Bayesian induction of programs, à la Solomonoff
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README.md

Johann

Bayesian induction of programs, à la Solomonoff

Johann is an equational theorem-proving system and Bayesian inference engine for various extensions of combinatory algebra (equivalently, lambda-calculus), making particular use of an interpretation of typed lambda-calculus in untyped concurrent lambda-calculus. Details can be found in the Ph.D. thesis http://fritzo.org/thesis.pdf . Johann implements:

  • Equational theorem proving for various programming languages.

  • Empirical Bayes learning of nonparametric PCFG models of programming languages.

  • Bayesian inference of probabilistic programs (aka Solomonoff induction).

  • Data-driven automated-conjecturing of equations in undecidable theories.

This repository includes:

  • A C++ kernel for building and reasoning about combinatory databases. See the src/ directory.

  • A collection of code-as-dissertation in a literate programming style ".jtext", which develops a lambda-calculus corpus for datamining. See the scripts/ directory

  • A Python front end to latex for typesetting the literate programs ( jtext2latex_ ). See the jtext/ directory.

  • A C++ mapper to visualize Johann databases. See the mapper/ directory.

  • Some sketches of web-apps using Johann databases. See the html/ and src/ directories.

From the thesis abstract (scripts/abstract.text):

... The final component of this thesis is a system, Johann, for automated reasoning about equality and order in the above languages. Johann was used to formally verify many of the theorems in this thesis (and even conjecture some simple theorems). The general design focus is on efficient knowledge representation, rather than proof search strategies. Johann maintains a database of all facts about a set of (say 10k) objects, or terms-modulo-equivalence. The database evolves in time by randomly adding or removing objects. Each time an object is added, the database is saturated with facts using a forward chaining algorithm.

A specific design goal is to be able to run Johann for long periods of time (weeks) and accumulate useful knowledge, subject to limited memory. This requires statistical analysis of a corpus of interest (e.g. the set of problems to be verified in this thesis), statistical search for missing equations (from our sigma-01 approximation to a pi-02-complete theory), and careful choice of sampling distributions from which to draw objects to add-to and remove-from the database. The (add,remove) pair of distributions is chosen to achieve a detailed balance theorem, so that, at steady state, Johann (provably) remembers simple facts relevant to the corpus.

Installation

On an ubuntu 12.04 system:

  • to build the johann system run requirements.sh, then make

  • to build documentation run pdf-requirements.sh, then make pdf

Roadmap

  • Architecture:

    • depricate pervasive use of nodes (esp, for equations)

    • rearchitect to separate symbolic from probabilistic: brain, combinatory structure, languages, ...

    • implement finer-grained threading in kernel

    • separate kernel from console

    • separate jdb into generic relational database

  • Parallelization:

    • replace splay forests with hash tables of arrays

    • parallelize forward-chaining workers (dropping the saturation guarantee) but run axiom validation in background

    • implpement multi-kernel collaboration via equation-trading

  • Infrastructure:

    • wrap expressions in python and move syntactic algorithms to python

    • switch to better build system, e.g. cmake, jam

    • switch to zeromq for kernel interface

    • replace custom .jdb format with HDF5

    • switch to TBB for threading library