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.
Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
1999635 citationsCraig Boutilier, Steve Hanks et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Steve Hanks'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 Steve Hanks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steve Hanks more than expected).
This network shows the impact of papers produced by Steve Hanks. 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 Steve Hanks. The network helps show where Steve Hanks may publish in the future.
Co-authorship network of co-authors of Steve Hanks
This figure shows the co-authorship network connecting the top 25 collaborators of Steve Hanks.
A scholar is included among the top collaborators of Steve Hanks 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 Steve Hanks. Steve Hanks is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Svensén, Markus, Qing Xu, David Stern, Steve Hanks, & Chris Bishop. (2011). Broad vs Narrow: Modelling Strategies for Online Behavioural Targeting.1 indexed citations
3.
Svensén, Markus, et al.. (2011). Proceedings of the Fifth International Workshop on Data Mining and Audience Intelligence for Advertising (ADKDD).6 indexed citations
Madani, Omid, Steve Hanks, & Anne Condon. (1999). On the undecidability of probabilistic planning and infinite-horizon partially observable Markov decision problems. National Conference on Artificial Intelligence. 541–548.128 indexed citations
6.
Etzioni, Oren, Steve Hanks, Tao Jiang, et al.. (1996). Efficient Information Gathering on the Internet (Extended Abstract).1 indexed citations
7.
Williamson, Michael P. & Steve Hanks. (1996). Flaw selection strategies for value-directed planning. 237–244.19 indexed citations
8.
Boutilier, Craig, Thomas Dean, & Steve Hanks. (1996). Planning under uncertainty: structural assumptions and computational leverage. IOS Press eBooks. 157–171.68 indexed citations
9.
Kushmerick, Nicholas, Steve Hanks, & Daniel S. Weld. (1995). An algorithm for probabilistic planning. Artificial Intelligence. 76(1-2). 239–286.193 indexed citations
10.
Williamson, Michael P. & Steve Hanks. (1994). Utility-directed planning. National Conference on Artificial Intelligence. 1498–1498.2 indexed citations
11.
Williamson, Michael P. & Steve Hanks. (1994). Optimal planning with a goal-directed utility model. 176–181.43 indexed citations
12.
Kushmerick, Nicholas, Steve Hanks, & Daniel S. Weld. (1994). An algorithm for probabilistic least-commitment planning. National Conference on Artificial Intelligence. 1073–1078.54 indexed citations
13.
Draper, Denise L., Steve Hanks, & Daniel S. Weld. (1994). Probabilistic planning with information gathering and contingent execution. 31–36.127 indexed citations
Williamson, Michael P. & Steve Hanks. (1993). Exploiting domain structure to achieve efficient temporal reasoning. International Joint Conference on Artificial Intelligence. 152–157.5 indexed citations
16.
Etzioni, Oren, Steve Hanks, Daniel S. Weld, et al.. (1992). An Approach to Planning with Incomplete Information.. Principles of Knowledge Representation and Reasoning. 115–125.126 indexed citations
17.
Haddawy, Peter & Steve Hanks. (1992). Representations for Decision-Theoretic Planning: Utility Functions for Deadline Goals.. Principles of Knowledge Representation and Reasoning. 71–82.42 indexed citations
18.
Hanks, Steve. (1990). Practical temporal projection. National Conference on Artificial Intelligence. 158–163.31 indexed citations
19.
Hanks, Steve. (1988). Representing and computing temporally scoped beliefs. National Conference on Artificial Intelligence. 501–505.6 indexed citations
20.
Hanks, Steve & Drew McDermott. (1986). Default reasoning, nonmonotonic logics, and the frame problem. National Conference on Artificial Intelligence. 328–333.135 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.