1 / 25

Automated Discovery in Pure Mathematics

Automated Discovery in Pure Mathematics. Simon Colton Universities of Edinburgh and York. Overview of Talk. Some example discoveries ATP, CSP, CAS, ad-hoc methods The HR system Automated theory formation Overview of applications Application to mathematical discovery

Télécharger la présentation

Automated Discovery in Pure Mathematics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Automated Discovery in Pure Mathematics Simon Colton Universities of Edinburgh and York

  2. Overview of Talk • Some example discoveries • ATP, CSP, CAS, ad-hoc methods • The HR system • Automated theory formation • Overview of applications • Application to mathematical discovery • Finite algebras, number theory, refactorables • Demonstration • NumbersWithNames program

  3. Automated Discoveries #1 • Robbins algebras are boolean • Automated theorem proving, McCune+Wos • Quasigroup existence problems (QG6.17) • Constraint solvers, John Slaney et al. • Inconsistency in Newton’s Principia • Formal methods (NS-analysis), Fleuriot

  4. Automated Discoveries #2 • Mersenne prime: 26972593 – 1 • Distributed (internet) search, CAS • New geometry results • Chou using Wu’s method • Simple axiomatisations of algebras • Group: x(y(((zz-1)(uy)-1)x))-1=u • McCune and Kunen, ATP

  5. Automated Discoveries #3 • Fajtlowicz’s Graffiti graph theory program • All G, Chrom+Rad < MaxDeg+FreqMaxDeg • 60+ papers about it’s conjectures • Bailey’s PSQL algorithm • New formula for : i (1/16i)(4/(8i+1)-2/(8i+4)-1/(8i+5)-1/(8i+6)) • Easier to calculate nth hex digit of 

  6. Theories in Pure Mathematics • Concepts • Examples and definitions • Statements • Conjectures and theorems • Explanations • Proofs, counterexamples • e.g., pure maths:group theory • Concepts: cyclic groups, Abelian groups • Conjecture: cyclic groups are Abelian • Examples provide empirical evidence • Simple proof for explanation

  7. HR: Theory Formation Cycle • Start with background knowledge • user-supplied axioms + concepts • Invent a new concept (machine learning) • Look for conjectures empirically (d-mining) • Prove the conjectures (theorem proving) • Disprove the conjectures (model generation) • Assess all concepts w.r.t. new concept • Invent a new concept • Build it from the most interesting old concepts

  8. Inventing New Concepts • Ten General Production Rules (PR) • Work in all domains (math + non math) • Build new concept from one (or two) old ones • Example: Abelian groups • Given: [G,a,b,c] : a*b=c • Compose PR: [G,a,b,c] : a*b=c & b*a=c • Exists PR: [G,a,b] :  c (a*b=c & b*a=c) • Forall PR: [G] :  a b ( c (a*b=c & b*a=c))

  9. Making Conjectures • Theory formation step • Attempt to invent a new concept • Concept has same examples as previous one • HR makes an equivalence conjecture • Concept has no examples • HR makes a non-existence conjecture • Examples of one concept are all examples of another concept • HR makes an implication conjecture

  10. Proving Theorems • HR relies on third party theorem provers • Equivalence conjectures: • Sets of implication conjectures • From which prime implicates are extracted • E.g.  a (a*a=a a=id) • a*a=a  a=id, a=id  a*a=a • HR uses the Otter theorem prover • William McCune et al. • Only uses this for finite algebras

  11. Disproving Non-Theorems • Any conjectures which Otter can’t prove • HR looks for a counterexample • Using the MACE model generator • Also written by William McCune • Other possibilities: • Computer algebra, constraint satisfaction • Counterexamples are added to the theory • Fewer similar non-theorems are made later

  12. Assessing Interestingness • New concepts from interesting old ones • Concepts measured in terms of: • Intrinsic values, e.g. complexity of definition • Relational values, e.g. novelty of categorisation • Concepts also assessed by conjectures • Quality, quantity of conjectures involving conc. • Conjectures also assessed • Difficulty of proof (proof length from Otter) • Surprisingness (of LHS and RHS definitions)

