A Numerical Method for the Evaluation of Kolmogorov Complexity, An alternative to lossless compression algorithms

A Numerical Method for the Evaluation of Kolmogorov Complexity Hector Zenil Amphith´ˆtre Alan M. Turing ea Laboratoire d’Informatique Fondamentale de Lille (UMR CNRS 8022) Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 1 / 39

Foundational AxisAs pointed out by Greg Chaitin (thesis report of H. Zenil): The theory of algorithmic complexity is of course now widely accepted, but was initially rejected by many because of the fact that algorithmic complexity is on the one hand uncomputable and on the other hand dependable on the choice of universal Turing machine.This last drawback is specially restrictive for real world applicationsbecause the dependency is specially true for short strings, and a solutionto this problem is at the core of this work. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 2 / 39

Foundational Axis (cont.)The foundational departure point of the thesis is based in a rather butapparent contradiction, pointed out by Greg Chaitin (same thesis report): ... the fact that algorithmic complexity is extremely, dare I say violently, uncomputable, but nevertheless often irresistible to apply ... Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 3 / 39

Algorithmic ComplexityFoundational Notion A string is random if it is hard to describe. A string is not random if it is easy to describe.Main Idea The theory of computation replaces descriptions with programs. It constitutes the framework of algorithmic complexity: description ⇐⇒ computer program Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 4 / 39

Algorithmic Complexity (cont.)Deﬁnition[Kolmogorov(1965), Chaitin(1966)] K (s) = min{|p|, U(p) = s}The algorithmic complexity K (s) of a string s is the length of the shortestprogram p that produces s running on a universal Turing machine U.The formula conveys the following idea: a string with low algorithmiccomplexity is highly compressible, as the information that it contains canbe encoded in a program much shorter in length than the length of thestring itself. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 5 / 39

Algorithmic RandomnessExampleThe string 010101010101010101 has low algorithmic complexity because itcan be described as 18 times 01, and no matter how long it grows inlength, if the pattern repeats the description (k times 01) increases onlyby about log (k), remaining much shorter than the length of the string.ExampleThe string 010010110110001010 has high algorithmic complexity becauseit doesn’t seem to allow a (much) shorter description other than the stringitself, so a shorter description may not exist. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 6 / 39

Example of an evaluation of KThe string 01010101...01 can be produced by the following program:Program A:1: n:= 02: Print n3: n:= n+1 mod 24: Goto 2The length of A (in bits) is an upper bound of K (010101...01).Connections to predictability: The program A trivially allows a shortcut tothe value of an arbitrary digit through the following function f(n): if n = 2m then f (n) = 1, f (n) = 0 otherwise.Predictability characterization (Shnorr) [Downey(2010)]simple ⇐⇒ predictablerandom ⇐⇒ unpredictable Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 7 / 39

Noncomputability of KThe main drawback of K is that it is not computable and thus can only beapproximated in practice.ImportantNo algorithm can tell whether a program p generating s is the shortest(due to the undecidability of the halting problem of Turing machines).No absolute notion of randomnessIt is impossible to prove that a program p generating s is the shortestpossible, also implying that if a program is about the length of the originalstring one cannot tell whether there is a shorter program producing s.Hence, there is no way to declare a string to be truly algorithmic random. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 8 / 39

Structure vs. randomnessFormal notion of structureOne can exhibit, however, a short program generating s (much) shorterthan s itself. So even though one cannot tell whether a string is randomone can declare s not random if a program generating s is (much) shorterthan the length of s.As a result, one can only ﬁnd upper bounds of K and s cannot be morecomplex than the length of that shortest known program producing s. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 9 / 39

Most strings have maximal algorithmic complexityEven if one cannot tell when a string is truly random it is known moststrings cannot have much shorter generating programs by a simplecombinatoric argument: There are exactly 2n bit strings of length n, But there are only 20 + 21 + 22 + . . . + 2(n−1) = 2n − 1 bit strings of fewer bits. (in fact there is one that cannot be compressed even by a single bit) Hence, there are considerably less short programs than long programs.Basic notionOne can’t pair-up all n-length strings with programs of much shorter length(there simply aren’t enough short programs to encode all longer strings). Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 10 / 39

The choice of U mattersA major criticism brought forward against K is its dependence of universalTuring machine U. From the deﬁnition: K (s) = min{|p|, U(p) = s}It may turn out that: KU1 (s) = KU2 (s) when evaluated respectively using U1 and U2 .Basic notionThis dependency is particularly troubling for short strings, shorter than forexample the length of the universal Turing machine on which K of thestring is evaluated (typically in the order of hundreds of bits as originallysuggested by Kolmogorov himself). Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 11 / 39

