Cited by 3,457Artificial Intelligence ApplicationsAI-based Problem Solving and PlanningLogic, Reasoning, and Knowledge

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Principles of Artificial Intelligence

Nilsson, Nils J.·1982·Springer eBooks

DOI 10.1007/978-3-662-09438-9

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Maher, M. J. (1986) · Lecture notes in computer science · doi:10.1007/3-540-16492-8_91 · Cited by 31
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“If an admissible heuristic is available [ 22 ] one can use the A* algorithm, which ensures that the first solution found is the optimal one.”
Method
N-tuple features for OCR revisited
Jung, D.-M., Krishnamoorthy, M. S., Nagy, G., et al. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.506795 · Cited by 27
A library of problem-solving components based on the integration of the search paradigm with task and method ontologies
Motta, E., & Zdráhal, Z. (1998) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.1998.0214 · Cited by 27
“For instance, the A* search algorithm (Nilsson, 1980) can be viewed as a PSM for finding the cheapest path - according to some criterion - to a solution state in a state space.”
Method
Interpretation in design: the problem of tacit and explicit understanding in computer support of cooperative design
Stahl, G. (1993) · Cited by 28
Dantes: An expert system for real-time network troubleshooting
Mathonet, R., Cotthem, H. V., & Vanryckeghem, L. (1987) · Cited by 27
Range nesting
Jarke, M., & Koch, J. (1983) · Proceedings - ACM-SIGMOD International Conference on Management of Data · doi:10.1145/582192.582225 · Cited by 27
Parallel rule-based fuzzy inference on mesh-connected systolic arrays
Eshera, M. A., & Barash, S. C. (1989) · IEEE Expert · doi:10.1109/64.43283 · Cited by 27
A Connectionist System for Rule Based Reasoning with Multi-Place Predicates and Variables
Shastri, L., & Ajjanagadde, V. (1989) · doi:10.21236/ada225897 · Cited by 26
Collaborative Planning with Confidentiality
Kanovich, M., Rowe, P. D., & Scedrov, A. (2010) · Journal of Automated Reasoning · doi:10.1007/s10817-010-9190-1 · Cited by 26
“To illustrate the definitions we use an example common from the literature: the blocks world [26, 34, 40].”
BackgroundMethod
Optimal Dynamic Scheduling of Aircraft Arrivals at Congested Airports
Saraf, A. P., & SLATER, G. L. (2008) · Journal of Guidance Control and Dynamics · doi:10.2514/1.29464 · Cited by 26
Design of service systems using a knowledge‐based approach
Kaner, M., & Karni, R. (2007) · Knowledge and Process Management · doi:10.1002/kpm.292 · Cited by 26
Solving constraint optimization problems from CLP-style specifications using heuristic search techniques
Dasgupta, P., Chakrabarti, P. P., Dey, A., et al. (2002) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.991721 · Cited by 26
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Osipov, G. S., Panov, A. I., & Chudova, N. V. (2015) · Journal of Computer and Systems Sciences International · doi:10.1134/s106423071505010x · Cited by 26
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Lelis, L. H. S., Otten, L., & Dechter, R. (2013) · Cited by 26
Applications of decision support and expert systems in flexible manufacturing systems
Turban, E., & Sepehri, M. (1986) · Journal of Operations Management · doi:10.1016/0272-6963(86)90015-x · Cited by 26
A hybrid approach for mobile robot path planning in dynamic environments
Du, Z., Qu, D., Xu, F., et al. (2007) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2007.4522310 · Cited by 26
“Thanks for all suggestions and discussions from members of our Institute.”
Background
Conspiracy numbers and caching for searching and/or trees and theorem-proving
Elkan, C. (1989) · International Joint Conference on Artificial Intelligence · Cited by 26
Non-minimax search strategies for use against fallible opponents
Reibman, A., & Ballard, B. W. (1983) · National Conference on Artificial Intelligence · Cited by 26
Using simulated annealing to obtain optimal linear end-member mixtures of hyperspectral data
Penn, B. S. (2002) · Computers & Geosciences · doi:10.1016/s0098-3004(01)00114-5 · Cited by 26
When is Planning Decidable?**This work was supported in part by NSF Grant NSFD CDR-88003012 to the University of Maryland Systems Research Center, as well as NSF grants IRI-8907890 and IRI-9109755.
