Andrew Cropper

425 citations
19 papers · 84 · h-index 5

Impact in

Papers in

    • Machine Learning and Algorithms 7
    • Logic, programming, and type systems 6
    • Logic, Reasoning, and Knowledge 6
    • Natural Language Processing Techniques 4
    • AI-based Problem Solving and Planning 3
    • Semantic Web and Ontologies 2
    • Software Engineering Research 5

Andrew Cropper

14 papers receiving 80 citations

Peers

Andrew Cropper
Comparison fields: 5 of 22
  • Artificial Intelligence 74
  • Information Systems and Management 5
  • Computational Theory and Mathematics 11
  • Information Systems 14
  • Software 2
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Citations per field
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Citations per year

Countries citing papers authored by Andrew Cropper

Since Specialization
Citations

This map shows the geographic impact of Andrew Cropper'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 Andrew Cropper with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Cropper more than expected).

Fields of papers citing papers by Andrew Cropper

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew Cropper. 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 Andrew Cropper. The network helps show where Andrew Cropper may publish in the future.

Co-authors

The 11 scholars most cited alongside Andrew Cropper, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Andrew Cropper Line = papers co-authored together Andrew Cropper links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 202122
2 202214
3 201814
4
Learning higher-order logic programs through abstraction and invention
201611
5
Learning efficient logical robot strategies involving composable objects
20159
6 20223
7 20232
8 20232
9
Typed meta-interpretive learning for proof strategies
20152
10
Knowledge Refactoring for Program Induction
20201
11 20241
12 20231
13 20201
14 20041
15 20240
16 20240
17 20240
18 19880
19 20140

About Andrew Cropper

Andrew Cropper is a scholar working on Artificial Intelligence, Information Systems, Computational Theory and Mathematics, Automotive Engineering and Control and Systems Engineering, having authored 19 papers that have together received 84 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (7 papers), Logic, programming, and type systems (6 papers), Logic, Reasoning, and Knowledge (6 papers), Software Engineering Research (5 papers), Natural Language Processing Techniques (4 papers), AI-based Problem Solving and Planning (3 papers), Semantic Web and Ontologies (2 papers) and Formal Methods in Verification (2 papers). The work is most often cited by research in Artificial Intelligence (74 citations), Information Systems and Management (5 citations), Computational Theory and Mathematics (11 citations), Information Systems (14 citations) and Software (2 citations). Andrew Cropper has collaborated with scholars based in United Kingdom, United States and Netherlands. Frequent co-authors include Stephen Muggleton, Sebastijan Dumančić, Gudmund Grov, Joshua Rule, Steven T. Piantadosi, Joshua B. Tenenbaum, Maxwell Nye, Alan Bundy, Stephen W. Nesbitt and Kevin Ellis. Their work appears in journals such as Machine Learning, Nature Communications, Journal of Artificial Intelligence Research, SAE technical papers on CD-ROM/SAE technical paper series and Journal of the Textile Institute.

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.

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