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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.

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Presentation on theme: "Automatic Generation of First Order Theorems Simon Colton Universities of Edinburgh and York Funded by EPSRC grant GR/M98012 and the Calculemus Network."— Presentation transcript:

1 Automatic Generation of First Order Theorems Simon Colton Universities of Edinburgh and York Funded by EPSRC grant GR/M98012 and the Calculemus Network

2 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

3 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

4 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

5 HR: Theory Formation Cycle Start with background knowledge user-supplied axioms + concepts 1. Invent a new concept (machine learning) 2. Look for conjectures empirically (d-mining) 3. Prove the conjectures (theorem proving) 4. Disprove the conjectures (model generation) 5. Assess all concepts w.r.t. new concept 1. Invent a new concept Build it from the most interesting old concepts

6 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))

7 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

8 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

9 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

10 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)

11 Bootstrapping ATF Cycle

12 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)

13 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)

14 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

15 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]

16 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

17 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

18 Experiments Possible experiments: Which one proves most of HR’s theorems 1 st 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

19 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

20 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

21 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

22 Web Pages Mathweb: www.mathweb.org HR: www.dai.ed.ac.uk/~simonco/research/hrww.dai.ed.ac.uk/~simonco/research/hr NumbersWithNames program: www.machine-creativity.com/programs/nwn Demonstration: Tomorrow @ 2pm? Room 208.


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