Jim, I really don't know if I'm finding common ground. But your words "selective compression" still read to me like saying compression must be partial. And where compression must be partial, I still think the best way will be to deal directly with uncompressed data.
The point is that if you do this in parallel there may not be any time penalty, anyway. If what you are saying is that compression must be partial, it appeals to me, because my big idea too is that compression must in general be partial. I work with algorithms which perform "selective compression" on raw data in ways which are cognitively relevant. Up to now I've been working with hierarchical structure in language, or grammar. Grammatical abstractions for natural language are also notoriously partial. I think logic will prove accessible to processing using uncompressed data too. Without time penalty if done in parallel. -Rob On Thu, Jun 8, 2017 at 1:17 AM, Jim Bromer <[email protected]> wrote: > Rob, > I am trying to make improvements on the analysis of logical statements. My > idea is that a program can analyze Satisfiabilty statements, (logical > statements where the states of some logical variables are not assigned > values), by using a system of selective compression. The problem of finding > logical values (true or false) that can satisfy a statement can become very > unwieldy in some cases. It is easy to find solutions to a logical > statement like, A V B ^C. For instance A=T, B=F, and C=T will satisfy the > statement. But with more logical variables (or literals), finding solutions > to some logical satisfiability statements can become very complicated and > time consuming. If an analytical program could operate on the compressed > data (derived from a satisfiability statement) it might be able to find > solutions to those statements faster than can be done at this time. > > Logic can be used to express many kinds of significant relations in > computer programs, so advances in finding solutions to these problems could > yield great increases in speeds and memory efficiency (because logical > satisfiability statements act just like compressions). > > Since I am talking about selective compression then if I could make any > kind of advance it would mean that I could make some general advances in > the field by showing that you can use selective strategies in compression > (by using auto-generation of ad hoc sub programs which could operate with > other individuated ad hoc sub programs). > > I was not really talking about lossy compression, but I did touch on that > subject. An advance in Satisfiability could be leveraged in lossy > compression. And as I was writing in this thread I started thinking about > the somewhat mysterious discrepancy between human recognition and recall. I > did not want to call it lossy compression exactly but if an agi program was > able to create components of recognition that relied on ranges of values > then by checking some input (the data from an ongoing event) against a > system of these components to see if there was a fit that ability might > explain the discrepancies between recognition and recall. The components of > the system would not necessarily be called lossy compression because it > might not make sense to try to decompress them. (So they would not serve as > methods to recall some event.) You would only use them to see if an input > could be fitted into them. (The system would also need some discrimination > methods as well.) The system would need some way to check all possible > combinations that might satisfy a given input so such a system would have > some of the earmarks of a satisfiability problem. That is why I thought > about this particular issue while writing this thread. One other thing > about this. This has been heavily studied using fuzzy logic, neural > networks and graph theory using weighted values (like probability > networks). However, most of those efforts have not taken discrete concepts > into account and they have not resolved the problem of using different > kinds of weighted values (what I consider to be pre-Cauchy thinking based > on an analogy from the contemporary miasma of using weighted reasoning > today to the unexplained failures in using calculus before Cauchy.) > Discrete concepts are important in recognition problems (using lossy > methods if you want to use that term) because they are needed to find other > related knowledge about the event that is occurring. However, this then > becomes a satisfiability problem. So even I were to be a modern day Cauchy > (I don't have the time to even pursue this fantasy) who could solve > contemporary dilemmas in weighted reasoning, it would not be useful without > some advances in satisfiabiilty problems. > > (I am planning on using Cauchy-appreciation in my logical satisfiabiilty > analysis.) > > I hope you can get past all my off-track tangential thinking and get what > I am saying as it relates to what you were saying. > > Jim Bromer > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
