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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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Towards optimal strategies for moving droplets in digital microfluidic systems
Böhringer, K. F. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2004.1308031 · Cited by 41
“Transitions between states define edges in this graph, and finding an optimal control strategy to transform start state As into goal state Ag becomes a standard graph search problem, which can be solved, for example, using an A* algorithm known from artificial intelligence programming [19]: A* graph search employs a m…”
An intelligent system for automatic fire detection in forests
Cappellini, V., Mattii, L., & Mecocci, A. (1989) · Lecture notes in computer science · doi:10.1007/3-540-51815-0_67 · Cited by 41
Automated structure design in 3D
Gillet, V. J., Johnson, A. P., Mata, P., et al. (1990) · Tetrahedron Computer Methodology · doi:10.1016/0898-5529(90)90167-7 · Cited by 41
Knowledge-based vision and simple visual machines
Cliff, D., & Jason, N. (1997) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.1997.0100 · Cited by 41
“Nilsson 1982; Charniak & McDermott 1985), paying little attention to criticisms of the approach (e.g.”
BackgroundGrand challenges in industrial and systems engineering
Karwowski, W., Salvendy, G., Albert, L. A., Kim, W. C., Denton, B. T., Dessouky, M. M., Dolgui, A. B., Duffy, V. G., Kumara, S. R. T., Li, J., et al. (2025) · International Journal of Production Research · doi:10.1080/00207543.2024.2432463 · Cited by 46
“AI has also progressed from foundational AI (Nilsson 1982; Gene-sereth & Nilsson 2012; Russell and Norvig 2010) to AI applications in medicine (Hamet and Tremblay 2017; Topol 2019) and manufacturing (Kumara, Kashyap, and Soyster 1989; Davies2023).”
ProGolem: A System Based on Relative Minimal Generalisation
Muggleton, S., Santos, J. C. A., & Tamaddoni‐Nezhad, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13840-9_13 · Cited by 44
“It has long been appreciated in AI [20] that generate-and-test procedures are less efficient than ones based on test-incorporation.”
MethodThe PN∗-search algorithm: Application to tsume-shogi
Seo, M., Iida, H., & UITERWIJK, J. W. H. M. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00084-4 · Cited by 40
“The algorithm is based on proof-number (PN) search, a best-first search algorithm, proposed by Allis et al. [Artificial Intelligence 66 (1) (1994) 91–124], and on Korf’s RBFS algorithm [Artificial Intelligence 62 (1) (1993) 41–78].”
MethodShape modeling and matching in identifying 3D protein structures
Abeysinghe, S. S., Ju, T., Baker, M. L., et al. (2008) · Computer-Aided Design · doi:10.1016/j.cad.2008.01.013 · Cited by 40
“Given a fitness function, the A*-search algorithm works by maintaining all unexpanded partial matches in a priority queue and only expanding the partial match with the best (smallest) fitness function value.”
MethodInfluentialDiscovering admissible heuristics by abstracting and optimizing: a transformational approach
Mostow, J., & Prieditis, A. E. (1989) · International Joint Conference on Artificial Intelligence · Cited by 40
The CRITTER System -- Automated Critiquing of Digital Circuit Designs
Kelly, V. E. (1984) · doi:10.5555/800033.800832 · Cited by 40
Online Planning for Large Markov Decision Processes with Hierarchical Decomposition
Bai, A., Wu, F., & Chen, X. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2717316 · Cited by 35
From human resource management to intelligent human resource management: a conceptual perspective
Yabancı, O. (2019) · Human-Intelligent Systems Integration · doi:10.1007/s42454-020-00007-x · Cited by 39
“Humans write computer programs, do mathematics, engage in commonsense reasoning, understand language, and drive cars that all require intelligence (Nilsson 1980).”
