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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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Statistical machine translation : from single-word models to alignment templates
Och, F. J. (2002) · RWTH Publications (RWTH Aachen) · doi:10.18154/rwth-conv-120579 · Cited by 148
A good ligand is hard to find: Automated docking methods
Blaney, J. M., & Dixon, J. S. (1993) · Perspectives in Drug Discovery and Design · doi:10.1007/bf02174531 · Cited by 141
Sharma, O., Sahoo, N. C., & Puhan, N. B. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104211 · Cited by 143
L 1 shortest paths among polygonal obstacles in the plane
Mitchell, J. S. B. (1992) · Algorithmica · doi:10.1007/bf01758836 · Cited by 141
A Robust Approach to High-Speed Navigation for Unrehearsed Desert Terrain
Urmson, C., Anhalt, J., Bartz, D., Clark, M., Galatali, T., Gutierrez, A., Harbaugh, S., Johnston, J., Kato, H. “., Koon, P., et al. (2007) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-540-73429-1_2 · Cited by 139
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Finn, P., Muggleton, S., Page, D., & Srinivasan, A. (1998) · Machine Learning · doi:10.1023/a:1007460424845 · Cited by 138
“In learning a single clause, progol performs a complete search of the hypothesis space, using pruning similar to that used in A * (Nilsson, 1980), to find a clause c that maximizes a compression function.”
MethodExplanation-based learning: a survey of programs and perspectives
Ellman, T. (1989) · ACM Computing Surveys · doi:10.1145/66443.66445 · Cited by 138
“This is essentially equivalent to calculating “weakest preconditions” as formalized in Dijkstra [ 19761 and to performing goal regression [Nilsson 1980; Waldinger 19771.”
BackgroundMethodInfluentialBundy, A., & Wallen, L. A. (1984) · Springer eBooks · doi:10.1007/978-3-642-96868-6_25 · Cited by 138
Search and Reasoning in problem solving
Simon, H. A. (1983) · Artificial Intelligence · doi:10.1016/s0004-3702(83)80003-4 · Cited by 137
Conformant Planning via Symbolic Model Checking
Cimatti, A., & Roveri, M. (2000) · Cited by 136
Randomized path planning for redundant manipulators without inverse kinematics
Weghe, M. V., Ferguson, D., & Srinivasa, S. S. (2007) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2007.4813913 · Cited by 137
“Dijkstra’s search [7] or A* [8]) are usually far too memory and computation intensive to generate solutions to manipulator path planning problems.”
BackgroundTwo-dimensional model and algorithm analysis for a class of iterative learning control systems
Geng, Z., CARROLL, R., & XIE, J. (1990) · International Journal of Control · doi:10.1080/00207179008953571 · Cited by 135
Expert systems for image processing: Knowledge-based composition of image analysis processes
Matsuyama, T. (1989) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(89)90103-5 · Cited by 134
In AI we trust? Citizen perceptions of AI in government decision making
Ingrams, A., Kaufmann, W., & Jacobs, D. (2022) · Policy & Internet · doi:10.1002/poi3.276 · Cited by 133
Bidirectional Heuristic Search Reconsidered
Kaindl, H., & Kainz, G. (1997) · Journal of Artificial Intelligence Research · doi:10.1613/jair.460 · Cited by 132
“In some sense, this bound may look quite weak, but actually Nilsson (1980) conjectured that a bidirectional heuristic search may expand twice as many nodes as would a corresponding unidirectional one.”
BackgroundGillet, V. J., Myatt, G. J., Zsoldos, Z., & Johnson, A. P. (1995) · Perspectives in Drug Discovery and Design · doi:10.1007/bf02174466 · Cited by 131
“In the first stage, one of the trees is expanded breadth-first [ 26 ].”
BackgroundPlanning Graph Heuristics for Belief Space Search
Bryce, D., Kambhampati, S., & Smith, D. E. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1869 · Cited by 132
“The main differences between our formulation of AO* and thatof Nilsson (1980) are that we do not allow cycles in the search graph, we update the costs ofnodes with an average rather than a summation, and use a weighted estimate of future cost.”
MethodInfluentialAutonomous robot navigation based on OpenStreetMap geodata
Hentschel, M., & Wagner, B. (2010) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2010.5625092 · Cited by 131
“With the representation of all admissible ways path W , path planning is performed with the well-known A* search algorithm [5].”
