Most cited papers in AI-based Problem Solving and Planning
What are the most cited papers in AI-based Problem Solving and Planning?
The most cited paper in AI-based Problem Solving and Planning is "Induction of decision trees" (J. R. Quinlan, 1986), with 12,440 citations. Next come "Probabilistic robotics" (8,102) and "Maintaining knowledge about temporal intervals" (7,599).
Showing 50 of 100 papers. Every paper with more than 824 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Induction of decision trees J. R. Quinlan · Machine Learning | Mar 1986 | 12,440 |
| 2 | Probabilistic robotics Sebastian Thrun · Communications of the ACM | Mar 2002 | 8,102 |
| 3 | Maintaining knowledge about temporal intervals James F. Allen · Communications of the ACM | Nov 1983 | 7,599 |
| 4 | Intelligent agents: theory and practice Michael Wooldridge, Nicholas R. Jennings · The Knowledge Engineering Review | Jun 1995 | 6,753 |
| 5 | Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches Agnar Aamodt, Enric Plaza · AI Communications | 1994 | 5,599 |
| 6 | Intelligence without representation Rodney A. Brooks · Artificial Intelligence | 1991 | 4,768 |
| 7 | Case-Based Reasoning Janet L. Kolodner · Cambridge University Press eBooks | 24 Apr 2005 | 4,251 |
| 8 | Planning and acting in partially observable stochastic domains Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra · Artificial Intelligence | May 1998 | 4,230 |
| 9 | Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems Steffen Lilholt Lauritzen, David J. Spiegelhalter · Journal of the Royal Statistical Society Series B (Statistical Methodology) | 1988 | 3,984 |
| 10 | Cognitive load theory, learning difficulty, and instructional design John Sweller · Learning and Instruction | 1994 | 3,772 |
| 11 | Knowledge engineering: Principles and methods Rudi Studer, V. Richard Benjamins, Dieter Fensel · Data & Knowledge Engineering | Mar 1998 | 3,663 |
| 12 | Acquisition of cognitive skill. John R. Anderson · Psychological Review | Jul 1982 | 3,600 |
| 13 | A theory of the learnable Leslie Gabriel Valiant · Communications of the ACM | 5 Nov 1984 | 3,307 |
| 14 | Intelligent Tutoring Systems John R. Anderson, C. Franklin Boyle, Brian J. Reiser · Science | 26 Apr 1985 | 3,134 |
| 15 | A theory of diagnosis from first principles Raymond Reiter · Artificial Intelligence | Apr 1987 | 2,930 |
| 16 | The structure of ill structured problems Herbert A. Simon · Artificial Intelligence | 1973 | 2,595 |
| 17 | Consistency in networks of relations Alan K. Mackworth · Artificial Intelligence | Feb 1977 | 2,463 |
| 18 | SOAR: An architecture for general intelligence John E. Laird, Allen Newell, Paul S. Rosenbloom · Artificial Intelligence | Sep 1987 | 2,427 |
| 19 | A fuzzy-logic-based approach to qualitative modeling Michio Sugeno, Toyoki Yasukawa · IEEE Transactions on Fuzzy Systems | Feb 1993 | 2,420 |
| 20 | A Bayesian method for the induction of probabilistic networks from data Gregory F. Cooper, Edward H. Herskovits · Machine Learning | Oct 1992 | 2,353 |
| 21 | Knowledge tracing: Modeling the acquisition of procedural knowledge Albert T. Corbett, John R. Anderson · User Modeling and User-Adapted Interaction | 1995 | 2,297 |
| 22 | Diagnosing multiple faults Johan de Kleer, Brian C. Williams · Artificial Intelligence | Apr 1987 | 2,053 |
| 23 | Learning Bayesian networks: The combination of knowledge and statistical data David E. Heckerman, Dan Geiger, David M. Chickering · Machine Learning | Sep 1995 | 2,048 |
| 24 | Qualitative process theory Kenneth D. Forbus · Artificial Intelligence | Dec 1984 | 2,024 |
