On Tue, Jun 20, 2017 at 5:59 AM, Linas Vepstas <[email protected]> wrote: >> , and see how your grammar+semantic approach will be effective (adding >> somehow a non-linear embedding in the phase space as I already discussed >> with Ben) > > > Ben has not yet relayed this to me. > > -- Linas
Yeah, it seemed you were already pretty busy! The short summary is: For any complex dynamical system, if one embeds the system's states in a K-dimensional space appropriately, and then divides the relevant region of that K-dimensional space into discrete cells... then each trajectory of that system becomes a series of "words" in a certain language (where each of the discrete cells corresponds to a word)... I guess you are probably familiar with this technique, which is "symbolic dynamics" One can then characterize a dynamical system, in various ways, via the inferred grammar of this "symbolic-dynamical language" ... I did work on this a couple decades ago using various Markovian grammar inference tools I hacked myself... Enzo at Cisco, as it turns out, had been thinking about applying similar methods to characterize the complex dynamics of some Cisco networks... So we have been discussing this as an interesting application of the OpenCog-based grammar inference tools we're now developing ... There's plenty more, but that's the high-level summary... (Part of the "plenty more" is that there may be a use of deep (or shallow, depending on the case) neural networks to help with the initial stage where one embeds the complex system's states in a K-dimensional space. In a different context, word2vec and adagram are examples of the power of modern NNs for dimensional embedding.) -- Ben -- Ben Goertzel, PhD http://goertzel.org "I am God! I am nothing, I'm play, I am freedom, I am life. I am the boundary, I am the peak." -- Alexander Scriabin -- 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/CACYTDBeYGVPZJo3OeV3sajuPgaosg9nbBiurttsVU%3Dz23pSg7w%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
