Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements
2004443 citationsRonen I. Brafman, Carmel Domshlak et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Carmel Domshlak
Since
Specialization
Citations
This map shows the geographic impact of Carmel Domshlak'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 Carmel Domshlak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carmel Domshlak more than expected).
This network shows the impact of papers produced by Carmel Domshlak. 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 Carmel Domshlak. The network helps show where Carmel Domshlak may publish in the future.
Co-authorship network of co-authors of Carmel Domshlak
This figure shows the co-authorship network connecting the top 25 collaborators of Carmel Domshlak.
A scholar is included among the top collaborators of Carmel Domshlak 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 Carmel Domshlak. Carmel Domshlak is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Karpas, Erez & Carmel Domshlak. (2009). Cost-optimal planning with landmarks. International Joint Conference on Artificial Intelligence. 1728–1733.99 indexed citations
6.
Brafman, Ronen I. & Carmel Domshlak. (2008). From one to many: planning for loosely coupled multi-agent systems. International Conference on Automated Planning and Scheduling. 28–35.108 indexed citations
Domshlak, Carmel, et al.. (2007). Cost-sharing approximations for h +. International Conference on Automated Planning and Scheduling. 240–247.7 indexed citations
9.
Brafman, Ronen I. & Carmel Domshlak. (2006). Factored planning: how, when, and when not. National Conference on Artificial Intelligence. 809–814.69 indexed citations
10.
Domshlak, Carmel & Jörg Hoffmann. (2006). Fast Probabilistic Planning Through Weighted Model Counting. Max Planck Institute for Plasma Physics. 243–252.14 indexed citations
11.
Hoffmann, Jörg, et al.. (2006). Friends or Foes? An AI Planning Perspective on Abstraction and Search. Max Planck Institute for Plasma Physics. 294–303.5 indexed citations
Domshlak, Carmel & Solomon Eyal Shimony. (2003). Efficient Probabilistic Reasoning in Bayes Nets with Mutual Exclusion and Context Specific Independence.. The Florida AI Research Society. 496–500.3 indexed citations
Domshlak, Carmel & Ronen I. Brafman. (2002). Structure and complexity in planning with unary operators. arXiv (Cornell University). 34–43.8 indexed citations
18.
Domshlak, Carmel & Ronen I. Brafman. (2002). CP-nets: Reasoning and Consistency Testing.. Principles of Knowledge Representation and Reasoning. 121–132.49 indexed citations
19.
Domshlak, Carmel, Ronen I. Brafman, & Solomon Eyal Shimony. (2001). Preference-based configuration of web page content. International Joint Conference on Artificial Intelligence. 1451–1456.22 indexed citations
20.
Domshlak, Carmel, et al.. (1998). FlexiMine - a flexible platform for KDD research and application construction. Knowledge Discovery and Data Mining. 184–188.10 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.