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Principles of Artificial Intelligence
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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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Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
“Learning hierarchical representations through deep SL, UL, RL Many methods of Good Old-Fashioned Artificial Intelligence (GOFAI) (Nilsson, 1980) as well as more recent approaches to AI (Russell, Norvig, Canny, Malik, & Edwards, 1995) and Machine Learning (Mitchell, 1997) learn hierarchies of more and more abstract dat…”
MethodInfluentialSimple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
“This latter strategy works in situations where the goodness of alternative actions is determined by estimates which are always overly optimistic and which become more realistic with continued experience, as occurs for example in A* search (Nilsson, 1980).”
BackgroundFuture paths for integer programming and links to artificial intelligence
Glover, F. (1986) · Computers & Operations Research · doi:10.1016/0305-0548(86)90048-1 · Cited by 4,370
Newell, A. (1982) · Artificial Intelligence · doi:10.1016/0004-3702(82)90012-1 · Cited by 2,589
“They do not even have to be limited to madiematics and logic, given the work in AI to use predicate logic (eg, resolution) as the symbol level structures for robot tasks of spatial manipulation and movement, as well as for many other sorts of tasks (Nilsson, 1980).”
BackgroundInfluentialSOAR: An Architecture for General Intelligence
Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90050-6 · Cited by 2,535
“In AI, problem spaces are commonly used for genuine problem solving [18, 51, 57, 58, 59, 72], but procedural representations are commonly used for routine behavior.”
BackgroundMaier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,432
“Doering et al. (1997) have proposed using the A*-Algorithm (Nilsson, 1980) for the same task.”
MethodMotion Planning in Dynamic Environments Using Velocity Obstacles
Fiorini, P., & Shiller, Z. (1998) · The International Journal of Robotics Research · doi:10.1177/027836499801700706 · Cited by 2,008
“By assigning an appropriate cost to each branch, this tree can be searched for the trajectory that maximizes some objective function, such as distance traveled, motion time, or energy, using standard search techniques (Nilsson, 1980; Pearl, 1985).”
MethodAn overview of the KL-ONE Knowledge Representation System
Brachman, R. J., & Schmolze, J. G. (1985) · Cognitive Science · doi:10.1016/s0364-0213(85)80014-8 · Cited by 1,624
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
A review of process fault detection and diagnosis
Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(02)00161-8 · Cited by 1,536
“Evolving the graph G(P) is essentially making explicit a portion of G(P) by a search procedure (Nilsson, 1980).”
MethodDechter, R. (2003) · Cited by 1,554
A (sub)graph isomorphism algorithm for matching large graphs
Cordella, L. P., Foggia, P., Sansone, C., & Vento, M. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.75 · Cited by 1,486
“The process of finding the mapping function can be suitably described by means of a State Space Representation (SSR) [18].”
MethodInfluentialNewell, A. (1980) · Cognitive Science · doi:10.1207/s15516709cog0402_2 · Cited by 1,458
Muggleton, S. (1995) · New Generation Computing · doi:10.1007/bf03037227 · Cited by 1,411
Harel, D. (1988) · Communications of the ACM · doi:10.1145/42411.42414 · Cited by 1,284
Optimal and Efficient Path Planning for Partially Known Environments
Stentz, A. (1997) · Springer eBooks · doi:10.1007/978-1-4615-6325-9_11 · Cited by 1,244
Explanation-Based Generalization: A Unifying View
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986) · Machine Learning · doi:10.1023/a:1022691120807 · Cited by 1,382
“We employ a slightly modified version of the goal-regression algorithm described by Waldinger (1977) and Nilsson (1980).”
MethodArtificial Intelligence: A New Synthesis
(1998) · Elsevier eBooks · doi:10.1016/c2009-0-27773-7 · Cited by 1,211
Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine
Miller, R. A., Pople, H. E., & Myers, J. D. (1982) · New England Journal of Medicine · doi:10.1056/nejm198208193070803 · Cited by 1,129
Connectionist Speech Recognition: A Hybrid Approach
Bourlard, H. A., & Morgan, N. H. (1993) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,115
Generalized best-first search strategies and the optimality of A*
Dechter, R., & Pearl, J. (1985) · Journal of the ACM · doi:10.1145/3828.3830 · Cited by 1,069
Study on artificial intelligence: The state of the art and future prospects
Zhang, C., & Lu, Y. (2021) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2021.100224 · Cited by 1,369
Learning metric-topological maps for indoor mobile robot navigation
Thrun, S. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00078-7 · Cited by 1,064
Robot Motion Planning: A Distributed Representation Approach
Barraquand, J., & Latombe, J.-C. (1991) · The International Journal of Robotics Research · doi:10.1177/027836499101000604 · Cited by 995
Readings in Distributed Artificial Intelligence
Bond, A. H., & Gasser, L. (1988) · Elsevier eBooks · doi:10.1016/c2013-0-07700-6 · Cited by 1,007
Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges
Aggarwal, S., & Kumar, N. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.10.014 · Cited by 1,025
A taxonomy of scheduling in general-purpose distributed computing systems
Casavant, T. L., & Kuhl, J. G. (1988) · IEEE Transactions on Software Engineering · doi:10.1109/32.4634 · Cited by 989
Bonet, B., & Geffner, H. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00108-4 · Cited by 975
“This approach, however, has not been popular in planning where approaches based on divideand-conquer ideas and search in the space of plans have been more common [35,46].”
