Revised library and new test suite

I’ve updated the chunks and rules format to better align with HTTP, to add new features, e.g. iteration over chunks, properties and lists, improved smart home demo, and a new web-based test suite.

 https://www.w3.org/Data/demos/chunks/testing/ <https://www.w3.org/Data/demos/chunks/testing/>

The chunks and rules format is documented at:

 https://github.com/w3c/cogai/blob/master/chunks-and-rules.md <https://github.com/w3c/cogai/blob/master/chunks-and-rules.md>

An open question is whether a more minimalist approach might be better for machine learning, see:

 https://github.com/w3c/cogai/blob/master/minimalist.md <https://github.com/w3c/cogai/blob/master/minimalist.md>

I’ve also created issue #14 and issue #16 to gather ideas on associative search across multiple cognitive modules, and computational models of unconscious thought.

Issue #14 looks at how to efficiently support integration of information across different cortical regions in terms of distributed graph algorithms and inter module messaging, drawing upon the research by Sharon Thompson-Schill at the University of Pennsylvania.

Issue #16 looks at some of the requirements for graph algorithms for unconscious thought, e.g. emotions, handling ambiguity in natural language, learning to spot anomalous (i.e. unfamiliar) behaviours, and unconscious ranking of alternatives, drawing upon research by Ap Dijksterhuis at Radboud University Nijmegen. 

Shahram Heshmat has likened the brain to a prediction machine that is continuously trying to predict incoming information based on past experiences. The discrepancy between the predictions made by the brain and the actual sensory input is a source of surprise, drawing conscious attention, and stimulating learning. This suggests the use of a system for statistical predictions of behaviour that is constantly being updated by observations.

Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
W3C Data Activity Lead & W3C champion for the Web of things