Erol, K., Nau, D. S., & Subrahmanian, V. S. (1992) · Elsevier eBooks · doi:10.1016/b978-0-08-049944-4.50031-8 · Cited by 26
“The idea is intuitively the same as that of Green (Green 1969, Nilsson 1980) (vis-a-vis the famous “Green’s formulation of planning”), with one difference: Green introduces function symbols even if the original language contained none: we introduce new constants.”
BackgroundMethod
A General Branch-and-Bound Formulation for AND/OR Graph and Game Tree Search
Kumar, V., Nau, D. S., & Kanal, L. N. (1988) · Springer eBooks · doi:10.1007/978-1-4613-8788-6_3 · Cited by 26
Medical image segmentation: automated design of border detection criteria from examples
Brejl, M., & Sonka, M. (1999) · Journal of Electronic Imaging · doi:10.1117/1.482684 · Cited by 26
Applying integer programming to AI planning
Vossen, T. W. M., Ball, M. O., Lotem, A., et al. (2000) · The Knowledge Engineering Review · doi:10.1017/s0269888900001065 · Cited by 26
Temporal agent programs
Dix, J., Kraus, S., & Subrahmanian, V. S. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00052-2 · Cited by 26
“Most traditional AI planning frameworks (Nilsson 1980) assume that an action’s effects are realized only after the entire action is successfully executed.”
BackgroundMethod
Using big data database to construct new GFuzzy text mining and decision algorithm for targeting and classifying customers
Liu, J.-W. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.04.003 · Cited by 25
Time-Optimal Path Following for Fixed-Wing Aircraft
Zhao, Y., & Tsiotras, P. (2012) · Journal of Guidance Control and Dynamics · doi:10.2514/1.57471 · Cited by 25
“, have been used to construct obstacle-free geometric paths in the configuration space [7–10].”
Method
The classical AI planning problems in the mirror of Horn linear logic: semantics, expressibility, complexity
Kanovich, M., & Vauzeilles, J. (2001) · Mathematical Structures in Computer Science · doi:10.1017/s0960129501003413 · Cited by 25
“Let us start with a classical example from Nilsson (1980).”
BackgroundMethodInfluential
A model-based approach to on-line process disturbance management: The models
Kim, I. S., Modarres, M., & Hunt, R. (1990) · Reliability Engineering & System Safety · doi:10.1016/0951-8320(90)90115-4 · Cited by 25
Plan Generation by Linear Proofs: On Semantics
Bibel, W., del Cerro, L. F., Fronhöfer, B., et al. (1989) · Informatik-Fachberichte · doi:10.1007/978-3-642-75100-4_7 · Cited by 25
Symbolic Heuristic Search Using Decision Diagrams
Hansen, E. A., Zhou, R., & Feng, Z. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45622-8_7 · Cited by 25
“LAO* is an extension of the classic search algorithm AO* [19].”
Background
Perspectives on artificial intelligence planning
Geffner, H. (2002) · National Conference on Artificial Intelligence · doi:10.5555/777092.777274 · Cited by 25
Knowledge-based modeling of marketing managers' problem solving behavior
Alpar, P. (1991) · International Journal of Research in Marketing · doi:10.1016/0167-8116(91)90003-p · Cited by 25
Strategies of searching for collision-free manipulator motions: automata theory approach
Jacak, W. A. (1989) · Robotica · doi:10.1017/s0263574700005439 · Cited by 25
The programming language GCLA — A definitional approach to logic programming
Aronsson, M., Eriksson, L.-H., Gäredal, A., et al. (1990) · New Generation Computing · doi:10.1007/bf03037453 · Cited by 25
An expert system for the evaluation of liver functional assessment
Lesmo, L., Marzuoli, M., Molino, G., et al. (1984) · Journal of Medical Systems · doi:10.1007/bf02221872 · Cited by 25
Intelligent scheduling: past, present and future
Prosser, P., & Buchanan, I. (1994) · Intelligent Systems Engineering · doi:10.1049/ise.1994.0010 · Cited by 25
On the number of acceptable task assignments in distributed computing systems
Shin, K. G., & Chen⋆, M.-S. (1990) · IEEE Transactions on Computers · doi:10.1109/12.46284 · Cited by 25
A knowledge-based vision system for industrial applications
Niemann, H. J., Br�nig, H., Salzbrunn, R., et al. (1990) · Machine Vision and Applications · doi:10.1007/bf01211848 · Cited by 25
“mal path is solved by an A*-algorithm (Nilsson 1982).”
