The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Strips: A new approach to the application of theorem proving to problem solving
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Related to program synthesis are the fields of automated theorem proving (Loveland, 2016) and automated planning (Ghallab et al., 2004).Both areas study methods for efficient automated reasoning over formal models, with the former focused on reasoning over mathematical theories and program logics, and with the latter, by analogy (Fikes & Nilsson, 1971), focused on reasoning over theories of”
From Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems (2024) · cites this paper as Fikes & Nilsson, 1971
In the citing paper
“The factored form subsumes STRIPS operators, where the add and delete lists are the difference between an object before and after, with conditional effects organized by type rather than enumerated [Fikes and Nilsson, 1971, Pednault, 1989, McDermott et al., 1998].”
From OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3 (2026) · cites this paper as Fikes and Nilsson, 1971
In the citing paper
“Regression planning presupposes a goal written as a conjunction of facts with operators that declare which facts they add and delete, the STRIPS form [Fikes and Nilsson, 1971].”
From OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3 (2026) · cites this paper as Fikes and Nilsson, 1971
Citations
Cites
(1997) · Naming the Mind: How Psychology Found its Language · doi:10.4135/9781446221815.n7 · Cited by 15,148
Some Philosophical Problems from the Standpoint of Artificial Intelligence
McCarthy, J., & Hayes, P. J. (1981) · Elsevier eBooks · doi:10.1016/b978-0-934613-03-3.50033-7 · Cited by 3,296
Problem-Solving Methods in Artificial Intelligence
Nilsson, N. J. (1971) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,607
Self-citationGPS : a case study in generality and problem solving
Ernst, G. W., & Newell, A. (1969) · Cited by 503
Green, C. (1981) · Elsevier eBooks · doi:10.1016/b978-0-934613-03-3.50019-2 · Cited by 459
Application of Theorem Proving to Problem Solving
Green, C. (1969) · doi:10.21236/ada459656 · Cited by 290
Toward automatic program synthesis
Manna, Z., & Waldinger, R. J. (1971) · Communications of the ACM · doi:10.1145/362566.362568 · Cited by 285
PROW: a step toward automatic program writing
Waldinger, R. J., & Lee, R. C. T. (1969) · International Joint Conference on Artificial Intelligence · Cited by 114
Extracting information from resolution proof trees
Luckham, D. C., & Nilsson, N. J. (1971) · Artificial Intelligence · doi:10.1016/0004-3702(71)90003-8 · Cited by 61
Self-citationMonitored Execution of Robot Plans Produced by Strips
Fikes, R. E. (1971) · doi:10.21236/ada459662 · Cited by 57
Self-citation
Research works printed but not linked: 1
- Garvey, 1969, User's guide to QA3.5 Question-Answering System, Stanford Research Institute Artifical Intelligence Group Technical Note 15
Cited by
Sorted by citations
Items 301–400 of 1,011
Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks
Keller, J. M., & Tahani, H. (1992) · International Journal of Approximate Reasoning · doi:10.1016/0888-613x(92)90018-u · Cited by 112
Guiding a power system restoration with an expert system
Kirschen, D. S., & Volkmann, T. L. (1991) · IEEE Transactions on Power Systems · doi:10.1109/59.76698 · Cited by 112
A pragmatics-based approach to ellipsis resolution
Carberry, S. (1989) · Cited by 111
Modeling the user's plans and goals
Carberry, S. (1988) · OLAC - Open Language Archives Community · Cited by 111
Design by derivational analogy:Issues in the automated replay of design plans
Mostow, J. (1989) · Artificial Intelligence · doi:10.1016/0004-3702(89)90048-9 · Cited by 111
The third AI summer: AAAI Robert S. Engelmore Memorial Lecture
Kautz, H. (2022) · AI Magazine · doi:10.1002/aaai.12036 · Cited by 155
Speeding up problem solving by abstraction: a graph oriented approach
Holte, R. C., Mkadmi, T., Zimmer, R. M., & MacDonald, A. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(95)00111-5 · Cited by 111
Dynamic scheduling in cellular manufacturing systems: A framework for networked decision making
