Counterfactuals
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Counterfactuals are a form of common-sense nonmonotonic inference that has been of long-term interest to philosophers. In this paper, we begin by describing some of the impact counterfactuals can be expected to have in artificial intelligence, and by reviewing briefly some of the philosophical conclusions which have been drawn about them. We continue by presenting a formal description of counterfactual implication and discussing the issues involved in implementing it. Specific applications in the domains of planning and the automated diagnosis of hardware faults are considered, and we conclude by describing possible extensions to this work involving multi-valued logics and situation semantics.
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Supporters of the pragmatic approach to explanation argue that the ability to distinguish the answer to an explanatory question from a set of contrastive hypothesized alternatives provides the explainee with sufficiently comprehensive information on the reasoning behind the question.
“Thus, supporters of the pragmatic approach to explanation argue that it is exactly the ability to distinguish the answer to an explanatory question from a set of contrastive hypothesized alternatives that provides the explainee with sufficiently comprehensive information on the reasoning behind the question [13].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [13]
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References 30 · Cited by 218
- Authors
- Ginsberg, M. L.
- Affiliations
- Stanford University (US)
- Corresponding author
- Ginsberg, Matthew L.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1986-10
- Volume
- 30
- Issue
- 1
- Pages
- 35-79
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W4239069293
- Cited by
- 218
- References
- 30
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 2 · 2023: 8 · 2022: 9 · 2021: 9 · 2020: 3 · 2019: 2 · 2018: 2 · 2017: 4 · 2016: 6 · 2015: 8 · 2014: 3 · 2013: 3 · 2012: 4
- FWCI
- 7.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- counterfactuals, nonmonotonic reasoning, commonsense reasoning, planning, automated diagnosis, hardware fault diagnosis, many-valued logic, situation semantics
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