Background
1Dynamic Courseware Generation: at the Cross Point of CAL, ITS and Authoring
Vassileva, J. (2014) · Cited by 25
Book review: Readings in Planning Edited by James Allen, James Hendler, and Austin Tate (Morgan Kaufmann, San Mateo, CA, 1990)
Hoebel, L. J. (1991) · ACM SIGART Bulletin · doi:10.1145/122344.1063807 · Cited by 25
Deep learning in fluorescence imaging and analysis
Mao, J., & He, H. (2024) · Journal of intelligent medicine. · doi:10.1002/jim4.17 · Cited by 26
iLPR: an Indian license plate recognition system
Saha, S., Basu, S., & Nasipuri, M. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2196-7 · Cited by 24
“Only one hidden layer is chosen during the MLP design mainly to keep the computational requirement of the experiment low without affecting their functional approximation capabilities [21].”
Engineering Design: Representation and Reasoning
Dym, C. L., & Brown, D. C. (2012) · Scholarship - Claremont (Claremont Colleges) · Cited by 24
Automatic Optimization of Wayfinding Design
Huang, H., Lin, N.-C., Barrett, L., et al. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2761820 · Cited by 24
“to design a cost function to evaluate each path, and then search for a path that corresponds to a low cost [30], [31], [32], [33].”
Background
Catalogue of Artificial Intelligence Tools
Bundy, A. (1986) · Springer eBooks · doi:10.1007/978-3-642-96964-5 · Cited by 24
2006: Celebrating 75 Years of AI - History and Outlook: The Next 25 Years
Schmidhuber, J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-77296-5_4 · Cited by 30
“Of course one cannot claim the old AI is devoid of formal research! The recent approach above, however, goes far beyond previous formally justified but very limited AI-related approaches ranging from linear perceptrons [17] to the A�-algorithm [ 18 ].”
Background
Network languages for concurrent multiagent systems
Stilman, B. (1997) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(97)00102-8 · Cited by 24
“A mathematical environment (a “glue”) for the formal implementation of this approach was developed following the theories of formal problem solving and planning by Nilsson [20], Fikes and Nilsson [34], Sacerdoti [22], McCarthy [35], McCarthy and Hayes [36], and others based on first order predicate calculus.”
BackgroundMethodInfluential
Interactive Decision Analysis and Interpretative Computer Intelligence
Wierzbicki, A. P. (1984) · Lecture notes in economics and mathematical systems · doi:10.1007/978-3-662-00184-4_1 · Cited by 24
Formation Planning and Control of UGVs with Trailers
Hao, Y., & Agrawal, S. K. (2005) · Autonomous Robots · doi:10.1007/s10514-005-4750-7 · Cited by 24
“The A* algorithm, introduced by Nilson ([33]), is an effective heuristic improvement of Dijkstra’s algorithm that yields a better average performance when one only needs the optimum path between two grid points in a directed graph with non-negative edge weights.”
Method
Flexible Automation of Cell Culture and Tissue Engineering Tasks
Knoll, A., Scherer, T., Poggendorf, I., et al. (2004) · Biotechnology Progress · doi:10.1021/bp049759v · Cited by 24
Web enabled expert systems using hyperlink-based inference
Kim, W., Song, Y. U., & Hong, J. S. (2004) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.08.011 · Cited by 24
“Finally, our conclusions are addressed in Section 6.”
