Mark d’Inverno

4.5k citations
98 papers · 1.7k indexed · h-index 21

Mark d’Inverno

92 papers receiving 1.5k citations

Peers

Mark d’Inverno
Comparison fields: 5 of 122
  • Artificial Intelligence 983
  • Management Information Systems 156
  • Management Science and Operations Research 204
  • Signal Processing 165
  • Human-Computer Interaction 81
Replace Hideaki Takeda with:
Hideaki Takeda Japan
Lynn Andrea Stein United States
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Countries citing papers authored by Mark d’Inverno

Since Specialization
Citations

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

Fields of papers citing papers by Mark d’Inverno

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Mark d’Inverno, 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 Mark d’Inverno Line = papers co-authored together Mark d’Inverno links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201816
2
Investigating Swarm Intelligence for Performance Prediction.
20162
3
A history of creativity for future AI research
20168
4
Experience driven design of creative systems
201610
5
Heroic versus collaborative AI for the arts
201515
6
Designing Educational Social Machines for Effective Feedback.
20147
7 20130
8 200652
9 200663
10 20045
11 200418
12 20041
13 20042
14 20041
15 20013
16 200184
17
Architecture for Agent Programming Languages
20003
18
Making and Breaking Engagements: An Operational Analysis of Agent Relationships
19971
19 19971
20
Understanding Autonomous Interaction.
19963

About Mark d’Inverno

Mark d’Inverno is a scholar working on Computer Science Applications, Signal Processing, Human-Computer Interaction, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 98 papers that have together received 1.7k indexed citations. Recurring topics across this work include Multi-Agent Systems and Negotiation (31 papers), Logic, Reasoning, and Knowledge (20 papers), Music Technology and Sound Studies (19 papers), Music and Audio Processing (16 papers), Semantic Web and Ontologies (10 papers), Mobile Agent-Based Network Management (9 papers), Auction Theory and Applications (8 papers) and Online Learning and Analytics (6 papers). The work is most often cited by research in Artificial Intelligence (983 citations), Management Information Systems (156 citations), Management Science and Operations Research (204 citations), Signal Processing (165 citations) and Human-Computer Interaction (81 citations). Mark d’Inverno has collaborated with scholars based in United Kingdom, Australia and Mexico. Frequent co-authors include G. Flucke, Fabiola López y López, Matthew Yee-King, Jon McCormack, Michael Wooldridge, David Kinny, Ronald Ashri, Michael Georgeff, Christophe Rhodes and Daniel Kudenko⋆. Their work appears in journals such as The Knowledge Engineering Review, Journal of New Music Research, Engineering Applications of Artificial Intelligence, Autonomous Agents and Multi-Agent Systems and IEEE Transactions on Multimedia.

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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