I constrained mutations to "algebraic identities" -- which constrains the paths to the search space. So the evolutionary program synthesis is more along the lines of a computer algebra system searching for a proof.
Which brings up related idea I proposed to the Maxima (Macsyma) folks back when DeepMind was making its first inroads into the game of go: If you gather user interactions with a computer algebra system like Macsyma the way you gather moves in a game, you might be able to build up enough of a database to train something like Alpha Go to do algebra in a reasonably intelligent manner that doesn't get stuck in local minima -- you know -- like sacrificing pieces for strategic advantage. Of course, there is also AlphaGo Zero which purportedly required no database of human game play... On Tue, Mar 23, 2021 at 6:39 PM Ben Goertzel <[email protected]> wrote: > Hmm, well if you just use mutations then this becomes a greedy > algorithm which will either get stuck in local optima or take > close-to-forever > > If you use crossover operators or EDA-style probabilistic models then > things become potentially tractable, but only under appropriate > assumptions regarding the fitness landscape... right? > > On Tue, Mar 23, 2021 at 2:15 PM James Bowery <[email protected]> wrote: > > > > Evolutionary program synthesis requires a fitness/cost function which, > in the case of Solomonoff Induction, can be approximated by the size of the > program that outputs exactly the currently known observations. The obvious > problem with this approach is that of all algorithms, only a disappearingly > small fraction will output exactly the known observations. > > > > Reduce the space by starting with the known observations as an > executable literal -- say by putting it in quotes for evaluation -- and use > a reversible programming language with its algebraic identities as > mutations -- treating the "discarded" bits (inherent in reversible > algorithms) as needing compression as well. In the limit, this can be > represented as a directed cyclic graph of reversible logic gates which will > tend to configure in such a way as to make the "heat" bits highly > compressible (and in the limit, all 0s or all 1s). > > > > This originally occurred to me prior to the announcement of the Hutter > Prize back in 2006 but Matt had some argument debunking this approach. > > > > PS: It was rather ironic that one of the first and most vocal critics > of The Hutter Prize was the inventor of the Kayak reversible programming > language. > > Artificial General Intelligence List / AGI / see discussions + > participants + delivery options Permalink > > -- > Ben Goertzel, PhD > http://goertzel.org > > “He not busy being born is busy dying" -- Bob Dylan ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tf8bb7754cbb517a4-M9f826fbb20b2d1d9be8916fa Delivery options: https://agi.topicbox.com/groups/agi/subscription