BackgroundMethod
A coarse-to-fine approach for fast path finding for mobile robots
Lee, J.-Y., & Yu, W. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354686 · Cited by 24
A speech understanding and dialog system with a homogeneous linguistic knowledge base
Mast, M., Kümmert, F., Ehrlich, U., et al. (1994) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.273733 · Cited by 24
Effective and Efficient Coordination Strategies for Agile Crisis Response Organizations
Lab, D. (2006) · Cited by 24
ARTIFICIAL INTELLIGENCE-BASED SYSTEM REPRESENTATION AND SEARCH PROCEDURES FOR TRANSIT ROUTE NETWORK DESIGN
Baaj, M. H., & Mahmassani, H. S. (1992) · Transportation Research Record Journal of the Transportation Research Board · Cited by 24
Fragmenting relations horizontally using a knowledge-based approach
Shin, D. G., & Irani, K. B. (1991) · IEEE Transactions on Software Engineering · doi:10.1109/32.92906 · Cited by 24
On the Heuristic Nature of Medical Decision-Support Systems
Aliferis, C., & Miller, R. A. (1995) · Methods of Information in Medicine · doi:10.1055/s-0038-1634584 · Cited by 24
About the Paterson-Wegman linear unification algorithm
de Champeaux, D. (1986) · Journal of Computer and System Sciences · doi:10.1016/0022-0000(86)90003-6 · Cited by 24
“The monumental text [2] does not even mention it, while the textbook [3] gives only a reference to a report [4].”
Background
A pattern-directed approach to FMS scheduling
Shaw, M. J. (1988) · Annals of Operations Research · doi:10.1007/bf02186803 · Cited by 24
Speculative computation in multilisp
Osborne, R. B. (1990) · Lecture notes in computer science · doi:10.1007/bfb0024152 · Cited by 24
OR and AI approaches to decision support systems
van Hee, K. M., & Lapinski, A. (1988) · Decision Support Systems · doi:10.1016/0167-9236(88)90008-5 · Cited by 24
UVT: a unification-based tool for knowledge base verification
Polat, F., & Güvenir, H. A. (1993) · IEEE Expert · doi:10.1109/64.215225 · Cited by 24
Task Planning and Control Synthesis for Flexible Assembly Systems
Sanderson, A. C., & Homem-de-Mello, L. S. (1987) · Springer eBooks · doi:10.1007/978-3-642-87387-4_13 · Cited by 24
FMS scheduling as cooperative problem solving
Shaw, M. J. (1989) · Annals of Operations Research · doi:10.1007/bf02096612 · Cited by 24
Dynamic Sensing for Robots: An Analysis and Implementation
Beni, G., Hackwood, S., Hornak, L. A., et al. (1983) · The International Journal of Robotics Research · doi:10.1177/027836498300200205 · Cited by 24
“However, one may conceive of an intelligent algorithm (Nilsson 1980) that learns from the perceived information (regardless of the environment) to optimize its pattern-recognition efficiency after initial learning is done.”
Background
Toward representing management-domain knowledge
Baldwin, D., & Kasper, G. M. (1986) · Decision Support Systems · doi:10.1016/0167-9236(86)90093-x · Cited by 24
Abstraction heuristics for symbolic bidirectional search
Torralba, Á., López, C. L., & Borrajo, D. (2016) · International Joint Conference on Artificial Intelligence · Cited by 24
Applying Graph Neural Networks to Support Decision Making on Collective Intelligent Transportation Systems
da Silva, E. S., Pedrini, H., & dos Santos, A. L. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3257993 · Cited by 23
From Nodes to Roads: Surveying DRL Applications in MEC-Enhanced Terrestrial Wireless Networks
Ullah, S. A., Bibi, M., Hassan, S. A., Abou-Zeid, H., Qureshi, H. K., Jung, H., Mahmood, A., Gidlund, M., & Hossain, E. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3568035 · Cited by 29
Trajectory Tracking of a Nonholonomic Mobile Robot with Kinematic Disturbances: A Variable Structure Control Design
Martins, N. A., Elyoussef, E. S., Bertol, D. W., et al. (2011) · IEEE Latin America Transactions · doi:10.1109/tla.2011.5893772 · Cited by 23
Artificial Intelligence in Computer-Aided Auditing Techniques and Technologies (CAATTs) and an Application Proposal for Auditors
Aksoy, T., & Gürol, B. (2021) · Contributions to finance and accounting · doi:10.1007/978-3-030-72628-7_17 · Cited by 22
Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions
Núñez, R., Harris, A., Ibrahim, O. A., Keller, J. M., Wikle, C. K., Robinson, E., Zukerman, R. J., Siesky, B. A., Vercellin, A. C. V., Rowe, L. W., et al. (2022) · Photonics · doi:10.3390/photonics9110810 · Cited by 23
Analysis of Algorithms for Radial Basis Function Neural Network
Šťastný, J., & Škorpil, V. (2007) · Springer eBooks · doi:10.1007/978-0-387-74159-8_5 · Cited by 23
On optimizing the satisfiability (SAT) problem
Gu, J. H., Gu, Q., & Du, D. (1999) · Journal of Computer Science and Technology · doi:10.1007/bf02952482 · Cited by 23
Kuaba approach: Integrating formal semantics and design rationale representation to support design reuse
de Medeiros, A. P., & Schwabe, D. (2008) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060408000279 · Cited by 23
“The sub-graph of the DR made up of “ Question ” and “ Ideas ” is actually an AND/OR graph (Nilsson, 1986) that can be seen as a goal decomposition of the root node, which is always a “ Question ”.”
