Mark Law

502 total citations
19 papers, 121 citations indexed

About

Mark Law is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Mark Law has authored 19 papers receiving a total of 121 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 3 papers in Computational Theory and Mathematics and 1 paper in Computer Networks and Communications. Recurrent topics in Mark Law's work include Logic, Reasoning, and Knowledge (11 papers), Multi-Agent Systems and Negotiation (8 papers) and Topic Modeling (5 papers). Mark Law is often cited by papers focused on Logic, Reasoning, and Knowledge (11 papers), Multi-Agent Systems and Negotiation (8 papers) and Topic Modeling (5 papers). Mark Law collaborates with scholars based in United Kingdom, United States and Spain. Mark Law's co-authors include Alessandra Russo, Krysia Broda, Elisa Bertino, Jorge Lobo, Richard A. Abrams, Bashar Nuseibeh, Seraphin Calo, Irene Manotas, Arosha K. Bandara and Blaine Price and has published in prestigious journals such as Experimental Brain Research, Artificial Intelligence and Machine Learning.

In The Last Decade

Mark Law

17 papers receiving 120 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark Law United Kingdom	6	95	25	17	14	11	19	121
Aleš Horák Czechia	8	112 1.2×	16 0.6×	20 1.2×	5 0.4×	7 0.6×	56	171
Michael Zhu United States	5	116 1.2×	18 0.7×	24 1.4×	11 0.8×	17 1.5×	14	151
Roberta Răileanu Israel	4	76 0.8×	6 0.2×	6 0.4×	4 0.3×	20 1.8×	11	114
Ales Kubicek Switzerland	1	102 1.1×	7 0.3×	19 1.1×	12 0.9×	25 2.3×	2	161
Piotr Nyczyk Switzerland	1	102 1.1×	7 0.3×	19 1.1×	12 0.9×	25 2.3×	2	161
Aman Madaan United States	5	107 1.1×	4 0.2×	23 1.4×	6 0.4×	18 1.6×	9	143
John Aslanides United Kingdom	4	142 1.5×	9 0.4×	18 1.1×	5 0.4×	22 2.0×	5	182
Patricia Martín-Rodilla Spain	7	58 0.6×	15 0.6×	22 1.3×	5 0.4×	23 2.1×	31	108
Nils Blach Switzerland	3	106 1.1×	7 0.3×	22 1.3×	15 1.1×	32 2.9×	4	173

Countries citing papers authored by Mark Law

Since Specialization

Citations

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

Fields of papers citing papers by Mark Law

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Law

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Law. A scholar is included among the top collaborators of Mark Law 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 Mark Law. Mark Law is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Law, Mark, et al.. (2023). FFNSL: Feed-Forward Neural-Symbolic Learner. Machine Learning. 112(2). 515–569. 4 indexed citations

Gebser, Martin, et al.. (2023). Learning to Break Symmetries for Efficient Optimization in Answer Set Programming. Proceedings of the AAAI Conference on Artificial Intelligence. 37(5). 6541–6549.

Law, Mark, Krysia Broda, & Alessandra Russo. (2022). Search Space Expansion for Efficient Incremental Inductive Logic Programming from Streamed Data. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 2697–2704. 2 indexed citations

Schekotihin, Konstantin, et al.. (2022). Efficient Lifting of Symmetry Breaking Constraints for Complex Combinatorial Problems. Theory and Practice of Logic Programming. 22(4). 606–622. 1 indexed citations

Law, Mark, Alessandra Russo, Krysia Broda, & Elisa Bertino. (2021). Scalable Non-observational Predicate Learning in ASP. 1936–1943. 4 indexed citations

Preece, Alun, Dave Braines, Federico Cerutti, et al.. (2021). Coalition situational understanding via explainable neuro-symbolic reasoning and learning. 61–61. 2 indexed citations

Law, Mark, et al.. (2021). Towards Neural-Symbolic Learning to support Human-Agent Operations. 1–8. 2 indexed citations

Law, Mark, Alessandra Russo, Elisa Bertino, Krysia Broda, & Jorge Lobo. (2020). FastLAS: Scalable Inductive Logic Programming Incorporating Domain-Specific Optimisation Criteria. Proceedings of the AAAI Conference on Artificial Intelligence. 34(3). 2877–2885. 21 indexed citations

White, Graham, John Ingham, Mark Law, & Alessandra Russo. (2019). Using an ASG Based Generative Policy to Model Human Rules. 99–103. 1 indexed citations

10.

Law, Mark, et al.. (2019). Towards a learning-algorithm agnostic generative policy model for coalitions. 18–18.

11.

Bertino, Elisa, Graham White, Jorge Lobo, et al.. (2019). Generative Policies for Coalition Systems - A Symbolic Learning Framework. Spiral (Imperial College London). 1590–1600. 3 indexed citations

12.

Law, Mark, Alessandra Russo, Elisa Bertino, Krysia Broda, & Jorge Lobo. (2019). Representing and Learning Grammars in Answer Set Programming. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 2919–2928. 14 indexed citations

13.

Law, Mark, et al.. (2019). Towards a Neural-Symbolic Generative Policy Model. 77. 4008–4016. 1 indexed citations

14.

Manotas, Irene, Mark Law, Geeth de Mel, et al.. (2019). A Generative Policy Model for Connected and Autonomous Vehicles. 1558–1565. 11 indexed citations

15.

Law, Mark, Alessandra Russo, & Krysia Broda. (2018). The complexity and generality of learning answer set programs. Artificial Intelligence. 259. 110–146. 23 indexed citations

16.

Russo, Alessandra, et al.. (2017). Machine Comprehension of Text Using Combinatory Categorial Grammar and Answer Set Programs.. Spiral (Imperial College London). 1 indexed citations

17.

Russo, Alessandra, et al.. (2016). An Abductive-Inductive Algorithm for Probabilistic Inductive Logic Programming.. Spiral (Imperial College London). 20–26. 1 indexed citations

18.

Çalıklı, Gül, Mark Law, Arosha K. Bandara, et al.. (2016). Privacy dynamics. Open Research Online (The Open University). 47–56. 21 indexed citations

19.

Abrams, Richard A. & Mark Law. (2002). Random visual noise impairs object-based attention. Experimental Brain Research. 142(3). 349–353. 9 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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