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“The most widely accepted architecture for robotic systems is known as “sense, plan, act” (Nilsson, 1982).”
From Lessons for Robotics From the Control Architecture of the Octopus (2022) · cites this paper as Nilsson, 1982
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On the foundations of intelligent processes—I. An evolving model for pattern learning
Goldfarb, L. (1990) · Pattern Recognition · doi:10.1016/0031-3203(90)90037-l · Cited by 64
Document analysis-from pixels to contents
Schürmann, J., Bartneck, N., Bayer, T. A., et al. (1992) · Proceedings of the IEEE · doi:10.1109/5.156473 · Cited by 64
On the role of stored internal state in the control of autonomous mobile robots
Gat, E. (1993) · AI Magazine · doi:10.1609/aimag.v14i1.1034 · Cited by 63
Designing Fuzzy Rule Based Expert System for Cyber Security
Göztepe, K. (2012) · Cited by 63
Learning Generalized Policies from Planning Examples Using Concept Languages
Martín, M., & Geffner, H. (2004) · Applied Intelligence · doi:10.1023/b:apin.0000011138.20292.dd · Cited by 59
On algebraic and graph structural properties of assembly Petri net
Suzuki, T., Kanehara, T., Inaba, A., et al. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1993.291907 · Cited by 63
Subrahmanian, V. S., Eiter, T., & Pick, G. G. (1998) · University Libraries (University of Maryland) · Cited by 63
AEMS: an anytime online search algorithm for approximate policy refinement in large POMDPs
Ross, S., & Chaib-draa, B. (2007) · Cited by 64
Ayodele, T. O. (2010) · InTech eBooks · doi:10.5772/9374 · Cited by 73
Zadeh, L. A. (2005) · Springer eBooks · doi:10.1007/bfb0006964 · Cited by 62
Gari: An Expert System for Process Planning
Descotte, Y., & Latombe, J. (1984) · Springer eBooks · doi:10.1007/978-1-4613-2811-7_15 · Cited by 62
Where there's a Will there's an agent
Moffat, D., & Frijda, N. H. (1995) · Lecture notes in computer science · doi:10.1007/3-540-58855-8_16 · Cited by 62
A Blockchain-Based Traceable Self-Tallying E-Voting Protocol in AI Era
Li, H., Li, Y., Yu, Y., Wang, B., & Chen, K. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3011928 · Cited by 63
A systematic review of emerging information technologies for sustainable data-centric health-care
Zahid, A., Poulsen, J. K., Sharma, R., & Wingreen, S. C. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104420 · Cited by 67
Feature-based similarity search in graph structures
Yan, X., Zhu, F., Yu, P. S., et al. (2006) · ACM Transactions on Database Systems · doi:10.1145/1189769.1189777 · Cited by 61
“These networks are usually very complex with multilevel structures embedded [Kanehisa and Goto 2000].”
BackgroundA new algorithm for shortest paths among obstacles in the plane
Mitchell, J. S. B. (1991) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/bf01530888 · Cited by 61
A Heuristic Search Approach for a Nonstationary Stochastic Shortest Path Problem with Terminal Cost
Bander, J. L., & White, C. C. (2002) · Transportation Science · doi:10.1287/trsc.36.2.218.562 · Cited by 61
“AO∗ is a well-known solution technique that is usually applied to a decomposable production system (Nilsson 1980) which defines an implicit AND/OR graph.”
BackgroundMethodHA, T. M., Zimmermann, M., & Bunke, H. (1998) · Pattern Recognition · doi:10.1016/s0031-3203(97)00050-2 · Cited by 61
Automating operating procedure synthesis for batch processes
Viswanathan, S., Johnsson, C., Srinivasan, R., et al. (1998) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(98)00228-2 · Cited by 61
Towards real-time global motion planning in a dynamic environment using the NLVO concept
Large, F., Sekhavat, S., Shiller, Z., et al. (2003) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/irds.2002.1041458 · Cited by 61
Real-time eye feature tracking from a video image sequence using Kalman filter
Xie, X., Sudhakar, R., & Zhuang, H. (1995) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.478445 · Cited by 61
Large language models for medicine: a survey
Zheng, Y., Gan, W., Chen, Z., Qi, Z., Liang, Q., & Yu, P. S. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02318-w · Cited by 90
“As traditional meth-ods struggle to cope with the vast volumes of information and intricate medical terminology, artificial intelligence (AI) technologies have emerged as pivotal tools in addressing these complexities [2, 3].”