BackgroundMethodInfluentialAutomatic Synthesis of Fine-Motion Strategies for Robots
Lozano‐Pérez, T., Mason, M. T., & Taylor, R. H. (1984) · The International Journal of Robotics Research · doi:10.1177/027836498400300101 · Cited by 901
Handbook of Knowledge Representation
Harmelen, F. V., Lifschitz, V., & Porter, B. (2008) · Foundations of artificial intelligence · doi:10.1016/s1574-6526(07)x0300-6 · Cited by 862
Explanation-Based Learning: An Alternative View
DEJONG, G. F., & Mooney, R. J. (1986) · Machine Learning · doi:10.1023/a:1022898111663 · Cited by 839
“The generalization process used in EBG is a specialization of goal-regression (Waldinger, 1977; Nilsson, 1980).”
BackgroundMethodExperiences with an interactive museum tour-guide robot
Burgard, W., Cremers, A. B., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., Steiner, W., & Thrun, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00070-3 · Cited by 841
“Almost all of the work surveyed here is focused on domains where the model is specified by a collection of logical axioms, often expressed as STRIPS-like operators [110].”
BackgroundMethodSellis, T. (1988) · ACM Transactions on Database Systems · doi:10.1145/42201.42203 · Cited by 814
“We will model the optimization problem as a state space search problem and propose the use of an A* algorithm [ 21 ].”
InfluentialThe focussed D* algorithm for real-time replanning
Stentz, A. (1995) · Cited by 809
Intelligent scissors for image composition
Mortensen, E. N., & Barrett, W. A. (1995) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/218380.218442 · Cited by 789
“This paper utilizes an optimal graph search similar to that presented by Dijkstra [6] and extended by Nilsson [13]; further, this technique builds on and extends previous boundary tracking methods in 4 important ways:”
MethodCollaborative multi-robot exploration
Burgard, W., Moors, M., Fox, D., Simmons, R. G., & Thrun, S. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2000.844100 · Cited by 781
“Value iteration updates the value of all grid cells by the value of their best neighbors, plus the cost of moving to this neighbor (just like A* [ 12 ]).”
BackgroundNetwork Optimization: Continuous and Discrete Models
Bertsekas, D. P. (1998) · Cited by 738
The Spatial Semantic Hierarchy
Kuipers, B. J. (2000) · Artificial Intelligence · doi:10.1016/s0004-3702(00)00017-5 · Cited by 727
“Where distance estimates exist, one can use A ∗ search [75] or Dijkstra’s algorithm [16].”
MethodKohavi, R. (1995) · Lecture notes in computer science · doi:10.1007/3-540-59286-5_57 · Cited by 756
“In order to search the space of feature subsets e(cid:11)ectively, we transform the problem into a state space search and use best-(cid:12)rst search to heuristically search the space (Ginsberg 1993, Nilsson 1980).”
MethodSilver, D. (2005) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v1i1.18726 · Cited by 740
“A well-known property of A* with a consistent heuristic is that the optimal distance from the start to a node is known once that node is expanded (Nilsson 1980).”