BackgroundFunctional Strips: A More Flexible Language for Planning and Problem Solving
Geffner, H. (2000) · Springer eBooks · doi:10.1007/978-1-4615-1567-8_9 · Cited by 42
Solving combinatorial problems: The 15-puzzle
Pizlo, Z., & Li, Z. (2005) · Memory & Cognition · doi:10.3758/bf03193214 · Cited by 41
“But if good approximations are known, the evaluation function may still lead to good solutions (see Nilsson, 1980, pp. 72–88, for examples of this approach in the case of the 8-puzzle).”
BackgroundArchitecture-adaptive routability-driven placement for FPGAs
Sharma, A., Hauck, S., & Ebeling, C. (2005) · International Conference on Field Programmable Logic and Applications (FPL) · doi:10.1109/fpl.2005.1515759 · Cited by 39
“this section we discuss the A* algorithm [ 17 ], a technique”
MethodProposed Methodology for Knowledge Acquisition: A Study on Congenital Heart Disease Diagnosis
Leão, B. F., & Rocha, A. F. (1990) · Methods of Information in Medicine · doi:10.1055/s-0038-1634764 · Cited by 39
A*-admissible heuristics for rapid lexical access
Kenny, P. J., Hollan, R., Gupta, V., et al. (1993) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.221367 · Cited by 39
Intelligent control schemes for automated storage and retrieval systems
Seidmann, A. A. (1988) · International Journal of Production Research · doi:10.1080/00207548808947910 · Cited by 39
Recognition of Handwritten Bangla Basic Characters and Digits using Convex Hull based Feature SetPreprint
Basu, S., Das, N., Sarkar, R., et al. (2009) · arXiv (Cornell University) · doi:10.13140/2.1.3689.4089 · Cited by 39
An algorithm to find all paths between two nodes in a graph
Migliore, M., Martorana, V., & Sciortino, F. (1990) · Journal of Computational Physics · doi:10.1016/0021-9991(90)90235-s · Cited by 38
Memory intensive AND/OR search for combinatorial optimization in graphical models
Marinescu, R., & Dechter, R. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2009.07.004 · Cited by 38
“It specializes the AND/OR space introduced in [19] to graphical models.”
MethodSafe Multirobot Navigation Within Dynamics Constraints
Bruce, J., & Veloso, M. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.876915 · Cited by 38
“This complexity has inspired many approximation methods, such as local minima free grid-based potentials [20], and common application of the A algorithm [17] on cost grid”
BackgroundMethodLaird, J. E., & Newell, A. (2018) · Figshare · doi:10.1184/r1/6602852 · Cited by 37
Exploiting graph properties of game trees
Plaat, A., Schaeffer, J., Pijls, W., et al. (1996) · Cited by 38
Hyperpath Formulations of Traffic Assignment Problems
Marcotte, P., & Nguyen, S. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5757-9_9 · Cited by 38
“Connectivity problems in And-Or graphs (Nilsson, 1980, Martelli and Montanari, 1973, Gnesi et al., 1981) may alsobe interpreted as connectivity problems in `forward hypergraphs'.1.3 CAPACITATED NETWORKSWe now turn our attention to a general model of equilibrium on a multicommoditytransportation…”
BackgroundInfluentialThe case for graph-structured representations
Sanders, K. E., Kettler, B. P., & Hendler, J. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63233-6_496 · Cited by 38
Walker, D. E. (1981) · Journal of the American Society for Information Science · doi:10.1002/asi.4630320516 · Cited by 38
Execution of OPS5 production systems on a massively parallel machine
Hillyer, B. K., & Shaw, D. E. (1986) · Journal of Parallel and Distributed Computing · doi:10.1016/0743-7315(86)90006-7 · Cited by 38
Sequential Composition for Navigating a Nonholonomic Cart in the Presence of Obstacles
Kallem, V., Komoroski, A. T., & Kumar, V. (2011) · IEEE Transactions on Robotics · doi:10.1109/tro.2011.2161159 · Cited by 37
“Following similar computations used in the previous proposition, one can show that that within (D\∂D) × (−π, π), g3 (q) = 0 only when θ = 0.”
