From OpenCog

Incorporating Other Cognitive Processes Into Inference

Hebbian inference control is a valuable and powerful process, but it is not always going to be enough. The solution of some problems that OCP chooses to address via inference will ultimately require the use of other methods, too. In these cases, one workaround is for inference to call on other cognitive processes to help it out.

This is done via the forward or backward chainer identifying specific Atoms deserving of attention by other cognitive processes, and then spawning Tasks executing these other cognitive processes on the appropriate Atoms.

Firstly, which Atoms should be selected for this kind of attention? What we want are InferenceTreeNodes that:

  • have high STI.
  • have the impact to significantly change the overall truth value of the inference tree they are embedded in (something that can be calculated by hypothetically varying the truth value of the InferenceTreeNode and seeing how the truth value of the overall conclusion is affected).
  • have truth values that are known with low confidence.

Truth values meeting these criteria should be taken as strong candidates for attention by other cognitive processes.

The next question is which other cognitive processes do we apply in which cases?

MOSES in supervised categorization mode can be applied to a candidate InferenceTreeNode representing a OCP Node if it has a sufficient number of members (Atoms linked to it by MemberLinks); and, a sufficient number of new members have been added to it (or have had their membership degree significantly changed) since MOSES in supervised categorization mode was used on it last.

Next, pattern mining can be applied to look for connectivity patterns elsewhere in the AtomTable, similar to the connectivity patterns of the candidate Atom, if the candidate Atom has changed significantly since pattern mining last visited it.

More subtly, what if, we try to find whether "cross breed" implies "Ugliness", and we know that "bad genes" implies Ugliness, but can't find a way, by backward chaining, to prove that "cross breed" implies "bad genes". Then we could launch a non-backward-chaining algorithm to measure the overlap of SatisfyingSet(cross breed) and SatisfyingSet(bad genes). Specifically, we could use MOSES in supervised categorization mode to find relationships characterizing "cross breed" and other relationships characterizing "bad genes", and then do some forward chaining inference on these relationships. This would be a general heuristic for what to do when there's a link with low confidence but high potential importance to the inference tree.

OpenCogPrime : SpeculativeConceptFormation may also be used to create new concepts and attempt to link them to the Atoms involved in an inference (via subsidiary inference processes, or HebbianLink formation based on usage in learned procedures, etc.), so that they may be used in inference.

<< Integrative Inference