The Invariance theoremA theorem guarantees that in the long term diﬀerent algorithmiccomplexity evaluations will converge to the same values as the length ofthe strings grow.TheoremInvariance theorem If U1 and U2 are two (universal) Turing machines andKU1 (s) and KU2 (s) the algorithmic complexity of a binary string s whenU1 or U2 are used respectively, there exists a constant c such that for allbinary string s: |KU1 (s) − KU2 (s)| < c (think of a compiler between 2 programming languages)Yet, the additive constant can be arbitrarily large, making unstable (if notimpossible) to evaluate K (s) for short strings. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 12 / 39

Theoretical holes 1 Finding a stable framework for calculating the complexity of short strings (one wants to have short strings like 000...0 to be always among the less algorithmic random despite any choice of machine. 2 Pathological cases: Theory says that a single bit has maximal random complexity because the greatest possible compression is evidently the bit itself (paradoxically it is the only ﬁnite string for which one can be sure it cannot be compressed further), yet one would intuitively say that a single bit is among the simplest strings.We try to ﬁll these holes by introducing the concept of algorithmicprobability as an alternative evaluation tool for calculating K (s). Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 13 / 39

Algorithmic ProbabilityThere is a measure that describes the expected output of a randomprogram running on a universal Turing machine.Deﬁnition[Levin(1977)]m(s) = Σp:U(p)=s 1/2|p| i.e. the sum over all the programs for which U (apreﬁx free universal Turing machine) with p outputs the string s and halts.m is traditionally called Levin’s semi-measure, Solomonof-Levin’ssemi-measure or the Universal distribution [Kirchherr and Li(1997)]. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 14 / 39

The motivation for Solomonoff-Levin’s m(s)Borel’s typewriting monkey metaphor1 is useful to explain the intuitionbehind m(s):If you were going to produce the digits of a mathematical constant like πby throwing digits at random, you would have to produce every digit of itsinfinite irrational decimal expansion.If you place a monkey on a typewriter (with say a 50 keys typewriter), theprobability of the monkey typing an initial segment of 2400 digits of π bychance is (1/502400 ). 1´ Emile Borel (1913) “Mćanique Statistique et Irr´versibilit´” and (1914) e e e“Le hasard”. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 15 / 39

The motivation for Solomonoﬀ-Levin’s m(s) (cont.)But if instead, the monkey is placed on a computer, the chances ofproducing a program generating the digits of π are of only 1/50158because it would take the monkey only 158 characters to produce the ﬁrst2400 digits of π using, for example, this C language code: int a = 10000, b, c = 8400, d, e, f[8401], g; main(){for(; b-c; )f[b + +] = a/5; for(; d = 0, g = c ∗ 2; c- = 14, printf(“%.4d”, e + d/a), e = d%a)for(b = c; d+ = f[b] ∗ a, f[b] = d%–g, d/ = g–, –b; d∗ = b);Implementations in any programming language, of any of the many knownformulae of π are shorter than the expansions of π and have thereforegreater chances to be produced by chance than producing the digits of πone by one. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 16 / 39

More formally saidRandomly picking a binary string s of length k among all (uniformlydistributed) strings of the same length has probability 1/2k .But the probability to ﬁnd a binary program p producing s (upon halting),among binary programs running on a Turing machine U is at least 1/2|p|such that U(p) = s (we know that such a program exists because U is auniversal Turing machine)Because |p| ≤ k (e.g. the example for π described before), a string s witha short generating program will have greater chances to have beenproduced by p rather than by writing down all k bits of s one by one.The less random a string the more likely to be produced by a shortprogram. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 17 / 39

Towards a semi-measureHowever, there is an infinite number of programs producing s, so theprobability of picking a program producing s among all possible programsis ΣU(p)=s 1/2|p| , the sum of all the programs producing s running on theuniversal Turing machine U.Nevertheless, for a measure to be a probability measure, the sum of allpossible events should add up 1. So ΣU(p)=s 1/2|p| cannot be a probabilitymeasure given that there is an infinite number of programs contributing tothe overall sum. For example, the following two programs 1 and 2 producethe string 0.1: Print 0and:1: Print 02: Print 13: Erase the previous 1and there are (countably) infinitely many more. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 18 / 39