MethodLearning Traversability From Point Clouds in Challenging Scenarios
Bellone, M., Reina, G., Caltagirone, L., & Wahde, M. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2769218 · Cited by 37
“2769218 intelligence (AI) [2].”
Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems
Ilager, S., Muralidhar, R., & Buyya, R. (2020) · IEEE Cloud Summit · doi:10.1109/ieeecloudsummit48914.2020.00007 · Cited by 43
“n computing capabilities has witnessed the resurgence of Artificial intelligence (AI) techniques driving innovation across different domains such as healthcare, autonomous driving, and robotics [10], [11]. Training Machine Learning (ML) models itself need huge resources and is increasing exponentially and doubling eve…”
BackgroundInfluentialDirected hypergraphs as a modelling paradigm
Gallo, G., & Scutellà, M. G. (1998) · Decisions in Economics and Finance · doi:10.1007/bf02735318 · Cited by 37
Berger, J., Boukhtouta, A., Benmoussa, A., et al. (2012) · Computers & Operations Research · doi:10.1016/j.cor.2012.05.002 · Cited by 37
“An online search algorithm extended from A [22] was applied to a single UAV in [17].”
MethodTranslations of network languages
Stilman, B. (1994) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(94)90037-x · Cited by 37
Behavior-based control for autonomous underwater exploration
Rosenblatt, J. K., Willams, S., & Durrant-Whyte, H. F. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2000.844166 · Cited by 37
“(1) A random sample of pixels are then chosen from the image, and a dynamic programming search [6] is begun from each pixel to find a least cost path from the top to the bottom of the image, using the color distance metric defined above, as shown in Figure 5a.”
MethodAllen, J. F., & Frisch, A. M. (1982) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/981251.981256 · Cited by 37
“The typical argument (e.g., Hayes, 1979; Nilsson, 1980; Charniak, 1981a) proceeds by showing how any given frame or network representation can be mapped to a logically isomorphic (i.e., logically equivalent when the mapping between the two notations is accounted for) FOPC representation.”
BackgroundApplying machine learning to model management in decision support systems
Shaw, M. J., Tu, P.-L., & De, P. (1988) · Decision Support Systems · doi:10.1016/0167-9236(88)90017-6 · Cited by 37
A Decompositional Approach for Computing Least Fixed-Points of Datalog Programs with Z-Counters
Fribourg, L., & Olsén, H. (1997) · Constraints · doi:10.1023/a:1009747629591 · Cited by 37
“This is applicable when the program satisfies certain properties for which sufficient syntactic criteria exist: e.g., boundedness [28], commutatitivity [32], splittability [ 29 , 23].”
BackgroundArtificial Intelligence—An Overview of Similarities with O.R.
Phelps, R. (1986) · Journal of the Operational Research Society · doi:10.1057/jors.1986.3 · Cited by 37
A formal model of trade-off between optimization and execution costs in semantic query optimization
Shekhar, S., Srivastava, J., & Dutta, S. (1992) · Data & Knowledge Engineering · doi:10.1016/0169-023x(92)90034-9 · Cited by 37
Artificial intelligence in the development and design of biochemical processes
Stephanopoulos, G., & Stephanopoulos, G. N. (1986) · Trends in biotechnology · doi:10.1016/0167-7799(86)90118-6 · Cited by 37
METEORs: High Performance Theorem Provers using Model Elimination
Astrachan, O. L., & Loveland, D. W. (1991) · Automated reasoning series · doi:10.1007/978-94-011-3488-0_2 · Cited by 37
“In proof procedures based on Model Elimination, the search tree can be viewed as an AND-OR tree [ 24 ].”
