[ranch vermin]

Ivan,   I like your style for generating equations for loose samples on a graph,    but i dont think you can develop anything you cant just zap in from the sensor, symbolic logic wise, BECAUSE you cant justify anything new, anything that you would have justified would have been able to been simply recorded.  youd just have noise logic left over that half passed the test,  which wouldnt be anything useful.

[ivan.moony]

Ivan,   I like your style for generating equations for loose samples on a graph,    but i dont think you can develop anything you cant just zap in from the sensor, symbolic logic wise, BECAUSE you cant justify anything new, anything that you would have justified would have been able to been simply recorded.  youd just have noise logic left over that half passed the test,  which wouldnt be anything useful.

I've given this a thought before and I think it will work like a charm. After providing parsing rules (which you generate by statistically analyzing input sensors or you provide hard-coded i.e. english grammar) you have to parse a list of bytes from a sensor. Then you get a notions tree (like abstract syntax tree) that recursively groups input bytes into bigger particles with which you can do an arithmetic of transforming expressions to deduce implicitly contained facts (i.e. you can predict other states). You can assign a keyboard terminal to a sensor, or any other time-depended input stream. Then it is all in discovering rules and parsing input data to generate a knowledge base.

[ranch vermin]

Well I cant say ive put as much thought as you into it,   maybe starting off from some hardcoded knowledge it can use with the sensor maybe something will develop?

Implement it, then you know.