Background
An Analysis of Artificial Intelligence Techniques in Multiplayer Online Battle Arena Game Environments
Waltham, M., & Moodley, D. (2016) · Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT) · doi:10.1145/2987491.2987513 · Cited by 23
“AI in Computer Science is focused on allowing computers to act with a degree of intelligence [11].”
Background
HiMoP: A three-component architecture to create more human-acceptable social-assistive robots
Rodríguez-Lera, F. J., Olivera, V. M., Conde, M. Á., et al. (2018) · Cognitive Processing · doi:10.1007/s10339-017-0850-5 · Cited by 23
“Control architectures for robots are defined by three paradigms: top-down architectures (Nilsson 1980); bottom-up This article is part of the Special Issue on ‘Cognitive Robotics’ guest-edited by Antonio Bandera, Jorge Dias, and Luis Manso.”
Method
Real-time motion planning and safe navigation in dynamic multi-robot environments
Veloso, M., & Bruce, J. (2006) · Cited by 23
Scheduling of parallel machines to minimize total completion time subject to s-precedence constraints
Kim, E.-S., Sung, C.-S., & Lee, I.-S. (2007) · Computers & Operations Research · doi:10.1016/j.cor.2007.10.025 · Cited by 23
Al in computer games: Survey and perspectives
Cavazza, M. (2000) · Virtual Reality · doi:10.1007/bf01408521 · Cited by 23
A generic production rules-based system for on-line simulation, decision making and discrete process control
Iassinovski, S., Artiba, A., & Fagnart, C. (2007) · International Journal of Production Economics · doi:10.1016/j.ijpe.2006.08.028 · Cited by 23
Communications structure for sensory data in mobile robots
Posadas-Yagüe, J.-L., Pérez, P. F., Simó, J. E., et al. (2002) · Engineering Applications of Artificial Intelligence · doi:10.1016/s0952-1976(02)00055-6 · Cited by 23
“AI community has stated that an architecture for autonomous robots should include three functional elements: a sensory system, a planning system, and an execution or action system (Sense-Plan-Act: SPA) organised in a hierarchical structure ( Nilsson, 1980 ).”
Background
Knowledge-based systems
Mainzer, K. (1990) · Journal for General Philosophy of Science · doi:10.1007/bf01801416 · Cited by 23
Chip-level area routing
Liu, L.-C. E., Tseng, H.-P., & Sechen, C. (1998) · International Symposium on Physical Design · doi:10.1145/274535.274564 · Cited by 23
“Our tile expansion router essentially takes two routing patterns a metal expansion model and a via expansion modelto find a feasible route with minimum cost using an admissible A* algorithm [ 29 ].”
Method
Reactive Instructional Planning to Support Interacting Teaching Strategies
Vassileva, J. (1995) · Cited by 23
On the relations between intelligent backtracking and failure-driven explanation-based learning in constraint satisfaction and planning
Kambhampati, S. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00087-3 · Cited by 23
“The regression process described above is similar to the notion of weakest precondition computation studied in planning [46] and program verification [22].”
Method
Partial-order reduction and trail improvement in directed model checking
Edelkamp, S., Leue, S., & Lafuente, A. L. (2004) · International Journal on Software Tools for Technology Transfer · doi:10.1007/s10009-004-0151-z · Cited by 23
“An interesting property of A* is that if h is a lower bound of the distance to a goal state, then A* is admissible, which means that it will always return the shortest path to a goal state [51].”