Shaw, M. J. (1988) · Journal of Manufacturing Systems · doi:10.1016/0278-6125(88)90016-7 · Cited by 110
Active Perception and Reinforcement Learning
Whitehead, S. D., & Ballard, D. H. (1990) · Neural Computation · doi:10.1162/neco.1990.2.4.409 · Cited by 111
Rearrangement: A Challenge for Embodied AI
Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V., Levine, S., Malik, J., Mordatch, I., Mottaghi, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2011.01975 · Cited by 109
Cavazza, M., Lugrin, J.-L., Pizzi, D., & Charles, F. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291387 · Cited by 109
Plan synthesis: a logical perspective
Rosenschein, S. J. (1981) · International Joint Conference on Artificial Intelligence · Cited by 109
A framework for end-user instruction of a robot assistant for manufacturing
Guerin, K. R., Lea, C., Paxton, C., & Hager, G. D. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7140065 · Cited by 108
Learning relational affordance models for robots in multi-object manipulation tasks
Moldovan, B., Moreno, P., van Otterlo, M., Santos-Victor, J., & Raedt, L. D. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225042 · Cited by 108
2001. On the extraction, ordering, and usage of landmarks in planning
Porteous, J., Sebastiá, L., & Hoffmann, J. (2001) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 108
Dynamic Mission Planning for Multiple Mobile Robots
Brumitt, B. L., & Stentz, A. (1997) · Springer eBooks · doi:10.1007/978-1-4615-6325-9_12 · Cited by 108
Planning with Markov Decision Processes
Mausam, M., & Kolobov, A. (2012) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00426ed1v01y201206aim017 · Cited by 173
Planning for Semantic Web Services.
Evren Sirin, A., & Parsia, B. (2004) · Cited by 108
Generality in artificial intelligence
McCarthy, J. (2007) · ACM Turing award lectures · doi:10.1145/1283920.1283926 · Cited by 107
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Pappa, G. L., Ochoa, G., Hyde, M. R., Freitas, A. A., Woodward, J. R., & Swan, J. (2013) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-013-9186-9 · Cited by 106
Speech Acts for Dialogue Agents
Traum, D. R. (1999) · Applied logic series · doi:10.1007/978-94-015-9204-8_8 · Cited by 111
Rough Terrain Autonomous Mobility—Part 2: An Active Vision, Predictive Control Approach
Kelly, A., & Stentz, A. (1998) · Autonomous Robots · doi:10.1023/a:1008822205706 · Cited by 107
Wilensky, R. (1981) · Cognitive Science · doi:10.1207/s15516709cog0503_2 · Cited by 107
An Integrated Approach to Automated Semantic Web Service Composition through Planning
Hatzi, O., Vrakas, D., Νικολαϊδου, Μ., Bassiliades, N., Anagnostopoulos, D., & Vlahavas, I. P. (2011) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2011.20 · Cited by 106
Ginsberg, M. L., & Smith, D. E. (1988) · Artificial Intelligence · doi:10.1016/0004-3702(88)90020-3 · Cited by 106
Contingent planning under uncertainty via stochastic satisfiability
Majercik, S. M., & Littman, M. L. (2003) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00379-x · Cited by 106
A formal framework for business process modelling and design
Koubarakis, M., & Plexousakis, D. (2002) · Information Systems · doi:10.1016/s0306-4379(01)00055-2 · Cited by 105
FUTURE DIRECTIONS FOR DEVELOPING DECISION SUPPORT SYSTEMS*
BONCZEK, R. H., Holsapple, C. W., & Whinston, A. B. (1980) · Decision Sciences · doi:10.1111/j.1540-5915.1980.tb01165.x · Cited by 105
Chatila, R., Renaudo, E., Andries, M., Chavéz-García, R. O., Luce-Vayrac, P., Gottstein, R., Alami, R., Clodic, A., Devin, S., Girard, B., et al. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00088 · Cited by 117
OARPLAN: Generating project plans by reasoning about objects, actions and resources
Darwiche, A. Y., Levitt, R. E., & Hayes‐Roth, B. (1988) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060400000639 · Cited by 104
Using regression-match graphs to control search in planning
McDermott, D. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00010-7 · Cited by 104
Taxonomy of Trust-Relevant Failures and Mitigation Strategies
Tolmeijer, S., Weiss, A., Hanheide, M., Lindner, F., Powers, T. M., Dixon, C., & Tielman, M. L. (2020) · Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3319502.3374793 · Cited by 103
