The Utility Problem
The Utility Problem results in explanation-based learning systems
when the method used to determine the usefulness of learned rules (the
operationality criterion) is unrealistic. This is the case in most current
systems since no techniques have been developed to make the operationality
criterion as sophisticated as the environments to which EBL has been applied.
Rules are generally learned too frequently and thus learning may actually
slow down the system.
Carbonell, et al. (1991)
identify three factors
that may contribute to this degradation in performance:
-
Low Application Frequency
The rule may be over-specific and thus applied too infrequently to be useful.
-
High Match Cost
The cost of matching a rule -- especially those which represent a long
sequence of operations -- may be prohibitively expensive (e.g., the matching
problem for pre-condition lists is NP-complete).
-
Low Benefit
The rule may have only marginal utility in the problem domain.
Several techniques have been described for addressing the utility problem
in integrated cognitive architectures. Examples in which the
utility problem is an issue (and may have been addressed
directly) include:
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