Towards a semi-measure (cont.)So for m(s) to be a probability measure, the universal Turing machine Uhas to be a prefix-free Turing machine, that is a machine that does notaccept as a valid program one that has another valid program in itsbeginning, e.g. program 2 starts with program 1, so if program 1 is a validprogram then program 2 cannot be a valid one.The set of valid programs is said to form a prefix-free set, that is noelement is a prefix of any other, a property necessary to keep0 < m(s) < 1. For more details see (Kraft’s inequality [Calude(2002)]).However, some programs halt or some others don’t (actually, most do nothalt), so one can only run U and see what programs produce scontributing to the sum. It is said then, that m(s) is semi-computablefrom below, and therefore is considered a probability semi-measure (asopposed to a full measure). Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 19 / 39

Some properties of m(s)Solomonoﬀ and Levin proved that, in absence of any other information,m(s) dominates any other semi-measure and is, therefore, optimal in thissense (hence also its universal adjective).On the other hand, the greatest contributor in the summation of programsΣU(p)=s 1/2|p| is the shortest program p, given that it is when thedenominator 2|p| reaches its smallest value and therefore 1/2|p| its greatestvalue. The shortest program p producing s is nothing but K (s), thealgorithmic complexity of s. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 20 / 39

The coding theoremThe greatest contributor in the summation of programs ΣU(p)=s 1/2|p| isthe shortest program p, given that it is when the denominator 2|p| reachesits smallest value and therefore 1/2|p| its greatest value. The shortestprogram p producing s is nothing but K (s), the algorithmic complexity ofs. The coding theorem [Levin(1977), Calude(2002)] describes thisconnection between m(s) and K (s):Theorem K (s) = −log2 (m(s)) + cNotice that the coding theorem reintroduces an additive constant! Onemay not get rid of it, but the choices related to m(s) are much lessarbitrary than picking a universal Turing machine directly for K (s). Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 21 / 39

An additive constant in exchange for a massivecomputationThe trade-off this is, however, that the calculation of m(s) requires anextraordinary power of computation.As pointed out by J.-P. Delahaye concerning our method (Pour LaScience, No. 405 July 2011 issue): Comme les durés ou les longueurs tr`s petites, les faibles e e complexit´s sont d´licates ` ´valuer. Paradoxalement, les e e ae m´thodes d’´valuation demandent des calculs colossaux. e eThe first description of our approach was published in Greg Chaitin’sfestchrift volume for his 60th. anniversary: J-P. Delahaye & H. Zenil,“On the Kolmogorov-Chaitin complexity for short sequences,” Randomness andComplexity: From Leibniz to Chaitin, edited by C.S. Calude, World Scientific,2007. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 22 / 39

Calculating an experimental mMain ideaTo evaluate K (s) one can calculate m(s). m(s) is more stable than K (s)because one makes less arbitrary choices on a Turing machine U.DeﬁnitionD(n) = the function that assigns to every ﬁnite binary string s thequotient:(# of times that a machine (n,2) produces s) / (# of machines in (n,2)).D(n) is the probability distribution of the strings produced by all n-state2-symbol Turing machines (denoted by (n,2)).Examples for n = 1, n = 2 (normalized by the # of machines thathalt) D(1) = 0 → 0.5; 1 → 0.5 D(2) = 0 → 0.328; 1 → 0.328; 00 → .0834 . . . Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 23 / 39

Calculating an experimental m (cont.)Deﬁnition[T. Rad´(1962)] oA busy beaver is a n-state, 2-color Turing machine which writes amaximum number of 1s before halting or performs a maximum number ofsteps when started on an initially blank tape before halting.Given that the Busy Beaver function values are known for n-state 2-symbolTuring machines for n = 2, 3, 4 we could compute D(n) for n = 2, 3, 4.We ran all 22 039 921 152 two-way tape Turing machines starting with atape ﬁlled with 0s and 1s in order to calculate D(4)2TheoremD(n) is noncomputable (by reduction to Rado’s Busy Beaver problem). 2 A 9-day calculation on a single 2.26 Core Duo Intel CPU. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 24 / 39

Complexity TablesTable: The 22 bit-strings in D(2) from 6 088 (2,2)-Turing machines that halt.[Delahaye and Zenil(2011)] 0 → .328 010 → .00065 1 → .328 101 → .00065 00 → .0834 111 → .00065 01 → .0834 0000 → .00032 10 → .0834 0010 → .00032 11 → .0834 0100 → .00032 001 → .00098 0110 → .00032 011 → .00098 1001 → .00032 100 → .00098 1011 → .00032 110 → .00098 1101 → .00032 000 → .00065 1111 → .00032Solving degenerate cases“0” is the simplest string (together with “1”) according to D. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 25 / 39

