Explanation-Based Learning in the Entropy Reduction Engine
Explanation-Based Learning in the Entropy Reduction Engine
Causal Theory can be refined by
detecting and recovering from failures. Failures are detected when
there are discrepancies between the real state of the world and the
projected state. These failures may be caused from either missing
operator preconditions, missing operator outcomes, or missing domain
constraints. An EBL technique has been implemented that resolves the
first cause. Work continues on methods to overcome the other two
causes.
Situation Control Rules can
be acquired and refined through caching goal-satisfying behaviors
synthesized by the Projector. SCRs
can be formed with a varying degree of generality. This process
is not unlike the goal-regression, knowledge compilation learning
technique called chunking
that is used in Soar.
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