Background
Task planning and action coordination in integrated sensor-based robots
Chen, C., & Trivedi, M. M. (1995) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.370189 · Cited by 23
Revealing Conceptual Structure in Data by Inductive Inference
Michalski, R. S., & Stepp, R. E. (1982) · George Mason University eBooks · doi:10.13021/mars/3395 · Cited by 23
On the use and internal structure of logic-based decision support systems
Chen, M. C., & Henschen, L. J. (1985) · Decision Support Systems · doi:10.1016/0167-9236(85)90240-4 · Cited by 23
On-Line Learning from Search Failures
Bhatnagar, N., & Mostow, J. (1994) · Machine Learning · doi:10.1023/a:1022613220324 · Cited by 23
“The standard regression method (Nilsson, 1980) is used, except that the meta-predicate current-goal regresses to itself; that is, regressing a condition of the form current-goal(G) back through an operator leaves the condition unchanged, since applying the operator did not change the goal.”
Method
A review of expert systems principles and their role in manufacturing systems
Rayson, P. (1985) · Robotica · doi:10.1017/s0263574700002393 · Cited by 23
“researchers who were disappointed with the results of using search techniques which predominately make use of the search tree paradigm.(10) Production systems do not 'explore' a search space.”
Multi-resolution Field D.
Ferguson, D. I., & Stentz, A. (2006) · IAS · Cited by 23
Three-dimensional boundary following
Cappelletti, J., & Rosenfeld, A. (1989) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(89)90105-9 · Cited by 23
Specification and analysis of real-time problem solvers
Hamidzadeh, B., & Shekhar, S. (1993) · IEEE Transactions on Software Engineering · doi:10.1109/32.238582 · Cited by 23
“A problem solver in AI consists of three main components, namely a global data base, a set of transition rules and a control system[2].”
Background
Artificial Intelligence-Aided Design (AIAD) for Structures and Engineering: A State-of-the-Art Review and Future Perspectives
Ao, Y., Li, S., & Duan, H. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10264-1 · Cited by 33
Consensus tree method for generating master assembly sequence
Kashkoush, M., & ElMaraghy, H. A. (2013) · Production Engineering · doi:10.1007/s11740-013-0499-6 · Cited by 22
“Instead of carrying out the assembly sequence generation in two consecutive phases, the Generative Assembly Process Planner (GAPP) developed by Laperriere and EIMaraghy [5] used an A* algorithm [19] which simultaneously applies both feasibility constraints (geometric and accessibility constraints) and optimization cri…”
Method
Enlarging the model of the human at the heart of human-centered AI: A social self-determination model of AI system impact
Bingley, W., Haslam, S. A., Steffens, N. K., Gillespie, N. A., Worthy, P., Curtis, C., Lockey, S., Bialkowski, A., Ko, R. K. L., & Wiles, J. (2023) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2023.101025 · Cited by 31
An Efficient Combinatorial Optimization Algorithm for Optimal Scheduling of Aircraft Arrivals at Congested Airports
Saraf, A. P., & SLATER, G. L. (2006) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2006.1655877 · Cited by 22

Authors: Nilsson, N. J.
Affiliations: SRI International (US)
Corresponding author: Nilsson, Nils J.
Venue: Springer eBooks
Publisher: Springer Berlin Heidelberg
Published: 1982
DOI: 10.1007/978-3-662-09438-9
OpenAlex: W2152475379
Cited by: 3,457
References: 0
Citations by year: 2026: 8 · 2025: 32 · 2024: 38 · 2023: 36 · 2022: 46 · 2021: 95 · 2020: 77 · 2019: 56 · 2018: 55 · 2017: 39 · 2016: 47 · 2015: 42 · 2014: 51 · 2013: 54 · 2012: 65
FWCI: 38.49
Type: book
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Springer eBooks (publishedVersion)
Virtual Defense Library (Ministerio de Defensa) (submittedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
Cumulative Index of Computer Aided Architectural Design (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
Keywords: artificial intelligence, data structures, knowledge representation, natural language processing, program synthesis, robotics, machine vision, automated theorem proving

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Principles of Artificial Intelligence

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