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XPLAIN: a system for creating and explaining expert consulting programs

Swartout, William R.·1983·Artificial Intelligence

Abstract

Traditional methods for explaining programs provide explanations by converting the code of the program or traces of its execution to English. While such methods can sometimes adequately explain program behavior, they typically cannot provide justifications for that behavior. That is, such systems cannot tell why what the system is doing is a reasonable thing to be doing. The problem is that the knowledge required to provide these justifications was used to produce the program but is itself not recorded as part of the code, and hence is unavailable. The XPLAIN system uses an automatic programmer to generate a consulting program by refinement from abstract goals. The automatic programmer uses a domain model, consisting of descriptive facts about the application domain, and a set of domain principles which prescribe behavior and drive the refinement process forward. By examining the refinement structure created by the automatic programmer, XPLAIN provides justifications of the code. XPLAIN has been used to re-implement major portions of a Digitalis Therapy Advisor and provides superior explanations of its behavior.

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Metadata

Authors
Swartout, W. R.
Venue
Artificial Intelligence
Publisher
Elsevier BV
Published
1983-09
Volume
21
Issue
3
Pages
285-325
ISSN
0004-3702 (ISSN-L)
OpenAlex
W2048737604
Cited by
393
References
0
FWCI
15.88
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: XPLAIN: a system for creating and explaining expert consulting programs. https://app.cochat.ai/papers/10.1016/s0004-3702(83)80014-9
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Swartout, W. R. (1983). XPLAIN: a system for creating and explaining expert consulting programs. Artificial Intelligence. https://doi.org/10.1016/s0004-3702(83)80014-9

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.