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Automatic Generation of First Order Theorems. Simon Colton Universities of Edinburgh and York Funded by EPSRC grant GR/M98012 and the Calculemus Network. Overview of Talk. Automated Theory Formation Principles Implementation in the HR system Applications Application to Theorem Generation

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Automatic Generation of First Order Theorems

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Automatic generation of first order theorems l.jpg

Automatic Generation of First Order Theorems

Simon Colton

Universities of Edinburgh and York

Funded by EPSRC grant GR/M98012 and the Calculemus Network


Overview of talk l.jpg

Overview of Talk

  • Automated Theory Formation

    • Principles

    • Implementation in the HR system

    • Applications

  • Application to Theorem Generation

    • HR adds to the TPTP library

    • HR becomes a MathWeb service

  • Future Directions


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Scientific Theories

  • Scientific theories about a domain contain:

    • Concepts, examples, definitions,

    • hypotheses, explanations, etc.

  • e.g. chemistry:acids

    • Concepts: Acid, Base, Salt

    • Hypothesis: Acid + Base  Salt + Water

    • Experiments for plausibility/evidence

    • Reaction pathways for explanation


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Theories in Pure Mathematics

  • Concepts have examples and definitions

  • Hypotheses are “conjectures”

  • Explanations are proofs

    • Conjectures become “theorems”

  • e.g pure maths:group theory

    • Concepts: cyclic groups, Abelian groups

    • Conjecture: cyclic groups are Abelian

    • Examples provide empirical evidence

    • Proof for explanation


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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


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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))


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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

  • HR can also make implication conjectures

    • Examples of one concept are all examples of another concept


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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

    • Only uses this for finite algebras


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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: CAS, CSP

  • Counterexamples are added to the theory

    • Fewer similar non-theorems are made later


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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)


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Bootstrapping ATF Cycle


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Applications of ATF

  • Machine Learning

    • Learn concept definitions: e.g. seq. ext.

    • Theory for prediction tasks

    • Theory for puzzle generation

  • Constraint Satisfaction Problems

    • Conjectures: induced constraints

    • Concepts: implied constraints

  • Mathematical Discovery

    • Exploration of new domains

    • Invention of Integer Sequences (NWN)


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Application to ATP

  • Big project: using ATF to improve ATP

  • Sub-project:

    • Using AFT to assess ATP programs

  • Compare first order ATP programs

    • Using a large set of HR’s conjectures

  • Facilitate comparison:

    • Using MathWeb (Zimmer,Franke,…)

    • Using SystemOnTPTP (Sutcliffe)


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First Attempt

  • Aim: add to the TPTP library

    • 5882 test problems for first order provers

    • Otter, SPASS, E, Vampire, etc.

    • New provers are tested using TPTP

  • HR produced 46,000 group conjectures

    • In ten minutes.

  • Around 200 of these were worthy of TPTP

    • All provable by SPASS in 120 seconds

    • 153 provable by only SPASS and E only

    • 42 provable by only SPASS


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Example Theorem

  • Otter and E could not prove this:

  • x y

    (( z (inv(z)=x & z*y=x) &

     u (x*u=y &  v (v*x=u & inv(v)=x)))

    ( a (inv(a)=x & a*y=x) &

  • b (b*y=x & inv(b)=y)))

    [about pairs of identity elements]


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Interface of HR into MathWeb

  • MathWeb project in Saarbrücken

  • Has access to many first order ATP progs.

    • E, Otter, SPASS, Vampire, Bliksem, …

  • Idea: HR passes conjectures to MathWeb

    • MathWeb translates conjectures using tptp2x

    • MathWeb calls the provers

  • Interface

    • Via sockets at the moment

    • Later by XMLRPC for better standardization


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Additional Implementation

  • By Zimmer, Colton and Franke

  • Changes to HR

    • Improvements in quantity of theorems

    • Ability to write conjectures in TPTP format

  • Changes to MathWeb

    • Calling one prover after another

      (1000s of times in a row)

    • Quicker interaction with tptp2x

    • Integration of the E system


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Experiments

  • Possible experiments:

    • Which one proves most of HR’s theorems 1st

    • Compare the average times

    • How many timeouts for each prover

  • Watch this space for results…..

    • Saturday: 9000 group theory theorems proved by SPASS, E & Otter, before a crash!

  • Preliminary (unsurprising) result

    • Average times: SPASS < E < Otter


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Future Work: MathWeb #1

  • Try HR on more provers in MathWeb

    • Vampire, Bliksem

  • Offer HR as a new MathWeb service

    • User says: “Give me 1,000 theorems which

      SPASS and E take over 10 secs. to prove”

  • Interface HR and model generators in MW

    • Use MACE, etc. to disprove theorems

  • Interface HR and CSP, CAS in MW

    • Infinite Group theory with Bundy and Sorge


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Future Work: MathWeb #2

  • Aim: Beat SPASS……

    • SPASS is too good for HR in group theory

    • 46,000 theorems and SPASS proved them all!

  • Part two of my Calculemus project:

    • With Jacques Calmet & Clemens Ballarin in Karlsruhe

    • HR invents new domains

    • Adds and constrains new operators for finite algebras

  • “Grow” difficult theorems from prime implicates


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Future Work: HR Project

  • Colton: Express HR as a ML program

    • Try domains other than maths

  • Walsh: Integrate HR

    • With every maths program ever written

  • Bundy:

    • Build an automated mathematician


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Web Pages

  • Mathweb:

    • www.mathweb.org

  • HR:

    • www.dai.ed.ac.uk/~simonco/research/hr

  • NumbersWithNames program:

    • www.machine-creativity.com/programs/nwn

  • Demonstration: Tomorrow @ 2pm? Room 208.


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