Tag Archives: lambda calculus

Divination table for anharmonic lambda

UPDATE: Second version, better: kali24.

UPDATE: first version to play here. Needs fiddling though, the termination rewrites not yet clear and it may need to pass to the full projective 24 nodes version, but I hope not. Anyway, it is surprisingly fit, seen that the background has absolutely nothing to do with lambda calculus.

The ambition is to use it for anharmonic chemlambda but IC style seems simpler.

I stare at this to generate the right choice of rewrites

zhexa

and it is much to say, but maybe you can figure it out from some links:

anharmonic group

em

shuffle trick

Lambda calculus inspires experiments with chemlambda

In the now deleted chemlambda collection I told several stories about how lambda calculus can bring inspiration for experiments with chemlambda. I select for this post a sequence of such experiments. For previous related posts here see this tag and this post.

Let’s go directly to the visuals.

Already in chemlambda v1 I remarked the interesting behaviour of the graph (or molecule) which is obtained from the lambda term of the predecessor applied to a Church number.  With the deterministic greedy algorithm of reductions, after the first stages of reduction there is a repeating pattern of  reduction, (almost) up to the end. The predecessor applied to the Church number molecule looks almost like a closed loop made of pairs A-FO (because that’s how a Church number appears in chemlambda), except a small region which contains the graph of the predecessor, or what it becomes after few rewrites.

In chemlambda v2 we have two kinds of fanouts: FO and FOE.  The end result of the reduction of the same molecule, under the same algorithm, is different: where in chemlambda v1 we had FO nodes (at the end of the reduction), now we have FOE nodes. Other wise there’s the same phenomenon.

Here is it, with black and white visuals

pred

Made by recording of this live (js) demo.

1. What happens if we start not from the initial graph, but from the graph after a short number of rewrites? We have just to cut the “out” root of the initial graph, and some nodes from it’s neighbourhood and glue back, so that we obtain a repeating pattern walking on a circular train track.

Here is it, this time with the random reduction algorithm:

bigpred_train-opt

I previously called this graph an “ouroboros”. Or a walker.

2. That is interesting, it looks like a creature (can keep it’s “presence”) which walks in a single direction in a 1-dimensional world.  What could be the mechanism?

Penrose comes to mind, so in the next animation I also use a short demonstration from a movie by Penrose.

bigpred_penrose-opt

 

3. Let’s go back to the lambda calculus side and recall that the algorithm for the translation of a lambda term to a chemlambda molecule is the same as the one from GLC, i.e the one from Section 3 here. There is a freedom in this algorithm, namely that trees of FO nodes can be rewired as we wish. From one side this is normal for GLC and chemlambda v1,  which have the CO-COMM and CO-ASSOC rewrites

convention_3

In chemlambda v2 we don’t have these rewrites at all, which means that in principle two diferent molecules,  obtained from the same lambda term, which differ only by the rewiring of the FO nodes may reduce differently.

In our case it would be interesting to see if the same is true for the FOE nodes as well. For example, remark that the closed loop, excepting the walker, is made by a tree of FOE nodes, a very simple one. What happens if we perturb this tree, say by permuting some of the leaves of the tree, i.e. by rewiring the connections between FOE and A nodes.

bigpred_train_perm-opt

The “creature” survives and now it walks in a world which is no longer 1 dimensional.

Let’s play more: two permutations, this time let’s not glue the ends of the loop:

bigpred_train_egg

It looks like a signal transduction from the first glob to the second. Can we make it more visible, say by making invisible the old nodes and visible the new ones? Also let’s fade the links by making them very large and almost transparent.

bigpred_train_egg_mist_blue

Signal transduction! (recall that we don’t have a proof that indeed two molecules from the same lambda term, but with rewired FO trees reduce to the same molecule, actually this is false! and true only for a class of lambda terms. The math of this is both fascinating and somehow useless, unless we either use chemlambda in practice or we build chemlambda-like molecular computers.)

4.  Another way to rewire the tree of FOE nodes is to transform it into another tree with the same leaves.

bigpred_tree-opt

 

5. Wait, if we understand how exactly this works, then we realize that we don’t really need this topology, it should also work for topologies like generalized Petersen graphs, for example for a dodecahedron.

dodecahedron_walker

 

This is a walker creature which walks in a dodecaheral “world”.

6. Can the creature eat? If we put something on it’s track, see if it eats it and if it modifies the track, while keeping it’s shape.

walker_bit-opt

So the creature seems to have a metabolism.

We can use this for remodeling the world of the creature. Look what happens after many passes of the creature:

walker_bit_new

 

7. What if we combine the “worlds” of two creatures, identical otherwise. Will they survive the encounter, will they interact or will they pass one through the other like solitons?

bigpred_bif

 

Well, they survive. Why?

