Countries citing papers authored by Hermann Kaindl
Since
Specialization
Citations
This map shows the geographic impact of Hermann Kaindl'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 Hermann Kaindl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hermann Kaindl more than expected).
This network shows the impact of papers produced by Hermann Kaindl. 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 Hermann Kaindl. The network helps show where Hermann Kaindl may publish in the future.
Co-authorship network of co-authors of Hermann Kaindl
This figure shows the co-authorship network connecting the top 25 collaborators of Hermann Kaindl.
A scholar is included among the top collaborators of Hermann Kaindl 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 Hermann Kaindl. Hermann Kaindl is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kaindl, Hermann, et al.. (2020). Towards Requirements Engineering for Superintelligence Safety..2 indexed citations
4.
Kaindl, Hermann & Davor Svetinović. (2019). Avoiding Undertrust and Overtrust..3 indexed citations
5.
Kaindl, Hermann, et al.. (2011). Revisiting the Requirements Communication Problem from a Knowledge Management Perspective. International Conference on Software Engineering Advances. 595–596.1 indexed citations
Kaindl, Hermann, et al.. (2004). A case study of revisiting best-first vs. depth-first search. European Conference on Artificial Intelligence. 141–145.11 indexed citations
8.
Kaindl, Hermann. (2000). Scalable Search in Computer Chess – Algorithmic Enhancements and Experiments at High Search Depths: Ernst A. Heinz, Vieweg, 2000. AI Communications. 13(4). 279–279.1 indexed citations
9.
Kaindl, Hermann & John M. Carroll. (1999). Symbolic Modeling in Practice - Introduction.. Communications of the ACM. 42. 28–30.1 indexed citations
10.
Kaindl, Hermann, et al.. (1998). Back-up of Heuristic Values: Minimaxing vs. Product Propagation.. European Conference on Artificial Intelligence. 665–669.1 indexed citations
Kaindl, Hermann, et al.. (1996). Dynamic improvements of heuristic evaluations during search. National Conference on Artificial Intelligence. 311–317.3 indexed citations
Kaindl, Hermann & Aliasghar Khorsand. (1994). Memory-bounded bidirectional search. National Conference on Artificial Intelligence. 1359–1364.16 indexed citations
15.
Kaindl, Hermann, et al.. (1994). Improvements on linear-space search algorithms. European Conference on Artificial Intelligence. 155–159.4 indexed citations
16.
Kaindl, Hermann, et al.. (1993). Bidirectional best-first search with bounded error: summary of results. International Joint Conference on Artificial Intelligence. 217–223.13 indexed citations
17.
Shams, Reza, Hermann Kaindl, & Helmut Horacek. (1991). Using aspiration windows for minimax algorithms. International Joint Conference on Artificial Intelligence. 192–197.2 indexed citations
18.
Kaindl, Hermann, et al.. (1989). The reason for the benefits of minimax search. International Joint Conference on Artificial Intelligence. 322–327.6 indexed citations
Kaindl, Hermann. (1983). Searching to variable depth in computer chess. International Joint Conference on Artificial Intelligence. 760–762.13 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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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.