  13. Bootstrapping ATF Cycle

  14. Applications of HR • Puzzle generation • Next in sequence, odd one out • Automated theorem proving • Discovering useful lemmas • Constraint satisfaction problems • Discovering additional constraints • Machine learning tasks • Puzzle solving, prediction tasks • Studying machine creativity • Multi-agent, cross-domain, meta-level

  15. Application to Mathematical Discovery • Exploration of algebras using HR • Anti-associative algebras • Quasigroups • Number theory results • Encyclopedia of Integer Sequences • Using HR and NumbersWithNames • Refactorable numbers • Results and open conjectures • Problem solving (Zeitz numbers)

  16. Anti-associative Algebras(Novel domain to me) • all a,b,c a*(b*c)  (a*b)*c • Used HR with Otter and MACE (2 hours) • 34 examples, sizes 2 to 6 (exists each size) • AAAs are not: abelian or quasigroups • Quasigroups must have associative triple • Have two elements on diagonal • Have no identity, or even local identity • Commutative pairs are not co-squares

  17. Quasigroup Results • Part of CSP project • QG3 quasigroups: (a*b)*(b*a)=a • HR conjectured, Otter proved, We interpreted • Diagonal elements are all different • a*a=b  b*b=a • a*b=b  b*a=a • QG3 quasigroups are anti-Abelian • a*b = b*a  a=b • Corollary to one of HR’s results (with our help) • 10x speed up over naïve model

  18. Neil Sloane’s Encyclopedia of Integer Sequences • Large database of sequences • E.g., Primes: 2, 3, 5, 7, 11, 13,… • Contains 67,000+ sequences (36 years) • A new sequence must be novel, infinite, interesting • HR has invented 20 new sequences • All supplied with interesting theorems (our proof) • Datamining the Encyclopedia itself • NumbersWithNames program (details ommitted)

  19. Some Nice Results • Number of divisors, (n), is a prime • 2, 3, 4, 5, 7, 9, 11, 13, … • m(n) is prime  (n) is prime • g(n) = #squares dividing n • 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, … • numbers setting the record for g(n) • 1, 4, 16, 36, 144, 576, … • Squares of the highly composite numbers • Perfect numbers are pernicious

  20. Refactorable Numbers • Number of divisors is itself a divisor • 1, 2, 8, 9, 12, 18, 24, 36, 40, … • HR’s first success [not in Encyclopedia] • Turned out to be a re-invention (1990) • Preliminary results (* - made by HR) • Infinitely many refactorables • Odd refactorables are perfect squares * • Congruent to 0, 1, 2 or 4 mod 8 * • Perfect numbers are not refactorable * • m,n relprim and refactorable  mn refactorable • x refactorable  2x refactorable *

  21. Refactorables – Deeper Results • Natural density is zero • Kennedy and Cooper 1990 • Joshua Zelinsky (hot off the press) • T(n) < 0.5 B(n) with finitely many counterexamples (max 1013) • T(n) = #refacs < n, B(n) = #primes < n • Assuming Goldbach’s strong conjecture • Every integer is the sum of 5 or fewer refactorables • Zelinsky uses the results from HR

  22. Refactorables – Questions….. • Numbers n!/3 are refactorable* • Numbers for which ((n))=n are refactorable* (x) = #integers less than or equal to and coprime to x • There are infinitely many pairs of refactorables • (1,2), (8,9), (1520,1521), (50624,50625), … • There are no triples of refactorables • We know there are no quadruples • And no triples less than 1053

  23. Demonstration – Zeitz numbers • Hungarian maths competition • Multiply four consecutive numbers • n(n+1)(n+2)(n+3) • Never a square number • Demonstration • Using NumbersWithNames

  24. Future Work: HR Project • McCasland? • Use HR to explore Zariski spaces • Colton: Express HR as a ML program • Try domains other than maths (bioinformatics) • Walsh: Integrate HR • With every maths program ever written • In particular Maple computer algebra • Bundy: • Build an automated mathematician

  25. Web Pages • HR: • www.dai.ed.ac.uk/~simonco/research/hr • NumbersWithNames program: • www.machine-creativity.com/programs/nwn • Encyclopedia of Integer Sequences: • www.research.att.com/~njas/sequences

More Related