MethodGenerating plans in linear logic
Masseron, M., Tollu, C., & Vauzeilles, J. (1990) · Lecture notes in computer science · doi:10.1007/3-540-53487-3_35 · Cited by 37
An Architecture for Real-Time Rule-Based Control
Handelman, D. A., & Stengel, R. F. (1987) · doi:10.23919/acc.1987.4789576 · Cited by 37
Bhattacharya, D., Konar, A., & Das, P. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.06.073 · Cited by 36
“Classical production rule-based reasoning [52] could be performed to predict the interval of cðtiþdÞ from the known interval of cðtiÞ using the previously acquired rules.”
MethodBunke, H., & Jiang, X. (2000) · International series in intelligent technologies · doi:10.1007/978-1-4615-4401-2_10 · Cited by 36
Finding Optimal-Path Maps for Path Planning across Weighted Regions
Rowe, N. C., & ALEXANDER, R. S. (2000) · The International Journal of Robotics Research · doi:10.1177/02783640022066761 · Cited by 36
“Usually "backpointers" are set whenever the wavefront reaches a new cell, indicating from what direction the "wave" came, though some algorithms set the pointers only for cells meeting certain criteria (like those that use A* search [Nilsson 1980] to improve performance).”
BackgroundSTEP-based feature recognition for manufacturing cost optimization
Han, J., 강무진, & Choi, H.-G. (2001) · Computer-Aided Design · doi:10.1016/s0010-4485(01)00071-9 · Cited by 36
Michener, E. R., Skalak, D. B., & Friedman, M. T. (1993) · International Conference on Artificial Intelligence and Law · doi:10.1145/158976.158991 · Cited by 36
The Interaction Between Knowledge and Practice in the Acquisition of Cognitive Skills
Ohlsson, S. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3172-2_5 · Cited by 34
Moore, R. C. (1994) · Cited by 36
Perspectives on operations research in data and knowledge management
Basu, A. (1998) · European Journal of Operational Research · doi:10.1016/s0377-2217(97)00316-0 · Cited by 36
Combining joint and semi-join operations for distributed query processing
Chen⋆, M.-S., & Yu, P. S. (1993) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.224205 · Cited by 36
Plane parallel a maze router and its application to FPGAs
Palczewski, M. (1992) · doi:10.5555/113938.149679 · Cited by 36
Knowledge retrieval as limited inference
Frisch, A. M., & Allen, J. F. (2005) · Springer eBooks · doi:10.1007/bfb0000065 · Cited by 36
The satisfiability of temporal constraint networks
Valdés‐Pérez, R. E. (1987) · National Conference on Artificial Intelligence · Cited by 36
Reverse Iterative Deepening for Finite-Horizon MDPs with Large Branching Factors
Kolobov, A., Dai, P., Mausam, M., et al. (2012) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v22i1.13523 · Cited by 35
“AO∗ (Nilsson 1980) is an algorithm that works specifically with loop-free MDPs (of which finite-horizon MDPs are a special case).”
BackgroundRighi, R., Samoili, S., López-Cobo, M., Baillet, M. V., Cardona, M., & Prato, G. D. (2020) · Telecommunications Policy · doi:10.1016/j.telpol.2020.101943 · Cited by 35
Using non-linear velocity obstacles to plan motions in a dynamic environment
Large, F., Sckhavat, S., Shiller, Z., et al. (2004) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2002.1238513 · Cited by 35
Mixed deterministic and probabilistic networks
Mateescu, R., & Dechter, R. (2008) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-009-9132-y · Cited by 35
“The AND/OR search tree for a graphical model specializes the notion of AND/OR search spaces for state-space models as defined in Nilsson [33].”
