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XPLAIN: a system for creating and explaining expert consulting programs
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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Cited by
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Weld, D. S., & Bansal, G. (2019) · Communications of the ACM · doi:10.1145/3282486 · Cited by 253
“n practice in AI systems. For example, early rule-based expert systems included explanation systems that augmented a trace of the system’s reasoning — on a particular case — with background knowledge [38]. Recommender systems, which were one of the first deployed uses of machine learning, also induced demand for expla…”
BackgroundExplanation in Case-Based Reasoning—Perspectives and Goals
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Interacting meaningfully with machine learning systems: Three experiments
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Self-citationArtificial Intelligence in Medicine
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An explanation facility for today's expert systems
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On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness
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Local and global explanations of agent behavior: Integrating strategy summaries with saliency maps
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Question-Driven Design Process for Explainable AI User Experiences
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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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