Design and evaluation of spatial similarity approaches for image retrieval
Petrakis, E. G. M. (2002) · Image and Vision Computing · doi:10.1016/s0262-8856(01)00077-4 · Cited by 60
“Traditionally, this can be formulated as a tree search problem which can be solved by an algorithm [25].”
BackgroundGraph minor approach for application mapping on CGRAs
Chen, L., & Mitra, T. (2012) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2012.6412149 · Cited by 60
Speculative computation in multilisp
Osborne, R. B. (1990) · doi:10.1145/91556.91644 · Cited by 60
Self-Supervised Classification for Planetary Rover Terrain Sensing
Brooks, C. A., & Iagnemma, K. D. (2007) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2007.352693 · Cited by 60
“Given an accurate map of the ease of traversability of terrain, existing path planning algorithms can generate a route to a target which avoids known obstacles [1] [2] [3].”
BackgroundPerceptual organization as nested control
Leyton, M. (1984) · Biological Cybernetics · doi:10.1007/bf00346136 · Cited by 60
Complete solution of the eight-puzzle and the benefit of node ordering in IDA
Reinefeld, A. (1993) · Cited by 58
The D* Algorithm for Real-Time Planning of Optimal Traverses
Stentz, A. (1994) · Cited by 59
Application of Heuristic Graph Search to Test-Point Selection for Analog Fault Dictionary Techniques
Yang, C., Tian, S., & Long, B. (2008) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2006725 · Cited by 59
“More details about the breadth-first graph search algorithm can be found in the literature [16].”
BackgroundParallel Algorithms for Real-time Motion Planning
McNaughton, M. (2011) · KiltHub Repository · doi:10.1184/r1/6720992 · Cited by 61
Michie, S., Thomas, J., Aonghusa, P. M., West, R., Johnston, M., Kelly, M. P., Shawe-Taylor, J. S., Hastings, J., Bonin, F., & O’Mara-Eves, A. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15900.1 · Cited by 58
KNOWLEDGE REPRESENTATION: A BRIEF REVIEW
Vickery, B. C. (1986) · Journal of Documentation · doi:10.1108/eb026790 · Cited by 59
“A further example of transforming a knowledge representation is given here, taken from Nilsson.(16) The following sentences are shown in possible predicate logic form:”
BackgroundText Line Segmentation in Handwritten Document Using a Production System
Nicolas, S., Paquet, T., & Heutte, L. (2004) · International Workshop on Frontiers in Handwriting Recognition (IWFHR) · doi:10.1109/iwfhr.2004.100 · Cited by 59
“In a production system, the global database is manipulated thanks to some defined operations (the production rules), under the control of a global control strategy (search procedure) [8].”
BackgroundMethodA parallel implementation of iterative-deepening-A
Rao, V. N., Kumar, V., & Ramesh, K. (1987) · National Conference on Artificial Intelligence · Cited by 59
KRYPTON: integrating terminology and assertion
Brachman, R. J., Fikes, R. E., & Levesque, H. J. (1983) · Cited by 59
Anatomy of a Mudular Multiple Query Optimizer
Rosenthal, A. S., & Chakravarthy, U. S. (1988) · Very Large Data Bases · Cited by 59
Clausal intuitionistic logic II. tableau proof procedures
McCarty, L. T. (1988) · The Journal of Logic Programming · doi:10.1016/0743-1066(88)90014-3 · Cited by 59
Dawson, M. R. W., & Schopflocher, D. (1992) · Connection Science · doi:10.1080/09540099208946601 · Cited by 59
Pilot testing a digital pet avatar for older adults
Chi, N.-C., Sparks, O., Lin, S.-Y., Lazar, A., Thompson, H. J., & Demiris, G. (2017) · Geriatric Nursing · doi:10.1016/j.gerinurse.2017.04.002 · Cited by 56
Iandolo, F., Loia, F., Fulco, I., Nespoli, C., & Caputo, F. (2020) · Journal of the Knowledge Economy · doi:10.1007/s13132-020-00703-8 · Cited by 61
“Big data analytics follow an approach based on artificial intelligence (AI), which can be defined as the way of training computers to mimic thinking patterns (Nilsson 2014).”
BackgroundAljazzar, H., & Leue, S. (2009) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2009.57 · Cited by 59
“Our method exploits the power of directed search strategies which have been developed to solve, among others, graph search and optimization problems [8], [9].”