Integrating cognition, perception and action through mental simulation in robots
Cassimatis, N. L., Trafton, J., Bugajska, M., & Schultz, A. (2004) · Robotics Auton. Syst. · doi:10.1016/j.robot.2004.07.014 · Cited by 104
Robot Learning from Demonstration: A Task-level Planning Approach
Ekvall, S., & Kragić, D. (2008) · International Journal of Advanced Robotic Systems · doi:10.5772/5611 · Cited by 103
Splitting and reduction heuristics in automatic theorem proving
Bledsoe, W. W. (1971) · Artificial Intelligence · doi:10.1016/0004-3702(71)90004-x · Cited by 103
FORMULATING MULTIAGENT, DYNAMIC-WORLD PROBLEMS IN THE CLASSICAL PLANNING FRAMEWORK
Pednault, E. (1987) · Elsevier eBooks · doi:10.1016/b978-0-934613-30-9.50006-8 · Cited by 103
A support tool for writing multilingual instructions
Paris, C. L., Linden, K. V., Fischer, M., Hartley, A. F., Pemberton, L., Power, R., & Scott, D. (1995) · Cited by 103
Synthesizing plans that contain actions with context‐dependent effects1
Pednault, E. (1988) · Computational Intelligence · doi:10.1111/j.1467-8640.1988.tb00285.x · Cited by 103
An ontology, intelligent agent-based framework for the provision of semantic web services
García‐Sánchez, F., Valencia‐García, R., Martínez‐Béjar, R., & Fernández‐Breis, J. T. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.037 · Cited by 102
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
Danielczuk, M., Kurenkov, A., Balakrishna, A., Matl, M., Wang, D., Martín-Martín, R., Garg, A., Savarese, S., & Goldberg, K. Y. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794143 · Cited by 102
Ignoring irrelevant facts and operators in plan generation
Nebel, B., Dimopoulos, Y., & Koehler, J. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63912-8_97 · Cited by 102
Episodic skeletal-plan refinement based on temporal data
Tu, S. W., Kahn, M. G., Musen, M. A., Fagan, L. M., & Ferguson, J. C. (1989) · Communications of the ACM · doi:10.1145/76380.76384 · Cited by 102
Recent Trends in Task and Motion Planning for Robotics: A Survey
Guo, H., Wu, F., Qin, Y., Li, R., Li, K., & Li, K. (2023) · ACM Computing Surveys · doi:10.1145/3583136 · Cited by 120
A Roadmap for Cognitive Development in Humanoid Robots
Vernon, D., von Hofsten, C., & Fadiga, L. (2010) · Cognitive systems monographs · doi:10.1007/978-3-642-16904-5 · Cited by 104
Knowledge Preconditions for Actions and Plans
Morgenstern, L. (1988) · Elsevier eBooks · doi:10.1016/b978-0-934613-63-7.50021-8 · Cited by 101
A market architecture for multi-agent contracting
Collins, J. E., Youngdahl, B., Jamison, S., Mobasher, B., & Gini, M. L. (1998) · doi:10.1145/280765.280845 · Cited by 101
Autonomously constructing hierarchical task networks for planning and human-robot collaboration
Hayes, B., & Scassellati, B. M. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487760 · Cited by 100
A logic programming approach to knowledge-state planning
Eiter, T., Faber, W., Leone, N., Pfeifer, G., & Polleres, A. (2004) · ACM Transactions on Computational Logic · doi:10.1145/976706.976708 · Cited by 100
Kelly, A. (1996) · Cited by 100
MARVIN: A heuristic search planner with online macro-action learning
Coles, A., & Smith, A. (2007) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 100
Proactive Instructions for Furniture Assembly
Antifakos, S., Michahelles, F., & Schiele, B. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45809-3_27 · Cited by 100
COLA2: A Control Architecture for AUVs
Palomeras, N., El-Fakdi, A., Carreras, M., & Ridao, P. (2012) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2012.2205638 · Cited by 100
Planning under uncertainty: structural assumptions and computational leverage
Boutilier, C. E., Dean, T., & Hanks, S. (1996) · Cited by 99
Planning Multi-Step Error Detection and Recovery Strategies
Donald, B. R. (1990) · The International Journal of Robotics Research · doi:10.1177/027836499000900101 · Cited by 99
Planning or organizing: The implications of theories of activity for management of operations
Johnston, R. B., & Brennan, M. (1996) · Omega · doi:10.1016/0305-0483(96)00016-3 · Cited by 98
