Partial abductive inference in Bayesian belief networks (BBNs) is intended as the process of generating the K most probable con®gurations for a set of unobserved variables (the explanation set). This problem is NP-hard and so exact computation is not always possible. In previous works genetic algori
Explanation-based generalisation = partial evaluation
✍ Scribed by Frank van Harmelen; Alan Bundy
- Publisher
- Elsevier Science
- Year
- 1988
- Tongue
- English
- Weight
- 600 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0004-3702
No coin nor oath required. For personal study only.
✦ Synopsis
We argue that explanation-based generalisation as recently proposed in the machine learning literature is essentially equivalent to partial evaluation, a well-known technique in the functional and logic programming literature. We show this equivalence by analysing the definitions and underlying algorithms of both techniques, and by giving a PROLOG program which can be interpreted as doing either explanation-based generalisation or partial evaluation.
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