Explanation-Based Learning
When an agent can utilize a worked example of a problem in order to the
problem-solving method, the agent is said to have the capability of
explanation-based learning (EBL). The advantage of explanation-based
learning is that, as a
deductive mechanism,
it requires only a single
training example (inductive learning methods often require many
training examples). However, to utilize just a single example most EBL
algorithms require all of the following:
- The training example
- A Goal Concept
- An Operationality Criteria
- A Domain Theory
From the training example, the EBL algorithm computes a
generalization of the example that is
consistent with the goal concept and that meets the operationality
criteria (a description of the appropriate form of the final concept).
One criticism of EBL is that the required domain theory needs to be
complete and
consistent.
The following architectures have mechanisms that support explanation-based
learning:
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