8. What happens if we shorten the track of the walker, as much as possible? We obtain a graph wit the following property: after one (or a finite give number of) step of the greedy deterministic algorithm we obtain an isomorphic graph. A quine! chemlambda quine.

At first, it looks that we obtained a 28 nodes quine. After some analysis we see that we can reduce this quine to a 20 nodes quine. A 20-quine.

Here is the first observation of the 20-quine under the random algorithm

20_quine_50steps

According to this train of thoughts, a chemlambda quine is a graph which has a periodic evolution under the greedy deterministic algorithm, with the list of priority of rewrites set to DIST rewrites (which add nodes)  with greater priority than beta and FI-FOE rewrites (which subtract ndoes), and which does not have termination nodes (because it leads to some trivial quines).

These quines are interesting under the random reduction algorithm, which transform them into mortal living creatures with a metabolism.

____________

So this is an example of how lambda calculus can inspire chemlambda experiments, as well as interesting mathematical questions.

Graphic lambda calculus and chemlambda(III)

This post introduces chemlambda v2. I continue from the last post, which describes the fact that chemlambda v1, even if it has only local rewrites, it is not working well when used with the dumbest possible reduction algorithms.

Nature has to work with the dumbest algorithms, or else we live in a fairy tale.

Chemlambda v2 is an artificial chemistry, in the following sense:

  • it is a graph rewrite system over oriented fatgraphs made of a finite number of nodes, from the list: 5 types of 3-valent nodes, A (application), L (lambda abstraction), FO (fanout), FI (fanin), FOE (external fanout), 1 type of 2-valent node Arrow, 3 types of 1-valent nodes, FRIN (free in), FROUT (free out), T (termination). Compared to chemlambda v1, there is a new node, the FOE. The nodes, not the rewrites, are described in this early explanation called Welcome to the soup. (Mind that the gallery of example which is available at the end of these explanation mixes chemlambda v1 and chemlambda v2 examples. I updated the links so that is no longer pointing to this very early gallery of examples. However if you like it here is it.)
  • the rewrites CO-COMM and CO-ASSOC of chemlambda v1 are not available, instead there are several new DIST rewrites: FO-FOE, L-FOE, A-FOE, FI-FO, and a new beta like rewrite FI-FOE. As in chemlambda v1, the patterns of the rewrites fit with the interaction combinator rewrites if we forget the orientation of the edges, but the 3-valent nodes don’t have a principal port, so they don’t form interaction nets. Moreover, the are conflicts among the rewrites, i.e. there are configurations of 3 nodes such that we have a node which belongs to two pairs of nodes which may admit rewrites. The order of application of rewrites may matter for such conflicts.
  • there is an algorithm of application of rewrites, which is either the deterministic greedy algorithm with a list of priority of rewrites (for example beta rewrites have priority over DIST rewrites, whenever there is a conflict), or the random application algorithm.

 

Sources for chemlambda v2:

 

The goal of chemlambda v2: to explore the possibility of molecular computers in this artificial chemistry.

This needs explanations. Indeed,  does the system work with the simplest random algorithm? We are not interested into semantics, because it is, or it relies on global notions, We are not (very) interested into reduction strategies for lambda terms, because they are not as simple as the dumbest algorithms we use here. Likewise for readback, etc.

So, does chemlambda v2 work enough for making molecular computers?  Pure untyped lambda calculus reduction problems are an inspiration. If the system works for the particular case of graphs related to lambda terms then this is a bonus for this project.

As you see, instead of searching for an algorithm which could implement, decentralized say, a lambda calculus reduction strategy, we ask if a particular system reduces (graphs related to) terms with one algorithm from the fixed class of dumbest ones.

That is why the universality in the sense of Lafont is fascinating. In this post I argued that Lafont universality property of interaction combinators means, in this pseudo-chemical sense, that the equivalent molecular computer based on interaction combinators reactions (though not the translations) works for implementing a big enough class of reactions which are Turing universal in particular (Lafont  shows concretely that he can implement Turing machines).

(continues with the part IV)

Graphic lambda calculus and chemlambda (II)

Chemlambda v2 is an entirely different project than GLC and chemlambda v1. This post continues from the first part. It explains the passage towards chemlambda v2.

A problem of GLC and chemlambda v1 is that research articles are opinion pieces, not validated by programs and experiments. The attempt to use GLC with the Actor Model in order to build a decentralized computing proposal, aka distributed GLC, failed because of this. Does all of this work?

The CO-COMM and CO-ASSOC rewrites lead to the situation that,  in order to be useful, either:

  • they have to be applied by a human or by a(n unknown) very clever algorithm
  • or they are applied in both directions randomly, which implies that no GLC or chemlambda v1 reduction ever terminates.