A complete navigation system for goal acquisition in unknown environments
Stentz, A., & Hebert, M. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1995.525831 · Cited by 58
“We define a traverse to be optimal if, at every point P along the traverse to the goal, the robot is following a lowest-cost path from P to the goal, given all obstacle information known in aggregate at point P. It is possible to produce an optimal traverse by using A* [ 22 ] to compute an optimal path from the known…”
MethodGeneralized XML security views
Kuper, G. M., Massacci, F., & Rassadko, N. (2005) · ACM Symposium on Access Control Models and Technologies (SACMAT) · doi:10.1145/1063979.1063994 · Cited by 58
An analysis of consecutively bounded depth-first search with applications in automated deduction
Stickel, M. E., & Tyson, W. M. (1985) · International Joint Conference on Artificial Intelligence · Cited by 58
The artificial intelligence approach to pattern recognition—a perspective and an overview
Nandhakumar, N., & Aggarwal, J. K. (1985) · Pattern Recognition · doi:10.1016/0031-3203(85)90009-3 · Cited by 59
Automated conformational analysis: Directed conformational search using the A* algorithm
Leach, A. R., & Prout, K. (1990) · Journal of Computational Chemistry · doi:10.1002/jcc.540111012 · Cited by 58
An Information Roadmap Method for Robotic Sensor Path Planning
Zhang, G., Ferrari, S., & Qian, M. (2009) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-009-9318-x · Cited by 57
“If the heuristic function is chosen such that h(ci) is always less than the actual cost of the (same) path from ci to cf , then the A∗ algorithm is guaranteed to return a path of minimum cost whenever such a path exists in G [44, 45].”
BackgroundMethodInstance-based natural language generation
Varges, S., & Mellish, C. (2001) · doi:10.3115/1073336.1073337 · Cited by 57
Kambhampati, S., & Kedar, S. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90011-6 · Cited by 63
“The advantages of popi plans in plan generation -- such as improved efficiency through least commitment and flexible plan representation -- are well known (cf. [24, 1, 21]).”
BackgroundMultiple-agent planning systems
Konolige, K., & Nilsson, N. J. (1980) · National Conference on Artificial Intelligence · Cited by 59
Self-citationRule based modeling for planning axisymmetrical deep-drawing
Eshel, G., Barash, M. M., & Johnson, W. (1986) · Journal of Mechanical Working Technology · doi:10.1016/0378-3804(86)90059-8 · Cited by 57
Strategies for learning search control rules: an explanation-based approach
Minton, S., & Carbonell, J. (1987) · International Joint Conference on Artificial Intelligence · Cited by 57
Shaw, M. J. (1988) · International Journal of Production Research · doi:10.1080/00207548808947905 · Cited by 57
Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions
Tan, A. H., Bejarano, F. P., Zhu, Y., Ren, R., & Nejat, G. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3224667 · Cited by 58
“This is achieved with a Path Planner submodule which utilizes the A* algorithm [40] to find a collision-free path between the robot’s current location 𝑥 𝑡 , and the chosen goal location, ℊ 𝑡 .”
MethodSeasonal total methane depletion in limestone caves
Waring, C. L., Hankin, S. I., GRIFFITH, D. W. T., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-07769-6 · Cited by 56
Dynamic task scheduling using online optimization
Lilja, D. J., Kit, L. Y., & Hamidzadeh, B. (2000) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/71.888636 · Cited by 56
“SADS is an online optimization algorithm that, similar to the branch-and-bound algorithm [58], searches through a space of all possible partial and complete schedules.”
MethodSuperlinear speedup in parallel state-space search
Rao, V. N., & Kumar, V. (1988) · Lecture notes in computer science · doi:10.1007/3-540-50517-2_79 · Cited by 56
“In B~B/A*, if the heuristic function is consistent[21], then the cost oi the nodes expanded in successive iterations never goes down (it either goes up or stays the same).”
BackgroundMethodInfluentialTop-down search for coordinating the hierarchical plans of multiple agents
Clement, B. J., & Durfee, E. H. (1999) · Adaptive Agents and Multi-Agents Systems · doi:10.1145/301136.301205 · Cited by 56
“(These are also the conditions in the first column of a STRIPS triangle table [ 13 ].) • A summary [pre, post]condition, c, is must if any of the subplans' corresponding summary [pre, post]conditions is must, and n o o ther subplan has a summary condition that may [achieve, clobber] c; otherwise, it is may.”
