet demonstrates a teaching methodology that involves two avatar
s. - We have not yet thought through exactly how rich the semantic formalism should be for handling th
le Experiments with Embodiment and Anaphor Resolution Now we turn to some fairly simple practical work that was done in 2008 with the OpenCog-based PetBrain software, involving the use of virtually embodied experience to help with interpretation of linguistic utterances. This work has been superseded som
ine instances of matter into new ones in a fairly rich way: e.g. glue or tie solids togethermix liquids
n used to control virtual agents in virtual worlds IGEA081. Prototype work done during 2007-2008 involved using an OpenCog variant called the Open- PetBrain to control virtual dogs in a virtual world (see Figure 6.6 for a screenshot of an OpenPetBrain-controlled virtual dog). While these OpenCog virtual
ine instances of matter into new ones in a fairly rich way: e.g. glue or tie solids togethermix liquids
en used to control virtual agents in virtual worlds [GEA08]. Prototype work done during 2007-2008 involved using an OpenCog variant called the Open- PetBrain to control virtual dogs in a virtual world (see Figure 6.6 for a screenshot of an OpenPetBrain-controlled virtual dog). While these OpenCog virtual d
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