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
Chen, Y., Arkin, J., Dawson, C., Zhang, Y., Roy, N., & Fan, C. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611163 · Cited by 146
Artificial intelligence and brain theory: Unities and diversities
Arbib, M. A. (1975) · Annals of Biomedical Engineering · doi:10.1007/bf02390972 · Cited by 98
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Bazan, J. G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89876-4_26 · Cited by 97
Lima, P. U., & Custódio, L. (2005) · Springer eBooks · doi:10.1007/10992388_1 · Cited by 99
Controlling Narrative Generation with Planning Trajectories: The Role of Constraints
Porteous, J., & Cavazza, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10643-9_28 · Cited by 95
The Use of Aggregation in Causal Simulation
Weld, D. S. (1986) · Artificial Intelligence · doi:10.1016/0004-3702(86)90066-4 · Cited by 117
Artificial Intelligence and Virtual Worlds – Toward Human-Level AI Agents
Petrović, V. M. (2018) · IEEE Access · doi:10.1109/access.2018.2855970 · Cited by 102
State-variable planning under structural restrictions: algorithms and complexity
Jönsson, P., & Bäckström, C. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00003-4 · Cited by 94
A comparative illustration of AI planning-based web services composition
Oh, S., Lee, D., & Kumara, S. R. T. (2006) · ACM SIGecom Exchanges · doi:10.1145/1124566.1124568 · Cited by 94
Developments and Challenges for Autonomous Unmanned Vehicles
Finn, A., & Scheding, S. (2010) · Intelligent systems reference library · doi:10.1007/978-3-642-10704-7 · Cited by 109
A geometric approach to error detection and recovery for robot motion planning with uncertainty
Donald, B. R. (1988) · Artificial Intelligence · doi:10.1016/0004-3702(88)90056-2 · Cited by 93
Dialogue Models for Inquiry and Transaction
Hulstijn, J. (2000) · University of Twente Research Information · doi:10.3990/1.9789036514408 · Cited by 94
FMAP: Distributed cooperative multi-agent planning
Torreño, A., Onaindía, E., & Sapena, Ó. (2014) · Applied Intelligence · doi:10.1007/s10489-014-0540-2 · Cited by 92
Foster, M. E., Gaschler, A., Giuliani, M., Isard, A., Pateraki, M., & Petrick, R. P. A. (2012) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2388676.2388680 · Cited by 92
Shakey: From Conception to History
Kuipers, B. J., Feigenbaum, E. A., Hart, P. E., & Nilsson, N. J. (2017) · AI Magazine · doi:10.1609/aimag.v38i1.2716 · Cited by 96
Self-citationToo Many Cooks: Bayesian Inference for Coordinating Multi‐Agent Collaboration
Wu, S. A., Wang, R., Evans, J. A., Tenenbaum, J. B., Parkes, D. C., & Max Kleiman-weiner, A. (2021) · Topics in Cognitive Science · doi:10.1111/tops.12525 · Cited by 107
Learning complex action models with quantifiers and logical implications
Zhuo, H. H., Yang, Q., Hu, D. H., & Li, L. (2010) · Artificial Intelligence · doi:10.1016/j.artint.2010.09.007 · Cited by 92
OBDD-based Universal Planning for Synchronized Agents in Non-Deterministic Domains
Jensen, R. M., & Veloso, M. (2000) · Journal of Artificial Intelligence Research · doi:10.1613/jair.649 · Cited by 92
On the role of abstraction in case-based reasoning
Bergmann, R., & Wilke, W. (1996) · Lecture notes in computer science · doi:10.1007/bfb0020600 · Cited by 91
Integrating Guidance into Relational Reinforcement Learning
Driessens, K., & Džeroski, S. (2004) · Machine Learning · doi:10.1023/b:mach.0000039779.47329.3a · Cited by 88
Multiple representations of space underlying behavior
Lieblich, I., & Arbib, M. A. (1982) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00013959 · Cited by 91
Introduction:Paradigms for machine learning
Carbonell, J. (1989) · Artificial Intelligence · doi:10.1016/0004-3702(89)90045-3 · Cited by 91
State-space planning by integer optimization
Kautz, H., & Walser, J. P. (1999) · Cited by 90
Artificial Intelligence in Manufacturing Planning and Control
Bullers, W. I., Nof, S. Y., & Whinston, A. B. (1980) · A I I E Transactions · doi:10.1080/05695558008974527 · Cited by 90
Solving mechanics problems using meta-level inference
Bundy, A., Byrd, L., & Luger, G. F. (1979) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 90
