On 12/18/2016 06:38 AM, Ben Goertzel wrote:
I have posted my design idea  (discussed in a recent email thread) on
the wiki site here


The Backward Chainer can also be framed that way. :-) Even more so directly than what you've sketched on the SampleLink page.

The BC as currently implemented evolves atomese programs, where each program encodes a specific forward chaining strategies (FCS for short). There is a difficulty though, there are no easy way to evaluate the fitness of a FCS, either it proves the target or it doesn't. Or let's say that the fitness landscape is extremely chaotic, some FCS may prove nothing at all, while a tiny variation of it may prove our target completely.

BUT this can be overcome by meta-learning, i.e. learning a measure of success for FCS that are half-way there. So in this framework meta-learning would be used to reshape the fitness function, from a crisp chaotic one to a smooth regular one.


so it doesn't get lost


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