The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
An explanation facility for today's expert systems
Abstract
The authors discuss explanation facility types found in most expert systems and shells used today, referring to these as practice systems, since they represent current practice in expert system work. Practice systems include nonresearch expert systems (being developed every day in industry) as well as nonresearch expert system shells. They maintain that an extensive explanation facility meeting the obligations of a practice system's role can be implemented using only practice technology. They discuss the relationship of journalism to explanation, showing that explanation facility tasks (roles) on current practice expert systems and shells resemble a newswriter's tasks, as both involve presenting users or readers with accurate, objective, noninteractive accounts of events. In news reporting, events are pieces of news. In expert-system explanation, events are actions taken by expert systems. The authors present a journalistic explanation facility, Joe, which is part of AGNESS (a generalized network-based expert system shell), under development at the University of Minnesota.>
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“While knowledge-based systems can provide clear explanations of the reasoning behind their outputs (Wick & Slagle, 1989; Gregor & Benbasat, 1999), models trained by machine learning algorithms may not (Lipton, 2015).”
From Algorithmic Accountability and Public Reason (2018) · cites this paper as Wick & Slagle, 1989
Citations
Cites
Computer-Based Medical Consultations: MYCIN.
(1976) · Annals of Internal Medicine · doi:10.7326/0003-4819-85-6-831_1 · Cited by 2,506
Explanation-Based Generalization: A Unifying View
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986) · Machine Learning · doi:10.1023/a:1022691120807 · Cited by 1,382
A Theory of Approximate Reasoning
Zadeh, L. A. (1979) · Machine intelligence · Cited by 1,329
Machine learning: An artificial intelligence approach
Stefik, M. (1985) · Artificial Intelligence · doi:10.1016/0004-3702(85)90005-0 · Cited by 986
The role of frame-based representation in reasoning
Fikes, R. E., & Kehler, T. (1985) · Communications of the ACM · doi:10.1145/4284.4285 · Cited by 813
WATERMAN, D. A. (1985) · CERN Document Server (European Organization for Nuclear Research) · Cited by 743
The epistemology of a rule-based expert system —a framework for explanation
Clancey, W. J. (1983) · Artificial Intelligence · doi:10.1016/0004-3702(83)90008-5 · Cited by 712
The Logic of Decision and Action
Anderson, A. R., Davidson, D., Rescher, N., Simon, H. A., & von Wright, G. H. (1967) · University of Pittsburgh Press eBooks · doi:10.2307/jj.13027259 · Cited by 672
XPLAIN: a system for creating and explaining expert consulting programs
Swartout, W. (1983) · Artificial Intelligence · doi:10.1016/s0004-3702(83)80014-9 · Cited by 407
Radon transform orientation estimation for rotation invariant texture analysis
Jafari‐Khouzani, K., & Hamid Soltanian-zadeh, A. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.126 · Cited by 378
Research works printed but not linked: 8
- metz, 1985, Newswriting From lead to "30"
- hughes, 1986, How and Why: How Far Will They Take Us, and Why Should We Need Any More?, Proceedings of the 5th Workshop on Explanations, 69
- reboh, 1981, Knowledge Engineering Techniques and Tools in the Prospector Environment
- ault, 1959, Reporting the News
- king, 1986, The Ergo Project: A Natural Language Query Facility for Explaining Financial Modeling Results, Transactions on Decision Support Systems, 136
- franc, 1987, Preliminary Safety and Risk Assessment for Existing Hydraulic Structures ? An Expert Systems Approach
- 1984, S&C Product Description
- wick, 1987, ?nowledge-Based Explanation
Cited by
Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs
Wang, W., & Benbasat, I. (2007) · Journal of Management Information Systems · doi:10.2753/mis0742-1222230410 · Cited by 540
Explanations From Intelligent Systems: Theoretical Foundations and Implications for Practice1
Gregor, S., & Benbasat, I. (1999) · MIS Quarterly · doi:10.2307/249487 · Cited by 535
Algorithmic Accountability and Public Reason
Binns, R. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0263-5 · Cited by 457
“…systems can 9 Although, significant challenges in specifying constraints remain; see e.g. (Bostrom, 2003). provide clear explanations of the reasoning behind their outputs (Wick & Slagle, 1989; Gregor & Benbasat, 1999), models trained by machine learning algorithms may not (Lipton, 2015).”
BackgroundA systematic review and taxonomy of explanations in decision support and recommender systems
Nunes, I., & Jannach, D. (2017) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-017-9195-0 · Cited by 447
A review of explanation methods for Bayesian networks
Lacave, C., & Díez, F. J. (2002) · The Knowledge Engineering Review · doi:10.1017/s026988890200019x · Cited by 242
Metadata
- Authors
- Wick, M. R., & Slagle, J. R.
- Affiliations
- University of Minnesota (US)
- Venue
- IEEE Expert
- Published
- 1989
- Volume
- 4
- Issue
- 1
- Pages
- 26-36
- ISSN
- 0885-9000 (ISSN-L)
- OpenAlex
- W2014798403
- Cited by
- 68
- References
- 31
- Citations by year
- 2023: 2 · 2018: 1 · 2017: 4 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 1
- FWCI
- 6.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Expert (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Computational Techniques and Applications
- Keywords
- explanation facilities, expert systems, expert system shells
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
