Explanation-Based Learning
When an agent can utilize a worked example of a problem as a
problem-solving method, the agent is said to have the capability of
explanation-based learning (EBL). This is a type of
analytic learning. 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. Additionally,
the utility of
learned information is an issue when learning
proceeds indiscriminately.
Other forms of learning that are based on EBL are knowledge
compilation, caching and
macro-ops.
Architectures having this capability include:
Go to A List of Common Capabilities.
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Current Location: Capabilities-Explanation-Based Learning