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Python framework for writing programs to solve complex problems not amenable to brute force. The architecture is a descendant of Douglas Hofstadter and Melanie Mitchell's Copycat architecture.

Project Description
PySeqsee: A python framework for solving complex problems not amenable to brute force.

PySeqsee aims to be a robust framework for developing blackboard-architecture
based programs that tackle hard problems in a human-like way.

It is open-source, under GNU GPLv3.

Mailing List:
-!forum/pyseqsee (to view)
- (to post)
Source Code:
Bug Tracker:
Development Status: Alpha, but actively-developed working code

Brief history and motivation

For over two decades now, Douglas Hofstadter's Fluid Analogies Research
Group at Indiana University has designed computer simulations aimed
at understanding human cognition. Each successive model has usually been
written from scratch. Very little of the actual code from previous
implementations was used by subsequent implementations, although ideas
and the basic approach survived.

Not just were the implementations different, they were typically in
different languages. Franz Lisp, Chez Scheme, C++, and even Perl have
been used by various projects, and there was also talk of using Delphi. A Java
port of Copycat exists.

This project aims to create a framework in which to implement various
cognitive architectures. It is written in Python 3, and aims to provide
many components out of the box without making too many irreversible
commitments. That is, it provides a full suite of tools to get the job
done, but also allows you to swap out any component and use the rest.

Services provided (and their level of completion):

* A reusable GUI. Every project will have a different workspace, but there is
still much that is shared. PySeqsee allows you to just write the visualization
for your data, and takes care of everything else. (complete)
* A robust testing framework. If you wish to test how well a proposed new feature
works, you can run a side-by-side comparison over many inputs and see the stats.
All that is needed is a file with the inputs to test and in case the input is
very specialized, a python class to convert these inputs to flags to be passed in.
(status: functionality implemented, but statistical analysis to compare which
of the two sides is better is not done).
* A setup script that creates the skeleton of a new project. (Under development,
basic functionality exists).
* A coderack and facilities for writing codelets (complete).
* A slipnet (known here as Long-term Memory), along with the ability to add nodes
and edges and the ability to save it to disk (works, but could be significantly
* A stream of thought. This is a component that first appeared in Seqsee (written
in Perl), but plays a central role here. The stream provides a temporal context
--- that is, recent thoughts can influence what the system does in flexible ways.
A full implementation exists.
* A full reimplementation of Seqsee (status: under development. Many sequences
seen, but not all that the perl version did). A short video of the Perl version
can be found here: The dissertation
is here:

Release History

Release History

This version
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PySeqsee-0.1.2.tar.gz (95.3 kB) Copy SHA256 Checksum SHA256 Source Apr 20, 2012

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