BackgroundAND/OR Graph Representation of Assembly Plans
Honicm, L. S., Arthur, M., & Sanderson, C. (1986) · Cited by 687
Frontiers in Artificial Intelligence and Applications
Yadav, J., Shukla, S., Sharma, K., Soni, N., Agarwal, S., & Pathak, P. C. (2022) · International Conference on Computation, Automation and Knowledge Management (ICCAKM) · doi:10.1109/iccakm54721.2022.9990098 · Cited by 674
“…of an algorithm that optimizes its overall performance by gathering and analyzing data, which is based on the different learning models [12] and can be divided into four categories that are discussed in Table II [13] and a brief concept of ML components is illustrated below: 1) A…”
Query Optimization in Database Systems
Jarke, M., & Koch, J. (1984) · ACM Computing Surveys · doi:10.1145/356924.356928 · Cited by 650
Actions and Events in Interval Temporal Logic
Allen, J. F., & Ferguson, G. (1997) · Springer eBooks · doi:10.1007/978-0-585-28322-7_7 · Cited by 620
Koza, J. R. (1990) · Cited by 608
Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020) · Annals of Operations Research · doi:10.1007/s10479-020-03620-w · Cited by 689
An Introduction to Least Commitment Planning
Weld, D. S. (1994) · doi:10.1609/aimag.v15i4.1109 · Cited by 588
“Another approach, called regression planning (Waldinger 1977), is outlined in algorithm 2. Instead of searching forward from the initial state (which is what PROGWS does), the REGWS algorithm (adapted from Nilsson [1980] ) searches backwards from the goal.”
MethodEngineering Self-Adaptive Systems through Feedback Loops
Brun, Y., Serugendo, G. D. M., Gacek, C., Giese, H., Kienle, H. M., Litoiu, M., Müller, H. A., Pezzè, M., & Shaw, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02161-9_3 · Cited by 579
“This model is a refinement of the AI community’s sense-plan-act approach of the early 1980s to control autonomous mobile robots [22,23].”
MethodArtificial intelligence in supply chain management: theory and applications
Min, H. (2009) · International Journal of Logistics Research and Applications · doi:10.1080/13675560902736537 · Cited by 699
“In general, AI is referred to as the use of computers for reasoning, recognising patterns, learning or understanding certain behaviors from experience, acquiring and retaining knowledge, and developing various forms of inference to solve problems in decision-making situations where optimal or exact solutions are eithe…”
BackgroundLangton, C. G. (1995) · Cited by 571
Interactive Segmentation with Intelligent Scissors
Mortensen, E. N., & Barrett, W. A. (1998) · Graphical Models and Image Processing · doi:10.1006/gmip.1998.0480 · Cited by 547
“formulated as a shortest or minimum cost path graph search. He applies Nilsson’s [ 27 ] A* heuris-”
MethodHelo, P., & Hao, Y. (2021) · Production Planning & Control · doi:10.1080/09537287.2021.1882690 · Cited by 672
“AI is concerned with how to process data, how to give computers the sophistication to perceive the surrounding environment, and also how to act intelligently with algorithms and synthesis (Russell and Norvig 2016; Nilsson 1980).”
BackgroundReaching Agreements Through Argumentation: A Logical Model and Implementation
Kraus, S., Sycara, K., & Evenchik, A. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00078-2 · Cited by 616
Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles
Likhachev, M. A., & Ferguson, D. (2009) · The International Journal of Robotics Research · doi:10.1177/0278364909340445 · Cited by 516
“A* search is perhaps one of the most popular methods for doing this ( Nilsson 1980 ).”
MethodOnline Planning Algorithms for POMDPs
Ross, S., Pineau, J., Paquet, S., & Chaib-draa, B. (2008) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2567 · Cited by 503
“Washington (1997) proposed a slightly different approach inspired by the AO∗ algorithm (Nilsson, 1980), where the search is only conducted in the best solution graph.”
MethodChunking in Soar: The Anatomy of a General Learning Mechanism
Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986) · Machine Learning · doi:10.1023/a:1022639103969 · Cited by 509
“This formalism has been widely used in Artificial Intelligence (Feigenbaum & Feldman, 1963; Nilsson, 1980; Rich, 1983) and it has already worked well in Soar across a wide variety of problem domains (Laird, Newell, & Rosenbloom, 1985).”
