Michael Cashmore

613 total citations
21 papers, 279 citations indexed

About

Michael Cashmore is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Michael Cashmore has authored 21 papers receiving a total of 279 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 6 papers in Computer Networks and Communications and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Michael Cashmore's work include AI-based Problem Solving and Planning (19 papers), Logic, Reasoning, and Knowledge (9 papers) and Robotic Path Planning Algorithms (5 papers). Michael Cashmore is often cited by papers focused on AI-based Problem Solving and Planning (19 papers), Logic, Reasoning, and Knowledge (9 papers) and Robotic Path Planning Algorithms (5 papers). Michael Cashmore collaborates with scholars based in United Kingdom, United States and Italy. Michael Cashmore's co-authors include Daniele Magazzeni, Maria Fox, Derek Long, Luca Iocchi, Tom Larkworthy, Erez Karpas, Andrew Coles, Wheeler Ruml, Jörg Hoffmann and Marcel Steinmetz and has published in prestigious journals such as IEEE Transactions on Automation Science and Engineering, Journal of Artificial Intelligence Research and IFAC-PapersOnLine.

In The Last Decade

Michael Cashmore

20 papers receiving 270 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael Cashmore United Kingdom 11 193 94 48 46 36 21 279
Iyad Tumar Palestinian Territory 8 88 0.5× 39 0.4× 58 1.2× 89 1.9× 23 0.6× 25 311
Felipe Trevizan Australia 9 148 0.8× 43 0.5× 12 0.3× 38 0.8× 19 0.5× 25 198
Kevin Leahy United States 10 100 0.5× 89 0.9× 6 0.1× 62 1.3× 32 0.9× 20 224
Enrico Scala Italy 9 191 1.0× 41 0.4× 8 0.2× 33 0.7× 18 0.5× 47 248
Mamun Abu-Tair United Kingdom 7 52 0.3× 127 1.4× 40 0.8× 89 1.9× 5 0.1× 28 271
Alain Appriou France 9 151 0.8× 30 0.3× 25 0.5× 34 0.7× 31 0.9× 16 239
Francesco M. Delle Fave United Kingdom 9 51 0.3× 36 0.4× 20 0.4× 109 2.4× 21 0.6× 15 209
Yihong Dong China 6 187 1.0× 26 0.3× 9 0.2× 17 0.4× 18 0.5× 16 287
Gianluca Torta Italy 7 87 0.5× 10 0.1× 10 0.2× 70 1.5× 24 0.7× 43 177

Countries citing papers authored by Michael Cashmore

Since Specialization
Citations

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

Fields of papers citing papers by Michael Cashmore

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Cashmore

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Cashmore. A scholar is included among the top collaborators of Michael Cashmore based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Cashmore. Michael Cashmore is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Yeoh, William, et al.. (2022). A Logic-Based Explanation Generation Framework for Classical and Hybrid Planning Problems. Journal of Artificial Intelligence Research. 73. 1473–1534. 6 indexed citations
3.
Cashmore, Michael, et al.. (2020). Planning for Hybrid Systems via Satisfiability Modulo Theories. Journal of Artificial Intelligence Research. 67. 235–283. 17 indexed citations
4.
Cashmore, Michael, et al.. (2020). A New Approach to Plan-Space Explanation: Analyzing Plan-Property Dependencies in Oversubscription Planning. Proceedings of the AAAI Conference on Artificial Intelligence. 34(6). 9818–9826. 17 indexed citations
5.
Cashmore, Michael, et al.. (2020). Using Machine Learning for Decreasing State Uncertainty in Planning. Journal of Artificial Intelligence Research. 69. 765–806. 2 indexed citations
6.
Cashmore, Michael, et al.. (2019). Replanning for Situated Robots. Proceedings of the International Conference on Automated Planning and Scheduling. 29. 665–673. 12 indexed citations
7.
Cashmore, Michael, et al.. (2019). Robustness Envelopes for Temporal Plans. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 7538–7545. 3 indexed citations
8.
Cashmore, Michael, et al.. (2018). Situated Planning for Execution Under Temporal Constraints. Strathprints: The University of Strathclyde institutional repository (University of Strathclyde). 2 indexed citations
9.
Cashmore, Michael, et al.. (2018). Strategic-Tactical Planning for Autonomous Underwater Vehicles over Long Horizons. 94. 3565–3572. 11 indexed citations
10.
Cashmore, Michael, et al.. (2018). Temporal Planning while the Clock Ticks. Proceedings of the International Conference on Automated Planning and Scheduling. 28. 39–46. 16 indexed citations
11.
Cashmore, Michael, et al.. (2017). Opportunistic Planning in Autonomous Underwater Missions. IEEE Transactions on Automation Science and Engineering. 15(2). 519–530. 27 indexed citations
12.
Cashmore, Michael, et al.. (2017). Decreasing Uncertainty in Planning with State Prediction. Research Portal (King's College London). 2032–2038. 4 indexed citations
13.
Cashmore, Michael, et al.. (2017). Short-Term Human-Robot Interaction through Conditional Planning and Execution. Proceedings of the International Conference on Automated Planning and Scheduling. 27. 540–548. 20 indexed citations
14.
Cashmore, Michael, et al.. (2017). Initial state prediction in planning. Strathprints: The University of Strathclyde institutional repository (University of Strathclyde). 1 indexed citations
15.
Cashmore, Michael, et al.. (2017). Proceedings of the 27th International Conference on Automated Planning and Scheduling, ICAPS 2017. 33 indexed citations
16.
Cashmore, Michael, et al.. (2015). Artificial Intelligence Planning for AUV Mission Control. IFAC-PapersOnLine. 48(2). 262–267. 6 indexed citations
17.
Cashmore, Michael, Maria Fox, Tom Larkworthy, Derek Long, & Daniele Magazzeni. (2014). AUV mission control via temporal planning. 6535–6541. 48 indexed citations
18.
Cashmore, Michael, Maria Fox, Tom Larkworthy, Derek Long, & Daniele Magazzeni. (2013). Planning inspection tasks for AUVs. 2013 OCEANS - San Diego. 1–8. 10 indexed citations
19.
Cashmore, Michael, Maria Fox, & Enrico Giunchiglia. (2013). Partially Grounded Planning as Quantified Boolean Formula. Proceedings of the International Conference on Automated Planning and Scheduling. 23. 29–36. 10 indexed citations
20.
Cashmore, Michael & Maria Fox. (2010). Planning as QBF. 1 indexed citations

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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