Here is an early visual tutorial  which introduces the nodes of chemlambda v2.  At the end of it you are pointed to See also a gallery of examples which mixes chemlambda v1 with chemlambda v2, like these:

 

Or, another example, the Y combinator. In chemlambda v1, without using CO-COMM and CO-ASSOC, the Y combinator applied to an unspecified term behaves like this.  In chemlambda v2, where there is a supplimentary node and other rewrites, the Y combinator behaves almost identically, but some nodes (the yellow FOE here instead of the green FO before) are different:

 

ycombi

 

 

See this page for a list of works, tagged with the version of chemlambda used.

(continues with part III)

 

 

Graphic lambda calculus and chemlambda (I)

Looks like there is a need to make a series of posts dedicated to the people who try to use this blog as a source in order to understand graphic lambda calculus (aka GLC) and chemlambda. This is the first one.

Sources for GLC:

  • the best source is the article M. Buliga, Graphic lambda calculus. Complex Systems 22, 4 (2013), 311-360   (link to article in journal) (link to article in arXiv)
  • you can see the GLC page (link) here, which has been updated many times after chemlambda appeared, but it is unmodified starting from the section “What is graphic lambda calculus?” and there are links to many others posts here which explain GLC as it is, that is before chemlambda.

GLC is a graph rewriting system for fatgraphs made of trivalent or 1-valent nodes. The trivalent nodes used are A (application), L (lambda), FO (fanout), epsilon (for dilations). There is one 1-valent node, T (termination). Loops with no nodes and arrows, i.e. oriented edges with no nodes are accepted as well. There is no algorithm proposed for the reduction of these graphs.

The graph rewrites are:

– local ones (i.e. involving only a finite number, a priori given, of nodes and edges)

  •   graphic beta move, which is like Lamping graph rewrite, only purely local, i.e. there is no limitation on the global shape of the graph, thus it is  somehow more general than the beta rewrite from untyped lambda beta calculus; a possible interpretation is C= let x=B in A rewrites to x=B and C=A

 

  • betaCO-COMM and CO-ASSOC rewrites for the FO (fanout) which are, due to the orientation of the edges, really like graphical, AST forms of co-commutativity and co-associativity
  • local pruning group of rewrites, which describe the interaction of the trivalent nodes with the T node; incidentally T and FO interact like if T is a co-unit for FO
  • a group of rewrites for the dilation nodes, which are those of emergent algebras, which involve the fanout FO and the dilation nodes

 

– global rewrites:

  • global fan-out
  • global pruning

 

In section 3 of the article on GLC is given an algorithm of conversion of untyped lambda terms into graphs which is a little more than a modification of the AST of the lambda term, in such a way that the orientation of the edges is respected. That is because the lambda node L has one incoming edge and two outgoing edges!

I prove then that GLC can be used for untyped lambda beta calculus, but also for emergent algebras and also for a variant of knot theoretic graphs where there is no need for them to be planar. Finally, I show that there might be other “sectors” of graphs which may be interesting, regardless of their meaning (or lack of it) with respect to lambda calculus.

The problem of GLC is that it has these global rewrites. Can these be replaced by local rewrites?

That’s how chemlambda appeared.

I was aware that some applications of global fanout can be done with local rewrites, but not all. Also, the interest in GLC was not extended to the interest into emergent algebras, to my dismay.

On the other side I became obsessed with the idea that if the global fanout can be replaced by local rewrites entirely then it should be possible in principle to see the rewrites as chemical reactions between individual molecules. A bigger problem would be then: would these reactions reduce the graph-molecules under the dumbest algorithm among all, the random one? Nature functions like this, so any algorithm which would be global in some sense is completely excluded.

This led me to write the article: M. Buliga, Chemical concrete machine (link to DOI) (link to arXiv version).  This is chemlambda v1, which is actually a transition from GLC to something else, or better said to another research subject in the making.

Other sources for this mix glc-chemlambda v1:

 

Chemlambda v1, or the “chemical concrete machine”, is a graph rewriting algorithm which uses the trivalent nodes A, L, FI (fan-in), FO, and the 1-valent node T and only local rewrites:

  • the graphic beta rewrite (between the nodes L and A) and the fan-in rewrite (between the nodes FI and FO)

 

convention_2

 

  • the CO-COMM and CO-ASSOC rewrites

 

 

convention_3

 

  • two DIST (from distributivity) rewrites, for the pairs A-FO and L-FO

 

 

convention_6

  • local pruning rewrites (involving the 1-valent node T)

 

convention_4

 

  •  and elimination of loops

 

convention_5

 

As you see, all rewrites except CO-COMM and CO-ASSOC are alike the ones from Lafont article Interaction combinators, except that the graphs are directed and the nodes don’t have a principal port for interaction. Here are the interaction combinators rewrites

 

 

lafont-2

In the chemical concrete machine article are mentioned Berry and Boudol chemical abstract machine and Fontana and Buss alchemy, but not Lafont.  My fault. From what I knew then the beta rewrite came from Lamping or better Wadsworth, the fan-in came from Turaev (knotted trivalent graphs), and the DIST rewrites came from the graphical version of linear emergent algebras, or even better from the Reidemeister 3 rewrite in knot theory.