| 25 | Motion Planning in Dynamic Environments Using Velocity Obstacles Paolo Fiorini, Zvi Shiller · The International Journal of Robotics Research | Jul 1998 | 2,012 |
| 26 | The computational complexity of probabilistic inference using bayesian belief networks Gregory F. Cooper · Artificial Intelligence | Mar 1990 | 1,968 |
| 27 | CYC Douglas B. Lenat · Communications of the ACM | Nov 1995 | 1,966 |
| 28 | Extremes and Related Properties of Random Sequences and Processes. Richard A. Davis, Malcolm R. Leadbetter, Georg Lindgren et al. · Journal of the American Statistical Association | Mar 1985 | 1,758 |
| 29 | An assumption-based TMS Johan de Kleer · Artificial Intelligence | Mar 1986 | 1,746 |
| 30 | PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains Maria Fox, Derek Long · Journal of Artificial Intelligence Research | Dec 2003 | 1,740 |
| 31 | Plans and situated actions: The problem of human-machine communication Philip E. Agre · Artificial Intelligence | Jun 1990 | 1,681 |
| 32 | The Open Motion Planning Library Ioan Alexandru Sucan, Mark Moll, Lydia E. Kavraki · IEEE Robotics & Automation Magazine | Dec 2012 | 1,645 |
| 33 | An overview of the KL-ONE Knowledge Representation System R. J. Brachman, James G. Schmolze · Cognitive Science | Jun 1985 | 1,623 |
| 34 | On the dempster-shafer framework and new combination rules Ronald R. Yager · Information Sciences | Mar 1987 | 1,571 |
| 35 | Depth-first iterative-deepening Richard E. Korf · Artificial Intelligence | Sep 1985 | 1,566 |
| 36 | Fast planning through planning graph analysis Avrim L. Blum, Merrick L. Furst · Artificial Intelligence | Feb 1997 | 1,566 |
| 37 | Building Watson: An Overview of the DeepQA Project David A. Ferrucci, Eric W. Brown, Jennifer Chu‐Carroll et al. · AI Magazine | Sep 2010 | 1,528 |
| 38 | A review of process fault detection and diagnosis Venkat Venkatasubramanian, Raghunathan Rengaswamy, Surya N. Kavuri · Computers & Chemical Engineering | Mar 2003 | 1,515 |
| 39 | A qualitative physics based on confluences Johan de Kleer, John Seely Brown · Artificial Intelligence | Dec 1984 | 1,498 |
| 40 | Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition Tom Dietterich · Journal of Artificial Intelligence Research | Nov 2000 | 1,459 |
| 41 | Principles of mixed-initiative user interfaces Eric Horvitz | 1999 | 1,454 |
| 42 | The structure-mapping engine: Algorithm and examples Brian C. Falkenhainer, Kenneth D. Forbus, Dedre Gentner · Artificial Intelligence | Nov 1989 | 1,451 |
| 43 | A model of inexact reasoning in medicine Edward Hance Shortliffe, Bruce G. Buchanan · Mathematical Biosciences | Apr 1975 | 1,448 |
| 44 | Generalization as search Tom M. Mitchell · Artificial Intelligence | Mar 1982 | 1,444 |
| 45 | Qualitative simulation Benjamin J. Kuipers · Artificial Intelligence | Sep 1986 | 1,420 |
| 46 | Sonar-based real-world mapping and navigation Alberto Elfes · IEEE Journal on Robotics and Automation | Jun 1987 | 1,398 |
| 47 | GRASP: a search algorithm for propositional satisfiability João P. Marques-Silva, Karem A. Sakallah · IEEE Transactions on Computers | May 1999 | 1,375 |
| 48 | Agent theories, architectures, and languages: A survey Michael Wooldridge, Nicholas R. Jennings · Lecture notes in computer science | 1995 | 1,363 |
| 49 | The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty Lee D. Erman, Frederick A. Hayes-Roth, Victor Lesser et al. · ACM Computing Surveys | Jun 1980 | 1,355 |
| 50 | How a cockpit remembers its speeds Elizabeth D. Hutchins · Cognitive Science | Sep 1995 | 1,304 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.