Partial D(4) (top strings) Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 26 / 39

From a Prior to an Empirical DistributionWe see algorithmic complexity emerging: 1 The classification goes according to our intuition of what complexity should be. 2 Strings are almost always classified by length except in cases in which intuition justifies they should not. For ex. even though 0101010 is of length 7, it came better ranked than some strings of length shorter than 7. One sees emerging the low random complexity of 010101... as a simple string.From m to DUnlike m, D is an empirical distribution and no longer a prior. Dexperimentally confirms the intuition behind Solomonoff and Levin’smeasure.Full tables are available online: www.algorithmicnature.org Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 27 / 39

Miscellaneous facts from D(3) and D(4) There are 5 970 768 960 machines that halt among the 22 039 921 152 in (4,2). That is a fraction of 0.27 halt. Among the most random looking group strings from D(4) there are : 0, 00, 000..., 01, 010, 0101, etc. Among the most random looking strings one can ﬁnd: 1101010101010101, 1101010100010101, 1010101010101011 and 1010100010101011, each with frequency of 5.4447×10−10 . As in D(3), where we reported that one string group (0101010 and its reversion) climbed positions, in D(4) 399 strings climbed to the top and were not sorted among their length groups. In D(4) string length was no longer a classiﬁcation determinant. For example, between positions 780 and 790, string lengths are: 11, 10, 10, 11, 9, 10, 9, 9, 9, 10 and 9 bits. D(4) preserves the string order of D(3) except in 17 places out of 128 strings in D(3) ordered from highest to lowest string frequency. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 28 / 39

Connecting D back to mTo get m we replaced a uniform distribution of bits composing strings to auniform distribution bits composing programs. Imagine that your(Turing-complete) programming language allows a monkey to producerules of Turing machines at random, every time that the monkey types avalid program it is executed.At the limit, the monkey (which is just a random source of programs) willend up covering a sample of the space of all possible Turing machine rules. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 29 / 39

Connecting D back to mOn the other hand, D(n) for a fixed n is the result of running all n-state2-symbol Turing machines according to an enumeration.An enumeration is just a thorough sample of the space of all n-state2-symbol Turing machines each with fixed probability1/(# of Turing machines in (n,2)) (by definition of enumeration).D(n) is therefore, a legitimate programmer monkey experiment. Theadditional advantage of performing a thorough sample of Turing machinesby following an enumeration is that the order in which the machines aretraversed in the enumeration is irrelevant as long as one covers all theelements of a (n,2) space. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 30 / 39

Connecting D back to m (cont.)One may ask why shorter programs are favored.The answer, in analogy to the monkey experiment, is based on the uniformrandom distribution of keystrokes: programs cannot be that long withouteventually containing the ending program keystroke. One can still thinkthat one can impose a diﬀerent distribution of the program instructions,for ex. changing the keyboard distribution repeating certain keys.Choices other than the uniform are more arbitrary than just assuming noadditional information, and therefore a uniform distribution (a keyboardwith two or more letter “a”’s rather than the usual one seems morearbitrary than having a key per letter). Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 31 / 39

Connecting D back to m (cont.)Every D(n) is a sample of D(n + 1) because (n + 1, 2) contains allmachines in (n, 2). We have empirically tested that strings sorted byfrequency in D(4) preserve the order of D(3) which preserves the order ofD(2), meaning that longer programs do not produce completely differentclassifications. One can think of the sequence D(1), D(2), D(3), D(4), . . .as samples which values are approximations to m.One may also ask, how can we know whether a monkey provided with adifferent programming language would produce a completely different D,and therefore yet another experimental version of m. That may be thecase, but we have also shown that reasonable programming languages(e.g. based on cellular automata and Post tag systems) producereasonable (correlated) distributions. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 32 / 39

Connecting D back to m (cont.) Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 33 / 39

m(s) provides a formalization for Occam’s razorThe immediate consequence of algorithmic probability is simple butpowerful (and surprising):Basic notion Type-writing monkeys (Borel) garbage in → garbage out Programmer monkeys: (Bennett, Chaitin) garbage in → structure out Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 34 / 39