BackgroundScalable Parallel Formulations of Depth-First Search
Kumar, V., & Rao, V. N. (1990) · Springer eBooks · doi:10.1007/978-1-4612-3390-9_1 · Cited by 56
Liang, T., Robert, L., Sarker, S., Cheung, C. M. K., Matt, C., Trenz, M., & Turel, O. (2021) · Internet Research · doi:10.1108/intr-11-2020-0668 · Cited by 54
Artificial Intelligence Techniques in Solar Energy Applications
Kalogirou, S. A., & Senc, A. (2010) · Sciyo eBooks · doi:10.5772/10343 · Cited by 55
“Most of the work on building these kinds of systems has taken place in the field called Artificial Intelligence (AI) (Nilsson, 1980).”
BackgroundOn the use of prosody in automatic dialogue understanding
Nöth, E., Batliner, A. M., Warnke, V., et al. (2002) · Speech Communication · doi:10.1016/s0167-6393(01)00025-5 · Cited by 55
“Here, we will concentrate on the search procedure [31] and introduce it in an informal manner.”
MethodOn the Variable Ordering in Subgraph Isomorphism Algorithms
Bonnici, V., & Giugno, R. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2515595 · Cited by 58
“The set of all possible mappings is the search space of the problem, which is compactly represented as a tree, named the Search Space Tree (SST), [35].”
BackgroundA Capability Maturity Model for Knowledge-Based Decisionmaking
Kaner, M., & Karni, R. (2004) · Information Knowledge Systems Management · doi:10.3233/iks-2004-00076 · Cited by 55
“The source knowledge is not usually retained [61].”
BackgroundZhang, B., & Zhang, L. (1987) · Journal of Computer Science and Technology · doi:10.1007/bf02943312 · Cited by 55
A prolog technology theorem prover: Implementation by an extended prolog compiler
Stickel, M. E. (1986) · Lecture notes in computer science · doi:10.1007/3-540-16780-3_122 · Cited by 55
Random Search in the Presence of Noise, with Application to Machine Learning
Yakowitz, S., & Lugosi, E. (1990) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0911041 · Cited by 55
Obstacle growing in a nonpolygonal world
Laumond, J. (1987) · Information Processing Letters · doi:10.1016/0020-0190(87)90091-3 · Cited by 55
Control and explanation in a signal understanding environment
Kümmert, F., Niemann, H. J., Prechtel, R., et al. (1993) · Signal Processing · doi:10.1016/0165-1684(93)90039-d · Cited by 55
Inexact subgraph isomorphism in MapReduce
Plantenga, T. D. (2012) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2012.10.005 · Cited by 53
Riverbed: A Novel User-Steered Image Segmentation Method Based on Optimum Boundary Tracking
Miranda, P. A. V., Falcão, A. X., & Spina, T. V. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2188034 · Cited by 54
“Martelli proposed a solution using the heuristic algorithm [19].”
MethodA Study of Two Sampling Methods for Analyzing Large Datasets with ILP
Srinivasan, A. (1999) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009824123462 · Cited by 52
“We assume that this is accomplished by implementing the key steps in the GRAPHSEARCH procedure described in ( Nilsson, 1980 ) (Fig. 2). If, given an example e, the search procedure returns e, then we shall say that e is “incompressible” with the input provided to search.”
MethodOn constraints in assembly planning
Jones, R. E., Wilson, R. H., & Calton, T. L. (1998) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.736770 · Cited by 54
“Attaching special-purpose methods to constraint tests is also a well known efficiency technique [ 23 ], although the methods we use to accomplish this are novel (see Section VI).”
MethodMathematical foundations of navigation and perception for an autonomous mobile robot
Crowley, J. L. (1996) · Lecture notes in computer science · doi:10.1007/bfb0013953 · Cited by 54
A Bayesian approach to reconstruction from incomplete projections of a multiple object 3D domain
Bresler, Y., Fessler, J. A., & Macovski, A. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.31446 · Cited by 54
Minimum classification error training for online handwriting recognition
Biem, A. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.146 · Cited by 53
“To track the best path within the LFSM (or equivalently within the grid), we used a modified version of the A* search algorithm [48] that is guaranteed to find the exact N-best paths through the graph.”
