Wow – it’s been while since the last OpenCog Recap. I think it’s time to rectify that, as there’s been a lot happening since then!
In early April, Ben visited Hong Kong to give guidance to the long-term plan for the HK project. We had many interesting discussions with plans for how to tackle a number of problems, including resolving issues in the language pipeline, frequent sub-graph mining, and recognizing event boundaries.
I implemented an initial workable version of OpenCog Python bindings, with support for MindAgents and more recently CogServer requests both written in Python.
Troy and Zhenhua developed the OpenCog embodiment workbench. The OpenCog workbench is designed to monitor the OpenPsi system, and other aspects of a running instance of OpenCog. Currently, the workbench has three modules: Psi monitor, Dialog system logger, and Psi Emotion Space. The Psi monitor is usable as it stands while the other two are under development. It’s written in Python and uses PyQt for the GUI library.
Jared has been doing a lot of background reading and has been experimenting with frequent sub-graph mining. He has currently decided on using SUBDUE to detect embodiment patterns, as it has a number of benefits over other packages.
Troy integrated minepackage, to provide a dynamic environment (with block destruction and creation) for OpenCog embodiment. Minepackage is an open source package for creating cubic type games, such as Minecraft and infiniminer. The full package supports (or will support): terrain generation, fluid physics, decoration objects and lighting systems. However, our research project will only need some of these features and will also be extending the game world significantly to provide an effective learning environment.
On this note, Cord Krohn has been working on the design side of things has put together some nice concept sheets for the characters in the demos. He is also planning how the learning we wish to demonstrate will be best represented in the minecraft-like environment.
Zhenhua is currently replacing the OpenPsi equations with more suitable expressions based on AtomSpace contents and feedback from the world. Zhenhua also started work on the Dialogue System, which is related to the Perception/Action system that OpenPsi will inform.
Together with Michel, Shuo Chen and others, Ben Goertzel worked out a detailed design for integrating DeSTIN (a variant of HTM) with OpenCog. In addition, a quite extensive document on Michel’s work to port DeSTIN to CUDA is expected by the end of June as a result of Michel’s thesis.
Ben Goertzel and Nil Geisweiller have designed and implemented a framework for “feature metalearning” (transferring information on feature quality from one categorization problem to another), integrated it with MOSES, and are currently testing it on a large database of text categorization problems.
Matt Ikle’ prototyped a simple, non-scalable version of information geometry based economic attention allocation in OpenCog, and found dramatic intelligence improvements over the standard ECAN version for these simple cases. A draft paper is available here.
So, progress on a number of fronts, hopefully by the next recap we’ll have a video of embodied learning to show