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.
SOAR: An architecture for general intelligence
19871.5k citationsJohn E. Laird, Allen Newell et al.profile →
This map shows the geographic impact of John E. Laird'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 John E. Laird with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John E. Laird more than expected).
This network shows the impact of papers produced by John E. Laird. 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 John E. Laird. The network helps show where John E. Laird may publish in the future.
Co-authorship network of co-authors of John E. Laird
This figure shows the co-authorship network connecting the top 25 collaborators of John E. Laird.
A scholar is included among the top collaborators of John E. Laird 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 John E. Laird. John E. Laird is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Laird, John E., et al.. (2013). A Preliminary Functional Analysis of Memory in the Word Sense Disambiguation Task.
4.
Li, Justin & John E. Laird. (2011). Preliminary Evaluation of Long-term Memories for Fulfilling Delayed Intentions. National Conference on Artificial Intelligence.1 indexed citations
Laird, John E., et al.. (2007). Extending cognitive architecture with episodic memory. National Conference on Artificial Intelligence. 1560–1565.59 indexed citations
7.
Laird, John E., et al.. (2007). SORTS: A Human-Level Approach to Real-Time Strategy AI. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 3(1). 55–60.22 indexed citations
Magerko, Brian, et al.. (2004). AI Characters and Directors for Interactive Computer Games.65 indexed citations
11.
Laird, John E. & Michael van Lent. (2000). Human-Level AI's Killer Application: Interactive Computer Games. National Conference on Artificial Intelligence. 1171–1178.190 indexed citations
12.
Lent, Michael van & John E. Laird. (1999). Learning Hierarchical Performance Knowledge by Observation. International Conference on Machine Learning. 229–238.23 indexed citations
13.
Wallace, Scott A. & John E. Laird. (1999). Toward a Methodology for AI Architecture Evaluation: Comparing Soar And CLIPS.2 indexed citations
14.
Laird, John E., et al.. (1998). Modeling dual-task performance improvement: Casting executive process knowledge acquisition as strategy refinement.. Deep Blue (University of Michigan).10 indexed citations
15.
Wray, Robert E. & John E. Laird. (1998). Maintaining consistency in hierarchical reasoning. National Conference on Artificial Intelligence. 928–935.3 indexed citations
16.
Laird, John E., Paul S. Rosenbloom, & Allen Newell. (1993). Overgeneralization during knowledge compilation in Soar. MIT Press eBooks. 387–398.4 indexed citations
Golding, Andrew R., Paul S. Rosenbloom, & John E. Laird. (1987). Learning general search control from outside guidance. MIT Press eBooks. 334–337.4 indexed citations
19.
Laird, John E., Allen Newell, & Paul S. Rosenbloom. (1984). Towards chunking as a general learning mechanism. MIT Press eBooks. 188–192.11 indexed citations
20.
Laird, John E. & Allen Newell. (1983). A universal weak method: summary of results. International Joint Conference on Artificial Intelligence. 771–773.42 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.