With enough data, Cogprime should be able to make at least one guess which accounted for variables such as the cats age, when it last ate, how many mice, etc. I am confident it could do so based on how critically engrained pattern matching is in its design. Given enough contextual data such as observing cats eat nice,, knowing cats are mammals, knowing mammals only need to eat until they are full, etc. drawing a reasonable conclusion should be no major stretch for the system. That recent embodiment example in Minecraft posted a few weeks ago (the one with multiple bots, keys, chests, and animals) demonstrated the system's ability to assume truths based on fuzzy but similar observations. Of course, the variables it accounts for in its prediction would have to be variables it knows may have impact. E.g. if it had never seen a cat eat a mouse, it may not assume that it would eat mice in this instance.
Tl;dr. I believe we have a solid contender so long as we stick to the basis of patterns and sufficiently educate our system both explicitly and through its own observations. -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/b40a25d7-a3a9-4406-8ae8-ee54fb025462%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
