> > > So this is an arithmetic query, rather than a spatial query -- but the two > cases > are similar in that both arithmetic operations and spatial operations are > "special domains" with their own algebras, and by using those algebras one > can answer queries in those domains more efficiently than one can do by > generic > means... >
Are you going to manually implement a special algorithm for each domain? > > So to efficiently handles queries like those you're mentioning, I would > want to > use the PLN backward chainer rather than just the PM, and have the backward > chainer perhaps connected to some computer-algebra engine as one option to > use > when encountering a GreaterThanLink ... > What rules for BC to do have in mind for this case? Let's try them and see if the solution will be O(N). Again, you just say: don't use PM with GreaterThanLinks. Then, for what reason their support is presented in PM? > > Or one could tweak the PM to use the backward chainer only when > encountering > a GreaterThanLink, and just do plain vanilla pattern matching otherwise... > PM doesn't need to know algebra to deal with this query efficiently. It just needs not to evaluate pairwise relations for every pair object, but only for objects belonging to same groups. > > It begs the question "OK but how would > something analogous to a computer-algebra engine be learned via > experience" .... > Yes, this is also the right question, although the considered problem is not necesserily related to it. > Human memory is very *constructive*. Rather than searching among stored > memories, as in a database search or whatever, the "pattern matching" done > when a human mind searches its memory is a matter of inventing memories > that match the pattern being searched for. Yeah, I know. I just tried to imagine OpenCog in place of human mind. So, does OpenCog have anything to perform memory quering? For me, PM was a natural candidate. But we can consider the backward chainer. > What human memory search does is way more like PLN abductive inference > based on the cues of stored memories (existing patterns) ... > So, you just say, we shouldn't use PM to match data pattern, but should use it to match patterns describing some general rules? > > One jewel of wisdom from Pei Wang is: Almost all algorithms used by > human-like > minds have exponential complexity in worst case.... > I doubt this is true for unconcsious algorithms. Or, at least, they are any-time algorithms. They will rather fail than run for more than a certain time. > > My gut reaction is it's perhaps often better to think about PLN > backward chainer (which uses the > URE which uses the PM)..... I.e. often, instead of thinking about > custom callbacks to the PM, > one can think about custom domain-specific inference rules to use within > PLN... > Maybe. So, what rules will work in this case? -- 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/CABpRrhyVVUX4_ohu6%2BjEMu1h6Wk%3DKj-7yhYxV5V3gQ0nk3Nucg%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