McDermott, D., & Hendler, J. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(95)00034-c · Cited by 90
An Introduction to the Planning Domain Definition Language
Haslum, P., Lipovetzky, N., Magazzeni, D., & Muise, C. J. (2019) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00900ed2v01y201902aim042 · Cited by 89
ProgPrompt: program generation for situated robot task planning using large language models
Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D.-F., Tremblay, J., Fox, D., Thomason, J., & Garg, A. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10135-3 · Cited by 141
Strong planning in non-deterministic domains via model checking
Cimatti, A., Roveri, M., & Traverso, P. (1998) · Cited by 89
Wipke, W. T., Ouchi, G. I., & Krishnan, S. (1978) · Artificial Intelligence · doi:10.1016/0004-3702(78)90016-4 · Cited by 89
Reasoning with worlds and truth maintenance in a knowledge-based programming environment
Filman, R. E. (1988) · Communications of the ACM · doi:10.1145/42404.42405 · Cited by 89
Natural intelligence in artificial creatures
Balkenius, C. (1995) · Lund University Publications (Lund University) · Cited by 89
Temporal representation and reasoning in artificial intelligence: Issues and approaches
Chittaro, L., & Montanari, A. (2000) · Annals of Mathematics and Artificial Intelligence · doi:10.1023/a:1018900105153 · Cited by 108
Galles, D., & Pearl, J. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00047-7 · Cited by 84
Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search
Nguyen, X., Subbarao Kambhampati, B., & Sanchez, R. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00158-8 · Cited by 88
Strong Cyclic Planning Revisited
Daniele, M., Traverso, P., & Vardi, M. Y. (2000) · Lecture notes in computer science · doi:10.1007/10720246_3 · Cited by 87
Formalizing planning knowledge for hierarchical planning
Yang, Q. (1990) · Computational Intelligence · doi:10.1111/j.1467-8640.1990.tb00126.x · Cited by 87
A framework of knowledge-based assembly planning
Huang, Y. F., & Lee, C. S. G. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1991.131647 · Cited by 87
ADL and the State-Transition Model of Action
Pednault, E. (1994) · Journal of Logic and Computation · doi:10.1093/logcom/4.5.467 · Cited by 86
Alterman, R. (1986) · National Conference on Artificial Intelligence · Cited by 86
Inducing programs in a direct-manipulation environment
Maulsby, D., & Witten, I. H. (1989) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/67449.67463 · Cited by 85
Explanatory Diagnosis: Conjecturing Actions to Explain Observations
Mcllraith, S. A. (1999) · Artificial intelligence · doi:10.1007/978-3-642-60211-5_13 · Cited by 85
Efficient Macroscopic Urban Traffic Models for Reducing Congestion: A PDDL+ Planning Approach
Vallati, M., Magazzeni, D., Schutter, B. D., Chrpa, L., & McCluskey, T. L. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10399 · Cited by 85
Discrete Mathematics and its applications
Oxley, A. (2010) · Teaching Mathematics and its Applications An International Journal of the IMA · doi:10.1093/teamat/hrq007 · Cited by 85
A Formal Model for Situated Multi-Agent Systems
Weyns, D., Distrinet, A., & Holvoet, T. (2001) · Lirias · Cited by 85
Metadata
- Authors
- Fikes, R. E., & Nilsson, N. J.
- Affiliations
- SRI International (US)
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1971-12
- Volume
- 2
- Issue
- 3-4
- Pages
- 189-208
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2337392266
- Cited by
- 4,882
- References
- 13
- Citations by year
- 2026: 53 · 2025: 86 · 2024: 71 · 2023: 94 · 2022: 132 · 2021: 135 · 2020: 138 · 2019: 112 · 2018: 169 · 2017: 133 · 2016: 114 · 2015: 128 · 2014: 165 · 2013: 159 · 2012: 157
- FWCI
- 16.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence (publishedVersion)
- American Association for Artificial Intelligence eBooks
- International Joint Conference on Artificial Intelligence
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Constraint Satisfaction and Optimization
- Keywords
- STRIPS, problem solving, theorem proving, means-ends analysis, world models, first-order logic, operator sequences, goal satisfaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