MethodExpected-outcome: a general model of static evaluation
Abramson, B. D. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.44404 · Cited by 131
A Survey of Methods for Searching the Conformational Space of Small and Medium‐Sized Molecules
Leach, A. R. (1991) · Reviews in computational chemistry · doi:10.1002/9780470125793.ch1 · Cited by 131
From E-R to “A-R” — Modelling strategic actor relationships for business process reengineering
Yu, E. S. K., & Mylopoulos, J. (1994) · Lecture notes in computer science · doi:10.1007/3-540-58786-1_101 · Cited by 133
Path Planning and Obstacle Avoidance for Autonomous Mobile Robots: A Review
Kunchev, V., Jain, L. C., Ivancevic, V. G., et al. (2006) · Lecture notes in computer science · doi:10.1007/11893004_70 · Cited by 130
Self‐supervised terrain classification for planetary surface exploration rovers
Brooks, C. A., & Iagnemma, K. D. (2012) · Journal of Field Robotics · doi:10.1002/rob.21408 · Cited by 130
“Existing path–planning algorithms can generate a route to a target that avoids known obstacles only if they are given an accurate map of the traversability of the surrounding terrain (Goldberg, Maimone, & Matthies, 2002; Nilsson, 1982; Stentz, 1994).”
Background(1987) · Cited by 128
The *-minimax search procedure for trees containing chance nodes
Ballard, B. W. (1983) · Artificial Intelligence · doi:10.1016/s0004-3702(83)80015-0 · Cited by 128
The emergence of path-dependent behaviors in cooperative contexts
Egidi, M., & Narduzzo, A. (1997) · International Journal of Industrial Organization · doi:10.1016/s0167-7187(97)00007-6 · Cited by 130
The minimum labeling spanning trees
Chang, R.-S., & Shing-Jiuan, L. (1997) · Information Processing Letters · doi:10.1016/s0020-0190(97)00127-0 · Cited by 128
On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm
Koehler, J., & Hoffmann, J. (2000) · Journal of Artificial Intelligence Research · doi:10.1613/jair.715 · Cited by 127
“…to us, quite a number of non-invertible planningproblems have also been proposed in the planning literature, e.g., the register assignmentproblem (Nilsson, 1980), the robot rossing a road problem (Sanborn & Hendler, 1988), someinstan es of manufa turing problems (Regli, Gupta, & Nau, 1995), and…”
BackgroundA second generation user interface design environment
Sukaviriya, P. “., Foley, J. D., & Griffith, T. W. (1993) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/169059.169299 · Cited by 126
Trajectory planning in a dynamic workspace: a 'state-time space' approach
Fraichard, T. (1998) · Advanced Robotics · doi:10.1163/156855399x00928 · Cited by 125
Commonsense Reasoning: An Event Calculus Based Approach
Mueller, E. T. (2006) · doi:10.1016/c2014-0-00192-x · Cited by 122
Real-Time Planning for Multiple Autonomous Vehicles in Dynamic Uncertain Environments
Pongpunwattana, A., & Rysdyk, R. T. (2004) · Journal of Aerospace Computing Information and Communication · doi:10.2514/1.12919 · Cited by 121
Model-based stroke extraction and matching for handwritten Chinese character recognition
Liu, C., Kim, I., & Kim, J. H. (2001) · Pattern Recognition · doi:10.1016/s0031-3203(00)00165-5 · Cited by 120
Heuristic search in restricted memory
Chakrabarti, P. P., Ghose, S., Acharya, A., et al. (1989) · Artificial Intelligence · doi:10.1016/0004-3702(89)90010-6 · Cited by 120
Gral: an extensible relational database system for geometric applications
Güting, R. H. (1989) · Cited by 120
Total-order planning with partially ordered subtasks
Nau, D. S., Muñoz‐Avila, H., Cao, Y., et al. (2001) · Cited by 117
Multiple Mobile Robot Teams, Path Planning and Motion Coordination in
Parker, L. E. (2009) · Encyclopedia of Complexity and Systems Science · doi:10.1007/978-0-387-30440-3_344 · Cited by 114
AND/OR graph representation of assembly plans
de Mello, L. S. H., & Sanderson, A. C. (1990) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.54734 · Cited by 114
“Structures called AND/OR graphs [8], or hypergraphs, are useful in representing decomposable problems and they have been used to represent the disassembly problem.”