MethodPlanning and ill-defined problems
Ormerod, T. C. (2004) · Psychology Press eBooks · doi:10.4324/9780203493564-13 · Cited by 35
Noise and knowledge acquisition
Manago, M., & Kodratoff, Y. (1987) · Cited by 35
Path-planning algorithm for vehicles operating in orchards
Linker, R., & Blass, T. (2008) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2008.06.002 · Cited by 35
Control strategies for two-player games
Abramson, B. D. (1989) · ACM Computing Surveys · doi:10.1145/66443.66444 · Cited by 35
Planning with Uncertainty in Position Using High-Resolution Maps
González, J. P., & Stentz, A. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.363118 · Cited by 36
“While there are many algorithms that can search the graph described by this grid, A*[68] expands the smallest number of nodes if all the edges have positive costs and an admissible heuristic can be found.”
BackgroundPrinciples and applications of structural image matching
Wang, Y. (1998) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/s0924-2716(98)00002-1 · Cited by 35
The state of the art in parallel production systems
Kuo, S., & MOLDOVAN, D. I. (1992) · Journal of Parallel and Distributed Computing · doi:10.1016/0743-7315(92)90055-r · Cited by 35
Colored 2D maps for robot navigation with 3D sensor data
Wulf, O., Brenneke, C., & Wagner, B. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2004.1389864 · Cited by 35
Hierarchical path planning in a multi-robot environment with a simple navigation function
Conte, G., & Zulli, R. (1995) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.370196 · Cited by 35
Work-load balancing in highly parallel depth-first search
Reinefeld, A., & Schnecke, V. (2002) · doi:10.1109/shpcc.1994.296719 · Cited by 35
Explicitly biased generalization
Gordon, D. F., & Perlis, D. R. (1989) · Computational Intelligence · doi:10.1111/j.1467-8640.1989.tb00317.x · Cited by 35
“The chronological backtracking, on the other hand, proceeds one level at a time and is similar to the depth-first search back-tracking described in Nilsson (1980).”
BackgroundStructure of intelligence for an autonomous vechile
Chavez, R. M., & Meystel, A. (2005) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1984.1087160 · Cited by 35
Manufacturing feature recognition toward integration with process planning
Han, J. H., Han, I., Lee, E., et al. (2001) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.931522 · Cited by 34
“If we can guarantee that never overestimates , the algorithm always finds an optimal path to a goal, if one exists [27].”
BackgroundLegal dilemmas of Estonian artificial intelligence strategy: in between of e-society and global race
Kerikmäe, T., & Pärn-Lee, E. (2020) · AI & Society · doi:10.1007/s00146-020-01009-8 · Cited by 41
Efficient steganalysis using convolutional auto encoder network to ensure original image quality
Ayaluri, M. R., Reddy, K. S., Konda, S. R., et al. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.356 · Cited by 34
“Data concealing systems can be utilized to advance the media, for example by including individual and restorative data inside medicinal pictures ( Nilsson, 2014 ).”
BackgroundExploiting Problem Decomposition in Multi-objective Constraint Optimization
Marinescu, R. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04244-7_47 · Cited by 34
“We start by revisiting the notion of partial solution trees [9] to represent sets of solution trees.”
BackgroundCordella, L. P., Foggia, P., Sansone, C., et al. (1996) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1996.546934 · Cited by 34
R&D Decision Advisor: An interactive approach to normative decision system model construction
Regan, P. J., & Holtzman, S. (1995) · European Journal of Operational Research · doi:10.1016/0377-2217(94)00321-3 · Cited by 35
Heuristic search in cyclic AND/OR graphs
Hansen, E. A., & Zilberstein, S. (1998) · Cited by 34
On optimizing the evaluation of a set of expressions
Grant, J., & Minker, J. (1982) · International Journal of Parallel Programming · doi:10.1007/bf00999727 · Cited by 34
Vincens, P., & Tarroux, P. (1987) · Electrophoresis · doi:10.1002/elps.1150080204 · Cited by 34
Principles of information structure common to six levels of the human cognitive system
Leyton, M. (1986) · Information Sciences · doi:10.1016/0020-0255(86)90067-8 · Cited by 36
Expert systems in mechanical and manufacturing engineering
Pham, D. T., & Pham, P. T. N. (1988) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/bf02601587 · Cited by 34
Lai, X.-C., Ge, S. S., & Mamun, A. A. (2007) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2007.906577 · Cited by 34
Collision avoidance: divide-and-conquer approach by space characterization and intermediate goals
Hasegawa, T., & Terasaki, H. (1988) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.7484 · Cited by 34
Žužek, A., Biasizzo, A., & Novak, F. (2000) · Microprocessors and Microsystems · doi:10.1016/s0141-9331(00)00073-9 · Cited by 34
“From the computational point of view, promising results have been achieved with the approach based on integrating concepts from the information theory and the heuristic AND/OR graph search methods [6, 10 ,11].”