What m(s) may tell us about the physical world?Basic notionm(s) tells that it is unlikely that a Rube Goldberg machine produces astring if the string can be produced by a much simpler process.Physical hypothesism(s) would tell that, if processes in the world are computer-like, it isunlikely that structures are the result of the computation of a RubeGoldberg machine. Instead, they would rather be the result of the shortestprograms producing that structures and patterns would follow thedistribution suggested by m(s). Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 35 / 39

On the algorithmic nature of the worldCould it be that m(s) tells us how structure in the world has come to beand how is it distributed all around? Could m(s) reveal the machinerybehind?What happens in the world is often the result of an ongoing (mechanical)process (e.g. the Sun rising due to the mechanical celestial dynamics ofthe solar system).Can m(s) tell something about the distribution of patterns in the world?We decided to see so we got some empirical datasets from the physicalworld and made a comparison against data produced by pure computationthat by deﬁnition should follow m(s).The results were published in H. Zenil & J-P. Delahaye, “On theAlgorithmic Nature of the World”, in G. Dodig-Crnkovic and M. Burgin (eds),Information and Computation, World Scientiﬁc, 2010. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 36 / 39

On the algorithmic nature of the world Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 37 / 39

ConclusionsOur method aimed to show that reasonable choices of formalisms forevaluating the complexity of short strings through m(s) give consistentmeasures of algorithmic complexity. [Greg Chaitin (w.r.t our method)] ...the dreaded theoretical hole in the foundations of algorithmic complexity turns out, in practice, not to be as serious as was previously assumed.Our method also seems notable in that it is an experimental approach thatcomes into the rescue of the apparent holes left by the theory. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 38 / 39

Bibliography C.S. Calude, Information and Randomness: An Algorithmic Perspective (Texts in Theoretical Computer Science. An EATCS Series), Springer, 2nd. edition, 2002. G. J. Chaitin. On the length of programs for computing ﬁnite binary sequences. Journal of the ACM, 13(4):547–569, 1966. G. Chaitin, Meta Math!, Pantheon, 2005. R.G. Downey and D. Hirschfeldt, Algorithmic Randomness and Complexity, Springer Verlag, to appear, 2010. J.P. Delahaye and H. Zenil, On the Kolmogorov-Chaitin complexity for short sequences, in Cristian Calude (eds) Complexity and Randomness: From Leibniz to Chaitin. World Scientiﬁc, 2007. J.P. Delahaye and H. Zenil, Numerical Evaluation of Algorithmic Complexity for Short Strings: A Glance into the Innermost Structure of Randomness, arXiv:1101.4795v4 [cs.IT]. Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 39 / 39

C.S. Calude, M.A. Stay, Most Programs Stop Quickly or Never Halt,2007.W. Kirchherr and M. Li, The miraculous universal distribution,Mathematical Intelligencer , 1997.A. N. Kolmogorov. Three approaches to the quantitative deﬁnition ofinformation. Problems of Information and Transmission, 1(1):1–7,1965.P. Martin-L¨f. The deﬁnition of random sequences. Information and oControl, 9:602–619, 1966.L. Levin, On a concrete method of Assigning Complexity Measures,Doklady Akademii nauk SSSR, vol.18(3), pp. 727-731, 1977.L. Levin., Universal Search Problems., 9(3):265-266, 1973.(submitted: 1972, reported in talks: 1971). English translation in:B.A.Trakhtenbrot. A Survey of Russian Approaches to Perebor(Brute-force Search) Algorithms. Annals of the History of Computing6(4):384-400, 1984.Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 39 / 39

M. Li, P. Vitńyi, An Introduction to Kolmogorov Complexity and Its aApplications,, Springer, 3rd. Revised edition, 2008.S. Lloyd, Programming the Universe: A Quantum Computer ScientistTakes On the Cosmos, Knopf Publishing Group, 2006.T. Rad´, On non-computable functions, Bell System Technical oJournal, Vol. 41, No. 3, 1962.R. J. Solomonoff. A formal theory of inductive inference: Parts 1 and2. Information and Control, 7:1–22 and 224–254, 1964.H. Zenil and J.P. Delahaye, On the Algorithmic Nature of the World,in G. Dodig-Crnkovic and M. Burgin (eds), Information andComputation, World Scientific, 2010.S. Wolfram, A New Kind of Science, Wolfram Media, 2002.Hector Zenil (LIFL) A Numerical Method for the Evaluation of Kolmogorov Complexity 39 / 39

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A Numerical Method for the Evaluation of Kolmogorov Complexity, An alternative to lossless compression algorithms

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