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.
This map shows the geographic impact of Mark Boddy'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 Boddy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Boddy more than expected).
This network shows the impact of papers produced by Mark Boddy. 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 Boddy. The network helps show where Mark Boddy may publish in the future.
Co-authorship network of co-authors of Mark Boddy
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Boddy.
A scholar is included among the top collaborators of Mark Boddy 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 Boddy. Mark Boddy 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
1.
Johnston, Steven, et al.. (2016). Exploiting Time Series Data for Task Prediction and Diagnosis in an Intelligent Guidance System.. 132–141.
Goldman, Robert P., et al.. (2007). "Unrolling" complex task models into MDPs. National Conference on Artificial Intelligence. 23–30.2 indexed citations
4.
Musliner, David J., et al.. (2006). Coordinated Plan Management Using Multiagent MDPs. National Conference on Artificial Intelligence. 73–80.31 indexed citations
Boddy, Mark, et al.. (2005). Course of action generation for cyber security using classical planning. International Conference on Automated Planning and Scheduling. 12–21.81 indexed citations
7.
Harp, Steven A., et al.. (2005). Automated Vulnerability Analysis Using AI Planning.. National Conference on Artificial Intelligence. 46–53.3 indexed citations
8.
Johnson, Daniel P., et al.. (2003). A method for global optimization of large systems of quadratic constraints.
9.
Goldman, Robert P., et al.. (1997). Dynamic abstraction planning. National Conference on Artificial Intelligence. 16. 680–686.7 indexed citations
10.
Goldman, Robert P. & Mark Boddy. (1996). Expressive planning and explicit knowledge. 110–117.59 indexed citations
11.
Boddy, Mark & Robert P. Goldman. (1994). Empirical results on scheduling and dynamic backtracking. 431–434.6 indexed citations
12.
Boddy, Mark, et al.. (1994). Constraint based scheduling for the Goddard Space Flight Center distributed Active Archive Center's data archive and distribution system.1 indexed citations
13.
Goldman, Robert P. & Mark Boddy. (1994). Conditional linear planning. 80–85.32 indexed citations
14.
Boddy, Mark, et al.. (1993). Scheduling with partial orders and a causal model. NASA Technical Reports Server (NASA).4 indexed citations
15.
Boddy, Mark. (1991). Anytime problem solving using dynamic programming. National Conference on Artificial Intelligence. 738–743.40 indexed citations
Boddy, Mark & Thomas Dean. (1989). Solving time-dependent planning problems. International Joint Conference on Artificial Intelligence. 979–984.201 indexed citations
18.
Boddy, Mark, Robert P. Goldman, Keiji Kanazawa, & Lynn Andrea Stein. (1989). Investigations of Model-Preference Defaults.1 indexed citations
Dean, Thomas & Mark Boddy. (1987). Incremental causal reasoning. National Conference on Artificial Intelligence. 196–201.12 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.