MethodTrends and Challenges in Unmanned Surface Vehicles (USV): From Survey to Shipping
Barrera, C., Armas, I. P., Luis, F., et al. (2021) · TransNav the International Journal on Marine Navigation and Safety of Sea Transportation · doi:10.12716/1001.15.01.13 · Cited by 113
On the multiple-query optimization problem
Sellis, T., & Ghosh, S. K. (1990) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.54724 · Cited by 113
Robot motion planning with uncertainty in control and sensing
Latombe, J., Lazanas, A., & Shekhar, S. (1991) · Artificial Intelligence · doi:10.1016/0004-3702(91)90023-d · Cited by 113
Chen, C. M., & Roussopoulos, N. (1994) · Lecture notes in computer science · doi:10.1007/3-540-57818-8_61 · Cited by 113
Diagnosis using hierarchical design models
Genesereth, M. (1982) · National Conference on Artificial Intelligence · Cited by 113
A hierarchical approach to recognition of handwritten Bangla characters
Basu, S., Das, N., Sarkar, R., et al. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.01.008 · Cited by 112
Survey on assembly sequencing: a combinatorial and geometrical perspective
Jiménez, P. (2011) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-011-0578-5 · Cited by 112
“The natural choice for directed graphs is an heuristically guided search algorithm like A* Nilsson (1980), which is guaranteed to find always a minimum-cost path (i.e., assembly sequence).”
BackgroundMethodInfluentialDIAGRAMMATIC REPRESENTATION AND REASONING
Kulpa, Z. (1994) · Cited by 112
Towards Lazy Data Association in SLAM
Hähnel, D., Thrun, S., Wegbreit, B., & Burgard, W. (2005) · Springer tracts in advanced robotics · doi:10.1007/11008941_45 · Cited by 113
“A simple argument (reminiscent of that underlying the correctness of the A* algorithm [19]) enables us to drastically reduce the number of nodes expended during this search.”
BackgroundInference procedures under uncertainty for the problem-reduction method
Ishizuka, M., Fu, K., & Yao, J. T. P. (1982) · Information Sciences · doi:10.1016/0020-0255(82)90047-0 · Cited by 112
Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions
He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3496744 · Cited by 162
Decoding algorithm in statistical machine translation
Wang, Y., & Waibel, A. (1997) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/976909.979664 · Cited by 111
The Archimedes 2 mechanical assembly planning system
Kaufman, S. G., Wilson, R. H., Jones, R. E., et al. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1996.509225 · Cited by 111
“The search algorithm used is an implementation of the A* state-space search algorithm [12], which is guaranteed to find a path of minimum cost under certain circumstances.”
MethodSpeeding up problem solving by abstraction: a graph oriented approach
Holte, R. C., Mkadmi, T., Zimmer, R. M., & MacDonald, A. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(95)00111-5 · Cited by 111
“As a second example, ALPINE is also unable to generate a non trivial abstraction for the standard representation of the Blocks World (e.g. the one in [Nilsson, 1980]).”
BackgroundInference analysis and adaptive training for belief rule based systems
Chen, Y., Yang, J., Xu, D., et al. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.077 · Cited by 110
“Moreover, it has also been convincingly argued that other knowledge representation forms can be transformed into rule-based schemes (Nilsson, 1982; Sun, 1995).”
BackgroundA predicate-transition net model for parallel interpretation of logic programs
Murata, T., & Zhang, D. (1988) · IEEE Transactions on Software Engineering · doi:10.1109/32.4671 · Cited by 110
“From Fig. 3, we can see that in the net model, enabling condition corresponds to the unifying composition [ 43 ] and firing rule is similar to the hyperresolution rule [4],”
BackgroundResultDynamic Courseware Generation on the WWW
Vassileva, J., & Deters, R. (1998) · British Journal of Educational Technology · doi:10.1111/1467-8535.00041 · Cited by 109
“The planning algorithm is a modification of a well known algorithm for graph planning AO* (Nilsson, 1980).”
MethodSimilarity measures for structured representations
Bunke, H., & Messmer, B. (1994) · Lecture notes in computer science · doi:10.1007/3-540-58330-0_80 · Cited by 109
“A possible approach to graph edit distance computation is state space search [12].”
BackgroundA domain independent explanation-based generalizer
Mooney, R. J., & Bennett, S. (1986) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 109
Reinventing Innovation Management: The Impact of Self-Innovating Artificial Intelligence
Hutchinson, P. (2020) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2020.2977222 · Cited by 122
Backjump-based backtracking for constraint satisfaction problems
Dechter, R., & Frost, D. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00120-0 · Cited by 108
Best-First Search Methods for Constrained Two-Dimensional Cutting Stock Problems
Viswanathan, K. V., & Bagchi, A. (1993) · Operations Research · doi:10.1287/opre.41.4.768 · Cited by 108
Automated extraction of odontocete whistle contours
Roch, M. A., Brandes, T., Patel, B., et al. (2011) · The Journal of the Acoustical Society of America · doi:10.1121/1.3624821 · Cited by 107
“Graphs are commonly used to represent the interconnections between nodes and have applications in search, path-finding, etc. (Nilsson, 1980, Chap.”
