MACHINE X LANGUAGEYun-Yan Chi

Programming and Machine Learning

Inductive Programming

Program Synthesis

Programming

problems / tasks

specifications

algorithms (in mind)

programs

Programming

problems / tasks

understand and realize

specifications

precise

Programming

problems / tasks

unknown or too complicated

specifications (static or dynamic behavior)

vague or incomplete

Machine Learning

learning from information

examples => problem solver

Neural network

Bayesian networks

Rule learning

Inductive Programming

Machine Learning

Artificial Intelligence

Programming Language

Software Engineering

Inductive Programming

deductive view of software developmentfrom the general to the specificcode generation from UMLprogram transformationtheorem proving model checking

Inductive Programming

inductive view of software developmentmathematical inductionfrom the specific to the generalincomplete specification -> programinputs: test cases, I/O examples, traces, behavior of a program, etc.outputs: program, algorithm

Inductive Programming

inductive logic programminglearn concepts(hypotheses), represented by logic clauses, from examplesgenerate-and-test somehow generate functions test functions by examplese.g. genetic programming, enumeration, etc.(term rewriting / search / ranking)

DEMO

De-typechecker

Oleg Kiselyov

converting from undefined to defined

[input] purely polymorphic function types

function types as logic programs (on type level)

implement SLD resolution (by typeclass)

[output] lambda functionhttp://okmij.org/ftp/Haskell/de-typechecker.lhs http://okmij.org/ftp/Haskell/types.html#de-typechecker

De-typechecker

undefined::(a->a)undefined::(a->b)undefined::(b -> c) -> (a -> b) -> a -> c

(((. head . uncurry zip . splitAt 1 . repeat) . uncurry) .) . (.) . flipreify (undefined `asTypeOf` ((((. head . uncurry zip . splitAt 1 . repeat) . uncurry) .) . (.) . flip)) gamma0

MagicHaskeller

Susumu Katayama (片山 晋)

Synthesize by exhaustive searching

[input] inputs and predicates

proposition (by type inference)

proof trees (by sequent calculus)

[output] function definitionhttp://okmij.org/ftp/Haskell/de-typechecker.lhs http://okmij.org/ftp/Haskell/types.html#de-typechecker

MagicHaskeller

webpage f [5,1,3,4,2] == [1,2,3,4,5]

f [1,2,3] == [[3,2,1],[3,2],[3],[]] && f [4,5,6] == [[6,5,4],[6,5],[6],[]]

f (1,3) >= 4 && f (7,3) <= 10

f ([1,2,3],[3,2,1]) == [(1,3),(2,2),(3,1)] && (f ([],[]) == []) && (f ([3],[2]) == [(3,2)])

http://www.inductive-programming.org/repository/problems/mhask/examples.txt

Program Synthesis

Inductive programming: 2005~2009, 2010, 2011

Sumit Gulwani (Microsoft Research)

(Inductive) Program Synthesis

Program Synthesis

[1] Programmerssoftware developalgorithm designprogram inverse[2] UsersEducation“enabling them to bring their creativity to life!”

GLANCE

Program Synthesis

[input] User Intentexampleslogicnatural language[output] Application / Tool

Program Synthesis

AI-style search

Formal reasoning (SAT/SMT solver)

FLOLAC 13

ICFPC

Reference

http://www.inductive-programming.org/

http://okmij.org/ftp/Haskell/types.html#de-typechecker

http://okmij.org/ftp/Haskell/de-typechecker.lhs

http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskellerLib.html

http://www.inductive-programming.org/repository/problems/mhask/examples.txt

http://research.microsoft.com/en-us/um/people/sumitg/pubs/synthesis.html