BackgroundMethodA formal model of diagnostic inference, II. Algorithmic solution and application
Reggia, J. A., Nau, D. S., Wang, P. Y., et al. (1985) · Information Sciences · doi:10.1016/0020-0255(85)90016-7 · Cited by 34
Model-based probabilistic reasoning for electronics troubleshooting
Cantone, R., Pipitone, F. J., Lander, W. B., & Marrone, M. P. (1983) · International Joint Conference on Artificial Intelligence · Cited by 34
An intelligent search method for query optimization by semijoins
Yoo, H., & Lafortune, S. (1989) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.87962 · Cited by 34
Symbol-Processing Theories and the SOAR Architecture
Lindsay, R. (1991) · Psychological Science · doi:10.1111/j.1467-9280.1991.tb00155.x · Cited by 34
“Predicate logic is a neutral, homogeneous representation that is advocated by many as the appropriate representational medium of artificial intelligence (McCarthy, -1979, 1980; Nilsson, 1980).”
BackgroundHybrid Risk-Aware Conditional Planning with Applications in Autonomous Vehicles
Huang, X., Jasour, A. M. Z., Deyo, M. Q., et al. (2018) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2018.8619771 · Cited by 33
“Similar to the AO* algorithm [18], RAO* utilizes a value heuristic to guide the search towards optimal policies.”
MethodReview of management information systems research: A management support emphasis
Cooper, R. B. (1988) · Information Processing & Management · doi:10.1016/0306-4573(88)90079-9 · Cited by 33
A metric space approach to the specification of the heuristic function for the A* algorithm
Passino, K. M., & Antsaklis, P. J. (1994) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.259697 · Cited by 33
Ney, H. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.134063 · Cited by 33
An expert system based controller for an automated storage/retrieval system
Linn, R. J., & Wysk, R. A. (1990) · International Journal of Production Research · doi:10.1080/00207549008942752 · Cited by 33
A conceptual framework for combining artificial intelligence and optimization in engineering design
Cagan, J., Grossmann, I. E., & Hooker, J. (1997) · Research in Engineering Design · doi:10.1007/bf01607055 · Cited by 33
Learning from demonstration: repetitive movements for autonomous service robotics
Urbanek, H., Albu‐Schäffer, A., & van der Smagt, P. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2004.1389957 · Cited by 33
“A (e.g., [ 13 ]) is a modified breadth-first search (BFS) that utilizes a heuristic to estimate the distance from the current to the goal state.”
BackgroundPlan-N-Scan: A Robotic System for Collision-Free Autonomous Exploration and Workspace Mapping
Renton, P., Greenspan, M., ElMaraghy, H. A., et al. (1999) · Journal of Intelligent & Robotic Systems · doi:10.1023/a:1008090503603 · Cited by 33
“This system, which is based on the STRIPS method [26], is capable of selecting a sequence of intermediate targets in order to scan the original target.”
MethodAdmissible heuristic search in and/or graphs
Bagchi, A., & Mahanti, A. (1983) · Theoretical Computer Science · doi:10.1016/0304-3975(83)90050-6 · Cited by 33
Path planning among moving obstacles using spatial indexing
Fujimura, K., & Samet, H. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1988.12305 · Cited by 33
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
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 ›
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