Chaitin's construction
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Contents |
1 Introduction
Are there any real numbers which are defined exactly, but cannot be computed? This question leads us to exact real arithmetic, and algorithmic information theory, and foundations of mathematics and computer science.
See Wikipedia article on Chaitin's construction, referring to e.g.
- Computing a Glimpse of Randomness (written by Cristian S. Calude, Michael J. Dinneen, and Chi-Kou Shu)
- Omega and why math has no TOEs (Gregory Chaitin).
2 Basing it on combinatory logic
Some more direct relatedness to functional programming: we can base Ω on combinatory logic (instead of a Turing machine).
2.1 Coding
See the prefix coding system described in Binary Lambda Calculus and Combinatory Logic (page 20) written by John Tromp:
of course, c, d are meta-variables, and also some other notations are changed slightly.
2.2 Decoding
Having seen this, decoding is rather straightforward. Here is a parser for illustration, but it serves only didactical purposes: it will not be used in the final implementation, because a good term generator makes parsing superfluous at this task.
2.3 Chaitin's construction
Now, Chaitin's construction will be here
where
- hnf
- should denote an unary predicate “has normal form” (“terminates”)
- dc
- should mean an operator “decode” (a function from finite bit sequences to combinatory logic terms)
- should denote the set of all finite bit sequences
- Dom_{dc}
- should denote the set of syntactically correct bit sequences (semantically, they may either terminate or diverge), i.e. the domain of the decoding function, i.e. the range of the coding function. Thus,
- “Absolute value”
- should mean the length of a bit sequence (not combinatory logic term evaluation!)
2.4 Table for small legths
Length (n) | All strings (2^{n}) | Decodable strings, ratio, their sum till now | Terminating, ratio, their sum till now |
---|---|---|---|
0 | 1 | 0, 0, 0 | 0, 0, 0 |
1 | 2 | 0, 0, 0 | 0, 0, 0 |
2 | 4 | 2, , | 2, , |
3 | 8 | 0, 0, | 0, 0, |
4 | 16 | 0, 0, | 0, 0, |
5 | 32 | 4, , | 4, , |
3 Eliminating any concept of code by handling combinatory logic terms directly
We can avoid referring to any code notion, if we transfer (lift) the notion of “length” from bit sequences to combinatory logic terms in an appropriate way. Let us call it the “norm” of the term:
where
Thus, we have no notions of “bit sequence”,“code”, “coding”, “decoding” at all. But their ghosts still haunt us: the definition of norm function looks rather strange without thinking on the fact that is was transferred from a concept of coding.
3.1 More natural norm functions (from CL terms)
Question: If we already move away from the approaches referring to any code concept, then could we define norm in other ways? E.g.
And is it worth doing it at all? The former one, at leat, had a good theoretical foundation (based on analysis, arithmetic and probability theory). This latter one is not so cleaner, that we should prefer it, so, lacking theoretical grounds.
What I really want is to exclude the (IMHO) underestimation of this “probability of termination” number -- an underestimation coming from taking into account the syntactically non-correct codes (IMHO). Thus taking only termination vs nontermination into account, when calculating this number (which can be interpreted as a probability).
3.1.1 Table for simpler CL-terms
Let us not take into account coding and thus excluding the notion of “syntactically incorrect coding” even conceptually. Can we guess a good norm?
Maximal depth, vertices, edges | Leafs, branches | Binary tree pattern | So many CL-terms = how to count it | Terminating, ratio | So many till now, ratio till now |
---|---|---|---|---|---|
0, 1, 0 | 1, 0 | 2 = 2 | 2, 1 | 2, 1 | |
1, 3, 2 | 2, 1 | 4, 1 | 6, 1 | ||
2, 5, 4 | 3, 2 | 8, 1 | 14, 1 | ||
2, 5, 4 | 3, 2 | 8, 1 | 22, 1 | ||
2, 7, 6 | 4, 3 | 16, 1 | 38, 1 |
UNIQ1ad6728a66fdf259-math-0000002D-QINUUNIQ1ad6728a66fdf259-math-0000002E-QINUUNIQ1ad6728a66fdf259-math-0000002F-QINUUNIQ1ad6728a66fdf259-math-00000030-QINUUNIQ1ad6728a66fdf259-math-00000031-QINU
4 Implementation
To do: Writing a program in Haskell -- or in combinatory logic:-) -- which could help in making conjectures on combinatory logic-based Chaitin's constructions. It would make only approximations, in a similar way that most Mandelbrot plotting softwares work. The analogy:
- they ask for a maximum limit of iterations, so that they can make a conjecture on convergence of a series;
- this program will ask for the maximum limit of reducton steps, so that it can make a conjecture on termination (having-normal-form) of a CL term.
Explanation for this: non-termination of each actually examined CL-term cannot be proven by the program, but a good conjecture can be made: if termination does not take place in the given limit of reduction steps, then the actually examined CL-term is regarded as non-terminating.
4.1 Architecture
A CL term generator generates CL terms in “ascending order” (in terms of a theoretically appropriate “norm”), and by computing the norm of each CL-term, it approximates Chaitin's construct (at a given number of digits, and according to the given maximal limit of reduction steps).
4.2 User interface
chaitin --model-of-computation=cl --encoding=tromp --limit-of-reduction-steps=500 --digits=9 --decimal chaitin --model-of-computation=cl --encoding=direct --limit-of-reduction-steps=500 --digits=9 --decimal
4.3 Term generator
module CLGen where import Generator (gen0) import CL (k, s, apply) direct :: [CL] direct = gen0 apply [s, k]
See combinatory logic term modules here.
module Generator (gen0) where import PreludeExt (cross) gen0 :: (a -> a -> a) -> [a] -> [a] gen0 f c = gen f c 0 gen :: (a -> a -> a) -> [a] -> Integer -> [a] gen f c n = sizedGen f c n ++ gen f c (succ n) sizedGen :: (a -> a -> a) -> [a] -> Integer -> [a] sizedGen f c 0 = c sizedGen f c (n + 1) = map (uncurry f) $ concat [sizedGen f c i `cross` sizedGen f c (n - i) | i <- [0..n]]
module PreludeExt (cross) where cross :: [a] -> [a] -> [(a, a)] cross xs ys = [(x, y) | x <- xs, y <- ys]
5 Related concepts
6 To do
- Making tasks described in #Implementation
- Making more natural norm functions (from CL-terms), see #More natural norm functions (from CL terms)