MethodInexact graph matching for structural pattern recognition
Bunke, H., & Allermann, G. (1983) · Pattern Recognition Letters · doi:10.1016/0167-8655(83)90033-8 · Cited by 487
Robotic Process Automation and Artificial Intelligence in Industry 4.0 – A Literature review
Ribeiro, J., Lima, R., Eckhardt, T., & Paiva, S. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.01.104 · Cited by 489
A general framework for adaptive processing of data structures
Frasconi, P., Gori, M., & Sperduti, A. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.712151 · Cited by 481
Scheduling Aircraft Landings—The Static Case
Beasley, J. E., Krishnamoorthy, M., Sharaiha, Y. M., et al. (2000) · Transportation Science · doi:10.1287/trsc.34.2.180.12302 · Cited by 450
“These systems include, in part, modules to schedule landings and takeoffs: OLCKERS 1986, 1987, 1990; P LATZ and B ROKOF , 1994), which takes an initial heuristic schedule and applies an enumeration procedure designed to eliminate separation-time conflicts ● MAESTRO (G ARCIA , 1990), where the solution procedure is unc…”
MethodTypes of machine learning algorithms
Oladipupo, T. (2010) · New Advances in Machine Learning · doi:10.5772/9385 · Cited by 443
A generalized S-D assignment algorithm for multisensor-multitarget state estimation
Deb, S., Yeddanapudi, M., Pattipati, K. R., et al. (1997) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/7.575891 · Cited by 416
“Comparison of Row-Column and Relaxation Algorithms row-column algorithm [ 25 , 29].”
MethodDAMN: a distributed architecture for mobile navigation
Rosenblatt, J. K. (1997) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/095281397147167 · Cited by 407
“These planners use dynamic programming techniques based on the A* search algorithm (Nilsson, 1980) to determine an optimal global path.”
MethodLearning Motion Patterns of People for Compliant Robot Motion
Bennewitz, M., Burgard, W., Cielniak, G., & Thrun, S. (2005) · The International Journal of Robotics Research · doi:10.1177/0278364904048962 · Cited by 399
“In our current system we apply the A∗ algorithm [46] to determine the minimum-cost path in the three dimensional configuration time-space of the robot.”
MethodLearning by Analogy: Formulating and Generalizing Plans from Past Experience
Carbonell, J. (1983) · Springer eBooks · doi:10.1007/978-3-662-12405-5_5 · Cited by 405
Précis of The Intentional Stance
Dennett, D. C. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058611 · Cited by 413
Scheduling flexible manufacturing systems using Petri nets and heuristic search
Lee, D. Y., & DiCesare, F. (1994) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.282537 · Cited by 398
Using interpolation to improve path planning: The Field D * algorithm
Ferguson, D., & Stentz, A. (2006) · Journal of Field Robotics · doi:10.1002/rob.20109 · Cited by 395
“for replanning Hart et al., 1968; Nilsson, 1980; Stentz, 1995; Koenig & Likhachev, 2002b.”
BackgroundDean, T., & McDermott, D. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90061-0 · Cited by 430
Algorithmics and applications of tree and graph searching
Shasha, D. E., Wang, J. T. L., & Giugno, R. (2002) · ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/543613.543620 · Cited by 389
LAO∗: A heuristic search algorithm that finds solutions with loops
Hansen, E. A., & Zilberstein, S. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00106-0 · Cited by 389
“Nilsson [18] also used the name AO* for this more general algorithm.”
BackgroundMethodInfluentialA comprehensive survey of recent trends in deep learning for digital images augmentation
Khalifa, N. E. M., Loey, M., & Mirjalili, S. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10066-4 · Cited by 429
“Deep learning (DL) is a sub-category of machine learning (Baştanlar and Özuysal 2014) which consequently a subset of artificial intelligence (Nilsson 1981).”
BackgroundCohen, W. W. (1998) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/276304.276323 · Cited by 379
Approaches to Multisensor Data Fusion in Target Tracking: A Survey
Smith, D., & Singh, S. G. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.183 · Cited by 379
“The evidence in [37] shows that, in all likelihood, an NP-Hard algorithm cannot be solved in a computationally efficient manner, but many approximation algorithms exist to find near optimal solutions.”
BackgroundImplementation of logical query languages for databases
Ullman, J. D. (1985) · ACM Transactions on Database Systems · doi:10.1145/3979.3980 · Cited by 374
“not variables, and A(x, x) cannot unify with A(l, 2). See [ 12 ], for example, for a discussion of”
BackgroundLearning from performance errors.
Ohlsson, S. (1996) · Psychological Review · doi:10.1037/0033-295x.103.2.241 · Cited by 392
“The details of the goal regression algorithm are not relevant here (see Nilsson, 1980, p, 288, for a technical description).”
MethodTeleo-Reactive Programs for Agent Control
Nilsson, N. J. (1994) · Journal of Artificial Intelligence Research · doi:10.1613/jair.30 · Cited by 367
“The conditions are therefore regressions (Nilsson, 1980) of higher conditions through the actions that achieve those higher conditions.”
BackgroundSelf-citationAbstract Planning and Perceptual Chunks: Elements of Expertise in Geometry
Koedinger, K. R., & Anderson, J. R. (1990) · Cognitive Science · doi:10.1207/s15516709cog1404_2 · Cited by 375
“In the field of problem solving this is known as the formation of macro-operators (Korf, 1987; Nilsson, 1980).”