MethodContingent planning under uncertainty via stochastic satisfiability
Majercik, S. M., & Littman, M. L. (2003) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00379-x · Cited by 106
“This representation of the planning problem is similar to AND/OR trees and MINIMAX trees (Nilsson 1980).”
MethodLearning reactive and planning rules in a motivationally autonomous animat
Donnart, J.-Y., & Meyer, J.-A. (1996) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.499790 · Cited by 107
“The MonaLysa architecture has been conceived in order to generate very general adaptive behaviors, and current work aims at demonstrating its applicability to, for example, traditional block-world planning [39].”
MethodJoint production and transportation scheduling in flexible manufacturing systems
Fontes, D. B. M. M., & Homayouni, S. M. (2018) · Journal of Global Optimization · doi:10.1007/s10898-018-0681-7 · Cited by 105
Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects
Moysiadis, V., Tsolakis, N., Katikaridis, D., et al. (2020) · Applied Sciences · doi:10.3390/app10103453 · Cited by 105
Coupling a UI framework with automatic generation of context-sensitive animated help
Sukaviriya, P. “., & Foley, J. D. (1990) · doi:10.1145/97924.97942 · Cited by 105
A logic for approximate reasoning
Ying, M. (1994) · Journal of Symbolic Logic · doi:10.2307/2275910 · Cited by 103
Multilevel Darwinist Brain (MDB): Artificial Evolution in a Cognitive Architecture for Real Robots
Bellas, F., Duro, R. J., Faíña, A., et al. (2010) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2010.2086453 · Cited by 102
“Finally, Section V provides some conclusions as well as some indications of the work that still needs to be done.”
BackgroundGomez-Fernandez, M., Higley, K. A., Tokuhiro, A., Welter, K. B., Wong, W., & Yang, H. (2019) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2019.110479 · Cited by 105
Online footstep planning for humanoid robots
Kuffner, J., Kagami, S., Nishiwaki, K., et al. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2003.1241712 · Cited by 102
“[4] or [10]).”
Graph partitioning using learning automata
Oommen, B. J., & Croix, E. V. D. S. (1996) · IEEE Transactions on Computers · doi:10.1109/12.485372 · Cited by 102
PROTEST: A Tool for Probabilistic Testability Analysis
Wunderlich, H.-J. (1985) · doi:10.5555/317825.317858 · Cited by 102
Explanation-based generalisation = partial evaluation
van Harmelen, F., & Bundy, A. (1988) · Artificial Intelligence · doi:10.1016/0004-3702(88)90088-4 · Cited by 102
Plan synthesis: a logical perspective
Rosenschein, S. J. (1981) · International Joint Conference on Artificial Intelligence · Cited by 109
Parallel best—first search of state-space graphs: a summary of results
Kumar, V., Ramesh, K., & Rao, V. N. (1988) · Cited by 101
AND/OR Graph and Search Algorithm for Discovering Composite Web Services
Liang, Q. A., & Su, S. Y. W. (2005) · International Journal of Web Services Research · doi:10.4018/jwsr.2005100103 · Cited by 101
“) The service dependency graph is formally represented as an AND/OR graph (Nilsson, 1980), and an AND/ OR graph search algorithm then is applied to find a composite service template (to be defined in the third section) that satisfies the request.”
BackgroundMethodWorld Modeling for the Dynamic Construction of Real-Time Control Plans
Musliner, D., Durfee, E., & Shin, K. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(94)00008-o · Cited by 159
Recognition and shape synthesis of 3-D objects based on attributed hypergraphs
Wong, A. K. C., Lu, S. W., & Rioux, M. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.21797 · Cited by 101
Derivational analogy and its role in problem solving
Carbonell, J. (1983) · National Conference on Artificial Intelligence · Cited by 101
The pace of artificial intelligence innovations: Speed, talent, and trial-and-error
Tang, X., Li, X., Ding, Y., Song, M., & Bu, Y. (2020) · Journal of Informetrics · doi:10.1016/j.joi.2020.101094 · Cited by 131
“Therefore, we divided the AI research in arXiv into four stages (He et al., 2019;Frank et al., 2019; Nilsson,2014; Russell & Norvig, 2016; Tran et al., 2019): the embryo stage (1993–1999), stable stage (2000– 2007), machine-learning stage (”
MethodStructured development of problem solving methods
Fensel, D., & Motta, E. (2001) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.971187 · Cited by 97
“This may be the basis for defining an A* search strategy [66].”
