Home | | **Artificial Intelligence** | | **Computational Intelligence** | | **Artificial Intelligence** | Explanation Based Learning

Extract general rules from examples

**EXPLANATION BASED LEARNING**

• Extract
general rules from examples

• Basic
idea

– Given
an example, construct a proof for the goal predicate that applies using the
background knowledge.

– In
parallel, construct a generalized proof with variabilized goal.

–
Construct a new rule, LHS with the leaves of the proof tree and RHS with the
variabilized goal.

– Drop
any conditions that are always true regardless of value of variables in the
goal.

•
Any partial subtree can be use for the extracted
general rule, how to choose?

•
Efficiency, Operationality, Generality

– Too
many rules slows down reasoning

– Rules
should provide speed increase by eliminating dead-ends and shortening the

proof

– As
general as possible to cover the most cases

• Tradeoffs,
how to maximize the efficiency of the knowledge base?

•
Any partial subtree can be use for the extracted
general rule, how to choose?

•
Efficiency, Operationality, Generality

– Too
many rules slows down reasoning

– Rules
should provide speed increase by eliminating dead-ends and shortening the proof

– As
general as possible to cover the most cases

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail

**Related Topics **

Copyright © 2018-2020 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.