Erik T. Mueller

34 papers receiving 743 citations

Peers

Erik T. Mueller
Comparison fields: 5 of 107
  • Artificial Intelligence 608
  • Computer Networks and Communications 130
  • Information Systems 83
  • Computer Vision and Pattern Recognition 66
  • Computational Theory and Mathematics 66
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Countries citing papers authored by Erik T. Mueller

Since Specialization
Citations

This map shows the geographic impact of Erik T. Mueller'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 Erik T. Mueller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erik T. Mueller more than expected).

Fields of papers citing papers by Erik T. Mueller

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Erik T. Mueller. 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 Erik T. Mueller. The network helps show where Erik T. Mueller may publish in the future.

Co-authorship network of co-authors of Erik T. Mueller

This figure shows the co-authorship network connecting the top 25 collaborators of Erik T. Mueller. A scholar is included among the top collaborators of Erik T. Mueller 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 Erik T. Mueller. Erik T. Mueller 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
#WorkIndexed citations
1 1
2 1
3 3
4 10
5
Thoughtful Surprise Generation as a Computational Creativity Challenge.
1
6 8
7 0
8 3
9
Question Answering in Natural Language Narratives Using Symbolic Probabilistic Reasoning.
3
10 204
11
Preface: Computational Models of Narrative
2
12
Discrete Event Calculus Reasoner Documentation
4
13
Understanding Goal-Based Stories through Model Finding and Planning.
7
14 13
15 10
16
A Tool for Satisfiability-Based Commonsense Reasoning in the Event Calculus.
13
17
Natural Language Processing with ThoughtTreasure
13
18
Daydreaming in humans and machines : a computer model of the stream of thought
31
19
Daydreaming and computation: a computer model of everyday creativity, learning and emotions in the human stream of thought
5
20 45

About Erik T. Mueller

Erik T. Mueller is a scholar working on Artificial Intelligence, Health Information Management and Experimental and Cognitive Psychology, having authored 36 papers that have together received 867 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Semantic Web and Ontologies (9 papers) and Logic, Reasoning, and Knowledge (8 papers). The work is most often cited by research in Health Informatics (33 citations), Artificial Intelligence (608 citations) and Computer Networks and Communications (130 citations). Erik T. Mueller has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Sugato Bagchi, David Ferrucci, David Gondek, Anthony Levas, Johanna D. Moore, Gerald J. Popek, Michael G. Dyer, Patrick Henry Winston, Hannaneh Hajishirzi and Siddharth Patwardhan. Their work appears in journals such as Scientific Reports, Communications of the ACM and Artificial Intelligence.

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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