BackgroundLigand docking to proteins with discrete side-chain flexibility
Leach, A. R. (1994) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80038-5 · Cited by 363
Expertise in debugging computer programs: A process analysis
Vessey, I. (1985) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(85)80054-7 · Cited by 359
Online recognition of chinese characters: the state-of-the-art
Liu, C., Jaeger, S., & Nakagawa, M. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1262182 · Cited by 356
A new algorithm for error-tolerant subgraph isomorphism detection
Messmer, B., & Bunke, H. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.682179 · Cited by 348
“The most common approach is based on tree search with the A algorithm [15].”
MethodSubstantive and procedural uncertainty
Giovanni Dosi, A., & Egidi, M. (1991) · Journal of Evolutionary Economics · doi:10.1007/bf01224917 · Cited by 353
Michie, S., Thomas, J., Johnston, M., Aonghusa, P. M., Shawe-Taylor, J. S., Kelly, M. P., Deleris, L. A., Finnerty, A. N., Marques, M. M., Norris, E., et al. (2017) · Implementation Science · doi:10.1186/s13012-017-0641-5 · Cited by 326
“[31]”
3D Complex: A Structural Classification of Protein Complexes
Levy, E. D., Pereira‐Leal, J. B., Chothia, C., & Teichmann, S. A. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020155 · Cited by 337
“The graph alignment algorithm developed here is a modified implementation of the A* algorithm [48].”
MethodAn Improved Algorithm for Matching Large Graphs
Cordella, L. P., Foggia, P., Sansone, C., & Vento, M. (2001) · Cited by 358
Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics
Mitchell, T. M., Utgoff, P. E., & Banerji, R. B. (1983) · Machine Learning · doi:10.1007/978-3-662-12405-5_6 · Cited by 338
UMCP: a sound and complete procedure for hierarchical task-network planning
Erol, K., Hendler, J., & Nau, D. S. (1994) · Cited by 335
Substructure similarity search in graph databases
Yan, X., Yu, P. S., & Han, J. (2005) · Proceedings - ACM-SIGMOD International Conference on Management of Data · doi:10.1145/1066157.1066244 · Cited by 319
“Nilsson[15] presented an algorithm for the pairwise approximate sub-”
BackgroundMethodIntegrating grid-based and topological maps for mobile robot navigation
Thrun, S., & Bü, A. (1996) · Cited by 314
Admissible heuristics for optimal planning
Haslum, P., & Geffner, H. (2000) · doi:10.1609/aimag.v21i4.1536 · Cited by 310
Programming by multiset transformation
Banâtre, J.-P., & Métayer, D. L. (1993) · Communications of the ACM · doi:10.1145/151233.151242 · Cited by 310
Information retrieval by constrained spreading activation in semantic networks
Cohen, P. R., & Kjeldsen, R. (1987) · Information Processing & Management · doi:10.1016/0306-4573(87)90017-3 · Cited by 308
Planning with incomplete information as heuristic search in belief space
Bonet, B., & Geffner, H. (2000) · Cited by 297
Barraquand, J., & Latombe, J. (1993) · Algorithmica · doi:10.1007/bf01891837 · Cited by 294
“Perhaps a good variant would be to replace the Dijkstra algorithm by a more general A* algorithm [ 40 ] with a cost function blending the number Nonholonomic Multibody Mobile Robots 151”
MethodLabeled RTDP: Improving the Convergence of Real-Time Dynamic Programming
Bonet, B., & Geffner, H. (2003) · Cited by 298
The "Map in the Head" Metaphor
Kuipers, B. J. (1982) · Environment and Behavior · doi:10.1177/0013916584142005 · Cited by 314
Speech recognition in noisy environments
Moreno, P. J. (1996) · Cited by 289
Cognitive Radio Architecture Evolution
Mitola, J. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2013012 · Cited by 289
“Communities of cognitive radios might analyze their own maintenance records to discover operations, administration, and maintenance issues with less human oversight [97], enhancing the costeffectiveness of cognitive devices and infrastructure compared to conventionally maintained wireless products and networks and com…”
BackgroundMethodArtificial intelligence and human decision making
Pomerol, J. (1997) · European Journal of Operational Research · doi:10.1016/s0377-2217(96)00378-5 · Cited by 291
“We consider the two situations displayed in Figure 6 in the (small) world of the blocks (see Nilsson, 1980).”
Background
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.
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 ›
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