 

 

In this article there is no algorithm for application of the rewrites. (You shall see that in chemlambda v2, which is an artificial chemistry,  there are actually several, among them the deterministic greedy one, with a list of priority of the rewrites, because there are collisions otherwise, and the random one, which interested me the most.)

However I suggest that the rewrites can be seen as done in a truly chemical sense, mediated by (invisible) enzymes, each rewrite type with it’s enzyme.

I proved that we can translate from GLC to chemlambda v1 and that global fan-out and global pruning can be replaced by sequences of rewrites from chemlambda v1. There was no proof that this replacement of the global rewrites with cascades of local rewrites can be done by the algorithms considered. I proved the Turing universality by using the BCKW system of combinators.

The chemical concrete machine article ends however with:

“With a little bit of imagination, if we look closer to what TRUE, FALSE and IFTHENELSE are doing, we see that it is possible to adapt the IFTHENELSE to a molecule which releases, under the detection of one molecule (like TRUE), the ”medicine” A, and under the detection of another molecule (like FALSE) the ”medicine” B.”

The chemlambda page (link) here is reliable for chemlambda v1, but it also contain links to newer versions.

[UPDATE: I retrieved this view from 2014  of the story.]

(continues with part II)

 

Deterministic vs random, an example of grandiose shows vs quieter, functioning anarchy

In the following video you can see the deterministic, at the right random evolution of the same molecule, duplex.mol from the chemlambda repository. They take about the same time.

The deterministic one is like a ballet, it has a comprehensible development, it has rhythm and drama. Clear steps and synchronization.

The random one is more fluid, less symmetric, more mysterious.

What do you prefer, a grand synchronized show or a functioning, quieter anarchy?

Which one do you think is more resilient?

What is happening here?

The molecule is inspired from lambda calculus. The computation which is encoded is the following. Consider the lambda term for the identity function, i.e. I=Lx.x. It has the property that IA reduces to A for any term A. In the molecule it appears as a red trivalent node with two ports connected, so it looks like a dangling red globe. Now, use a tree of fanouts to multiply (replicate) this identity 8 times, then build the term

(((II)(II))((II)(II)))(((II)(II))((II)(II)))

Then use one more fanout to replicate this term into two copies and reduce all. You’ll get two I terms, eventually.
In the deterministic version the following happens.

– the I term (seen as a red dangling node) is replicated (by sequence of two rewrites, detail) and gradually the tree of fanouts is destroyed

– simultaneously, the tree of applications (i.e. the syntactic tree of the term, but seen with the I’s as leaves) replicates by the fanout from the end

– because the reduction is deterministic, we’ll get 16 copies of I’s exactly when we’ll get two copies of the application tree, so in the next step there will be a further replication of the 16 I’s into 32 and then there will be two, disconnected, copies of the molecule which represents ((II)(II))((II)(II))

– after that, this term-molecule reduces to (II)(II), then to II, then to I, but recall that there are two copies, therefore you see this twice.

In the random version everything mixes. Anarchy. Some replications of the I’s reach the tree of applications before it has finished to replicate itself, then reductions of the kind II –> I happen in the same time with replications of other pieces. And so on.
There is no separation of stages of this computation.
And it still works!

I used quiner_experia, with the mol file duplex.mol. The first time I modified all the weights to 0 (to have deterministic application) and took the rise parameter=0 (this is specific to quiner_experia, not present in quiner) too, because the rise parameter lower the probabilities of new rewrites, exponentially, during the same step, in order to give fair chances to any subset of all possible rewrites possible.
Then I made a screencast of the result, without speeding it, and by using safari to run the result.
For the random version I took all the weights equal to 1 and the rise parameter equal to 8 (empirically, this gives the most smooth evolution, for a generic molecule from the list of examples). Ran the result with safari and screencasted it.
Then I put the two movies one near the other (deterministic at left, random at right) and made a screencast of them running in parallel. (Almost, there is about 1/2 second difference because I started the deterministic one first, by hand).
That’s it, enjoy!
For chemlambda look at the trailer from the collections of videos I have here on vimeo.