David McSherry
- Artificial Intelligence top 2%
- AI-based Problem Solving and Planning 22
- Semantic Web and Ontologies 7
- Multi-Agent Systems and Negotiation 5
- Topic Modeling 4
- Information Systems top 2%
- Data Mining Algorithms and Applications 11
- Rehabilitation top 10%
- Cognitive Neuroscience top 10%
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- Rough Sets and Fuzzy Logic 7
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- Biomedical Text Mining and Ontologies 6
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- Constraint Satisfaction and Optimization 4
- Co-authors
- Ken FullertonR. W. StoutDavid LeakeAgnar AamodtRamón López de MántarasBarry SmythMichael T. CoxKenneth D. Forbus
- Journals
- Artificial Intelligence Review (6 papers)Knowledge-Based Systems (4 papers)Artificial Intelligence in Medicine (3 papers)
- Partner nations
- United KingdomUnited StatesAustralia
In The Last Decade
David McSherry
47 papers receiving 909 citations
Peers
Comparison fields: 5 of 125
- Artificial Intelligence 577
- Information Systems 283
- Management Science and Operations Research 95
- Rehabilitation 45
- Cognitive Neuroscience 115
Countries citing papers authored by David McSherry
This map shows the geographic impact of David McSherry's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David McSherry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David McSherry more than expected).
Fields of papers citing papers by David McSherry
This network shows the impact of papers produced by David McSherry. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David McSherry. The network helps show where David McSherry may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David McSherry, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 28 | |
| 2 | Real-time Planning for Interactive Storytelling | 2009 | 1 |
| 3 | The ins and outs of critiquing | 2007 | 4 |
| 4 | Automating the discovery of recommendation knowledge | 2005 | 2 |
| 5 | 2005 | 340 | |
| 6 | 2005 | 22 | |
| 7 | Increasing dialogue efficiency in case-based reasoning without loss of solution quality | 2003 | 12 |
| 8 | Coverage-optimized retrieval | 2003 | 4 |
| 9 | Recommendation engineering | 2002 | 6 |
| 10 | minimizing dialog length in interactive case-based reasoning | 2001 | 30 |
| 11 | Demand-Driven Discovery of Adaptation Knowledge | 1999 | 4 |
| 12 | 1999 | 13 | |
| 13 | 1999 | 1 | |
| 14 | 1997 | 4 | |
| 15 | 1997 | 5 | |
| 16 | 1992 | 3 | |
| 17 | 1989 | 15 | |
| 18 | 1986 | 9 | |
| 19 | 1986 | 133 | |
| 20 | 1976 | 0 |
About David McSherry
David McSherry is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics, having authored 49 papers that have together received 1.0k indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (22 papers), Data Mining Algorithms and Applications (11 papers), Semantic Web and Ontologies (7 papers), Rough Sets and Fuzzy Logic (7 papers), Biomedical Text Mining and Ontologies (6 papers), Multi-Agent Systems and Negotiation (5 papers), Topic Modeling (4 papers) and Constraint Satisfaction and Optimization (4 papers). The work is most often cited by research in Artificial Intelligence (577 citations), Information Systems (283 citations) and Management Science and Operations Research (95 citations). David McSherry has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Ken Fullerton, R. W. Stout, David Leake, Agnar Aamodt, Ramón López de Mántaras, Barry Smyth, Michael T. Cox, Kenneth D. Forbus, Susan Craw and Ian Watson. Their work appears in journals such as Artificial Intelligence Review, Knowledge-Based Systems, Artificial Intelligence in Medicine, The Knowledge Engineering Review and Lara D. Veeken.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.