BackgroundOn the efficiency of parallel backtracking
Rao, V. N., & Kumar, V. (1993) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/71.219757 · Cited by 100
Generalized resolution and cutting planes
Hooker, J. (1988) · Annals of Operations Research · doi:10.1007/bf02186368 · Cited by 99
Kortylewski, A., Liu, Q., Wang, A., Sun, Y., & Yuille, A. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01401-3 · Cited by 98
“And-Or graphs (AOG) [44,24,9] have been investigated e.”
BackgroundTwo Causal Theories of Counterfactual Conditionals
Rips, L. J. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2009.01080.x · Cited by 95
A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs
Bunke, H., Foggia, P., Guidobaldi, C., et al. (2002) · Lecture notes in computer science · doi:10.1007/3-540-70659-3_12 · Cited by 98
“In each state a pair of nodes not yet analyzed, the first belonging to the first graph and the second belonging to the second graph, is selected (whenever it exists) through the function NextPair(n1,n2) .”
BackgroundConformant planning via symbolic model checking and heuristic search
Cimatti, A., Roveri, M., & Bertoli, P. (2004) · Artificial Intelligence · doi:10.1016/j.artint.2004.05.003 · Cited by 98
“We assume now that at each step the belief states are selected for extraction from the Open based on a score function ! , so that the algorithms become instances of A algorithms [39].”
BackgroundInfluentialHeterogeneous active agents, I: Semantics
Eiter, T., Subrahmanian, V. S., & Pick, G. G. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00005-3 · Cited by 98
“In most work in AI [73] [41] [82] and logical approaches to action [12], it is assumed that states are sets of ground logical atoms.”
BackgroundPlanning the motions of a mobile robot in a sensory uncertainty field
Takeda, H., Facchinetti, C., & Latombe, J.-C. (1994) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.329009 · Cited by 98
AND/OR Branch-and-Bound search for combinatorial optimization in graphical models
Marinescu, R., & Dechter, R. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2009.07.003 · Cited by 97
“In this section we overview the AND/OR search space for graphical models [1] [8], which forms the core of our work in this paper.”
BackgroundMethodInfluentialAutomated process planning for sheet metal bending operations
Gupta, S. K., Bourne, D., Kim, K. H., et al. (1998) · Journal of Manufacturing Systems · doi:10.1016/s0278-6125(98)80002-2 · Cited by 97
Barraquand, J., & Latombe, J.-C. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1991.131750 · Cited by 97
Path Planning and Motion Coordination in Multiple Mobile Robot Teams
Parker, L. E. (2009) · Cited by 97
Jaques, P. A., Seffrin, H. M., Rubi, G., de Morais, F., Ghilardi, C., Bittencourt, I. I., & Isotani, S. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.04.004 · Cited by 96
“Artificial Intelligence classifies knowledge into two types: declarative and procedural (Nilsson, 1982).”
BackgroundHeuristic methods for flexible flow line scheduling
Kochhar, S., & Morris, R. (1987) · Journal of Manufacturing Systems · doi:10.1016/0278-6125(87)90006-9 · Cited by 94
Statistical cross-language information retrieval using n-best query translations
Federico, M., & Bertoldi, N. (2002) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/564376.564407 · Cited by 92
“Moreover, intermediate results of the Viterbi algorithm can be used by an A∗ search algorithm [12] to efficiently compute the N most probable, or N -best, translations of i.”
MethodSteiner, N., Wood, A., Shojaei, H., et al. (2011) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/1950413.1950425 · Cited by 92
“The Torc routing capability includes an optional preliminary router, along with a global PathFinder [8] implementation to resolve net contention, and an underlying signal router based on an A* search [9].”
MethodA theoretic and practical framework for scheduling in a stochastic environment
Bidot, J., Vidal, T., Laborie, P., & Beck, J. C. (2008) · Journal of Scheduling · doi:10.1007/s10951-008-0080-x · Cited by 91
“In some sense, the capability of dealing with conditional subsets of partially-ordered activities helps bridging the gap between scheduling and planning as such choices introduce some level of (limited) reasoning about actions, suggesting for instance to represent the problem with AND/OR graphs, as it is often done in…”
BackgroundConte, D., Foggia, P., & Vento, M. (2007) · Journal of Graph Algorithms and Applications · doi:10.7155/jgaa.00139 · Cited by 92
Multiprocessing of Combinatorial Search Problems
Wah, Li, G., & Yu, C. F. (1985) · Computer · doi:10.1109/mc.1985.1662926 · Cited by 92
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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