MethodOn the long-term use of visual gui testing in industrial practice: a case study
Alégroth, E., & FELDT, R. H. (2017) · Empirical Software Engineering · doi:10.1007/s10664-016-9497-6 · Cited by 53
Routing Large Vehicles on Industrial Construction Sites
Varghese, K., & O'Connor, J. T. (1995) · Journal of Construction Engineering and Management · doi:10.1061/(asce)0733-9364(1995)121:1(1 · Cited by 53
Hawas, Y. E., & Mahmassani, H. S. (1996) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1537-12 · Cited by 53
WHIRL: A word-based information representation language
Cohen, W. W. (2000) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00102-2 · Cited by 53
Development of an expert system for physical property predictions
Bañares‐Alcántara, R., Westerberg, A. W., & Rychener, M. D. (1985) · Computers & Chemical Engineering · doi:10.1016/0098-1354(85)85003-1 · Cited by 53
Parallel rule firing in production systems
Ishida, T. (1991) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.75883 · Cited by 53
Prozedurale Aspekte der Wegplanung und Wegbeschreibung
Habel, C. U. (1988) · VS Verlag für Sozialwissenschaften eBooks · doi:10.1007/978-3-322-86251-8_5 · Cited by 52
Dynamic and consequential consistency of choices between paths of decision trees.
Busemeyer, J. R., Weg, E., Barkan, R., et al. (2000) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.129.4.530 · Cited by 52
“Dynamic consistency also provides the foundation for working-backward search procedures that underlay planning heuristics used in problem solving (see Nilsson, 1980).”
BackgroundAn algebra of product families
Höfner, P., Khédri, R., & Möller, B. (2009) · Software & Systems Modeling · doi:10.1007/s10270-009-0127-2 · Cited by 52
“As we have seen, the representation of the elements in sum-of-products form corresponds to OR/AND trees of features [27].”
BackgroundKaié, L. V. (1990) · Springer eBooks · doi:10.1007/978-1-4612-3390-9_5 · Cited by 52
A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles
Malola, S., Nieminen, P., Pihlajamäki, A., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12031-w · Cited by 52
Exact and heuristic methodologies for scheduling in hospitals: problems, formulations and algorithms
Beliën, J. (2006) · 4OR · doi:10.1007/s10288-006-0006-4 · Cited by 52
Solving Rostering Tasks as Constraint Optimization
Meyer, H. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44629-x_12 · Cited by 52
“can then be used by the A*-heuristic [15] to guide depth-first search.”
BackgroundMethodInfluentialA SYSTEM AND CONTROL THEORETIC PERSPECTIVE ON ARTIFICIAL INTELLIGENCE PLANNING SYSTEMS
Passino, K. M., & Antsaklis, P. J. (1989) · Applied Artificial Intelligence · doi:10.1080/08839518908949915 · Cited by 51
Symbolic and Geometric Connectivity Graph Methods for Route Planning in Digitized Maps
Holmes, P. D., & Jungert, E. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.134059 · Cited by 58
A class of algorithms for distributed constraint optimization
Petcu, A. (2007) · doi:10.5075/epfl-thesis-3942 · Cited by 52
Finding optimal satisficing strategies for and-or trees
Greiner, R., Hayward, R., Jankowska, M., et al. (2005) · Artificial Intelligence · doi:10.1016/j.artint.2005.09.002 · Cited by 51
“3 algorithms such as A [ Nil80 , Pea84]. Such algorithms use only ilocali structural information, namely nodes that are adjacent to the current node, and seek the heuristic cost function that is uniformly best, namely that expand the fewest number of nodes in every situation.”
MethodBidirectional Search That Is Guaranteed to Meet in the Middle
Holte, R. C., Felner, A., Sharon, G., et al. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10436 · Cited by 51
Ma, Z., Leung, J. Y., & Zanon, S. D. (2017) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2017.12.046 · Cited by 51
A Joint Introduction to Natural Language Processing and to Deep Learning
Deng, L., & Liu, Y. (2018) · Springer eBooks · doi:10.1007/978-981-10-5209-5_1 · Cited by 57
Höfner, P., Khédri, R., & Möller, B. (2006) · Lecture notes in computer science · doi:10.1007/11813040_21 · Cited by 51
Epstein, S. L. (1994) · Machine Learning · doi:10.1023/a:1022637925775 · Cited by 54
“This was explored with a series of informed trainers that apply an input, game-dependent evaluation function in an alphabeta search to a fixed depth (Nilsson, 1980).”
BackgroundInfluentialSecurity and Privacy in User Modeling
Schreck, J. (2003) · Human-computer interaction series · doi:10.1007/978-94-017-0377-2 · Cited by 51
Metadata
- Authors
- Nilsson, N. J.
- Affiliations
- SRI International (US)
- Corresponding author
- Nilsson, Nils J.
- Venue
- Springer eBooks
- Publisher
- Springer Berlin Heidelberg
- Published
- 1982
- 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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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.
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