MOSES -- Meta-Optimizing Semantic Evolutionary Search
MOSES is a machine-learning tool; it is an "evolutionary program
learner". It is capable of learning short programs that capture
patterns in input datasets. These programs can be output in either
combo programming language, or in python. For a given data
input, the programs will roughly recreate the dataset on which they
MOSES has been used in several commercial applications, including the analysis of medical patient and physician clinical data, and in several different financial systems. It is also used by OpenCog to learn automated behaviors, movements and actions in response to perceptual stimulus of artificial-life virtual agents (i.e. pet-dog game avatars). Future plans including using it to learn behavioral programs that control real-world robots, via the OpenPsi implementation of Psi-theory and ROS nodes running on the OpenCog AtomSpace.
The term "evolutionary" means that MOSES uses genetic programming techniques to "evolve" new programs. Each program can be thought of as a tree (similar to a "decision tree", but allowing intermediate nodes to be any programming-language construct). Evolution proceeds by selecting one exemplar tree from a collection of reasonably fit individuals, and then making random alterations to the program tree, in an attempt to find an even fitter (more accurate) program.
It is derived from the ideas forumlated in Moshe Looks' thesis, "Competent Program Evolution", 2006 (Washington University, Missouri) http://metacog.org/main.pdf. Moshe is also one of the primary authors of this code.
MOSES is under double license, Apache 2.0 and GNU AGPL 3.
Documentation can be found in the
/docs directory, which includes a
"QuickStart.pdf" that reviews the algorithms and data structures
used within MOSES. A detailed man-page can be found in
/moses/moses/man/moses.1. There is also a considerable amount of
information in the OpenCog wiki:
To build and run MOSES, the packages listed below are required. With a few exceptions, most Linux distributions will provide these packages.
C++ utilities package http://www.boost.org/ | libboost-dev
Build management tool; v2.8 or higher recommended. http://www.cmake.org/ | cmake
Common OpenCog C++ utilities http://github.com/opencog/cogutil It uses exactly the same build procedure as this package. Be sure to
sudo make installat the end.
The following packages are optional. If they are not installed, some optional parts of MOSES will not be built. The CMake command, during the build, will be more precise as to which parts will not be built.
Message Passing Interface Required for compute-cluster version of MOSES Use either MPICHV2 or OpenMPI | http://www.open-mpi.org/ | libopenmpi-dev
Peform the following steps at the shell prompt:
cd to project root dir mkdir build cd build cmake -DCMAKE_BUILD_TYPE=Release .. make
Libraries will be built into subdirectories within build, mirroring the
structure of the source directory root. The flag
results in binaries that are optimized for for performance; ommitting
this flag will result in faster builds, but slower executables.
To build and run the unit tests, from the ./build directory enter (after building moses as above):
sudo make install after finishing the build.
MOSES can be used in one of two ways: either directly from the command line, or by embedding its low-level API into C++ programs. For almost all users, the command-line interface is strongly recommended.
For those who absolutely must used the low-level C++ programming API,
there is the
/examples directory. To build the examples, say:
- example-ant: Santa Fe Trail ant example
- example-data: Simple data sets on which moses can be run.
- example-progs: Other example programs.