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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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An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems
Shaw, M. J., & Whinston, A. B. (1989) · IIE Transactions · doi:10.1080/07408178908966221 · Cited by 51
Foundations of explanations as model reconciliation
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Application of the fast marching method for outdoor motion planning in robotics
Garrido, S., Malfáz, M., & Blanco, D. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.10.012 · Cited by 51
“A∗ uses a heuristic to focus the search from a particular start location towards the goal and thus produces a path from a single location to the goal very efficiently [5].”
MethodLinear connectivity problems in directed hypergraphs
Thakur, M., & Tripathi, R. S. (2009) · Theoretical Computer Science · doi:10.1016/j.tcs.2009.02.038 · Cited by 50
Knowledge-based model for constructing master assembly sequence
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Close target reconnaissance using autonomous UAV formations
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“It was introduced by [10], as an effective heuristic improvement of Dijkstra’s algorithm, see [11], and it provides a better average performance than Dijkstra’s with respect to searched nodes when one only needs the optimum path.”
MethodGlobal Path Planning for Unmanned Ground Vehicles
Giesbrecht, J. L. (2004) · Defense Technical Information Center (DTIC) · Cited by 51
Action Selection for MDPs: Anytime AO* Versus UCT
Bonet, B., & Geffner, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8369 · Cited by 50
“We call the new algorithm Anytime AO*, because it is a very simple variation of the classical AO* algorithm for AND/OR graphs (Nilsson 1980) that is optimal even in the presence of non-admissible heuristics.”
MethodA result on the computational complexity of heuristic estimates for the A∗ algorithm
Valtorta, M. (1984) · Information Sciences · doi:10.1016/0020-0255(84)90009-4 · Cited by 50
“The facts given are also discussed in more detail in my thesis [24].”
BackgroundPAO for planning with hidden state
Ferguson, D., Stentz, A., & Thrun, S. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2004.1307491 · Cited by 50
“To solve for the cost of an individual state s a fast A* based focussed propagation of values can be performed [6].”
BackgroundTROVATO, K. I., & Dorst, L. (2002) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2002.1047763 · Cited by 50
AND/OR net representation for robotic task sequence planning
Cao, T., & Sanderson, A. C. (1998) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/5326.669552 · Cited by 50
“To find the optimal solution graph with minimum cost, a heuristic search procedure, algorithm [24], was used to speed up the search.”
MethodAction and institution, network and function: The cybernetic concept of social structure
Fararo, T. J., & Skvoretz, J. V. (1986) · Sociological Forum · doi:10.1007/bf01115738 · Cited by 50
Admissibility of AO∗ when heuristics overestimate
Chakrabarti, P. P., Ghose, S., & DeSarkar, S. C. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90005-1 · Cited by 50
Edgel aggregation and edge description
Nalwa, V. S., & Pauchon, E. (1987) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(87)90057-0 · Cited by 58
Hearst, M. A., Levy, A. Y., Knoblock, C. A., et al. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.722342 · Cited by 50
The ontological status of intentional states: Nailing folk psychology to its perch
Churchland, P. M. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058647 · Cited by 50
Negotiated A* Routing for FPGAs ∗
Tessier, R. (1998) · Cited by 49
Benke, K. K., Norng, S., Robinson, N. J., Chia, K., Rees, D. B., & Hopley, J. (2020) · Geoderma · doi:10.1016/j.geoderma.2020.114210 · Cited by 47
Statistical parsing of messages
Chitrao, M. V., & Grishman, R. (1990) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/116580.116665 · Cited by 49
Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions
Chen, J., Holte, R. C., Zilles, S., et al. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/69 · Cited by 48
“Along with these algorithms there have been explanations for the poor performance of bidirectional heuristic search, including that the frontiers miss [Nilsson, 1982] or that the frontiers meet early, and a long time is spent proving the optimal solution [Kaindl and Kainz, 1997].”
BackgroundAgents’ Interaction in Virtual Storytelling
Cavazza, M., Charles, F., & Mead, S. J. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44812-8_13 · Cited by 48
“graph [ 14 ]. The process by which a state-space is normally from a Hierarchical Task Network (HTN) is called [23].”
BackgroundError-tolerant graph matching: A formal framework and algorithms
Bunke, H. (1998) · Lecture notes in computer science · doi:10.1007/bfb0033223 · Cited by 48
“The most common approach is based on tree search with A*-like algorithms [15].”
MethodBehavioral matchmaking for service retrieval: Application to conversation protocols
Grigori, D., Corrales, J. C., & Bouzeghoub, M. (2008) · Information Systems · doi:10.1016/j.is.2008.02.004 · Cited by 48
Network Languages for Complex Systems
Stilman, B. (1993) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(93)90331-o · Cited by 48
Logical inference of Horn clauses in Petri net models
Lin, C., Chaudhury, A., Whinston, A. B., et al. (1993) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.224194 · Cited by 48
“For example directed and acyclic networks are used as a syntactic device for representing facts in a first order logic system [Nil 80].”
MethodPlanning and Control in Artificial Intelligence: A Unifying Perspective
Bonet, B., & Geffner, H. (2001) · Applied Intelligence · doi:10.1023/a:1011286518035 · Cited by 48
“While solution methods in classical planning have traditionally been based on divide-and-conquer ideas [ 13 , 19], methods based on explicit state space exploration and suitable domain-independent heuristic have recently been shown to be effective [23].”
MethodPlanning and reasoning for autonomous vehicle control
Mitchell, J. S. B., Payton, D. W., & Keirsey, D. M. (1987) · International Journal of Intelligent Systems · doi:10.1002/int.4550020204 · Cited by 48
Developing expert systems for management applications
Holroyd, P., Mallory, G. R., Price, D. H. R., et al. (1985) · Omega · doi:10.1016/0305-0483(85)90077-5 · Cited by 48
Policy-based self-adaptive architectures
Georgas, J. C., & Taylor, R. N. (2008) · doi:10.1145/1370018.1370038 · Cited by 47
“One of the first robotic control system architectures to gain wide acceptance was the sense-plan-act architecture (SPA) [11].”
BackgroundLRTDP Versus UCT for Online Probabilistic Planning
Kolobov, A., Mausam, M., & Weld, D. S. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8362 · Cited by 47
“Besides VI and LRTDP, another well-known algorithm for solving them is AO∗ (Nilsson 1980).”
MethodDesign of Sick Chicken Automatic Detection System Based on Improved Residual Network
Zhang, H., & Chen, C. (2020) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec48623.2020.9084666 · Cited by 47
“With the advent of the era of artificial intelligence [10], machine learning, deep learning, and deep neural networks have developed rapidly, and the importance of knowledge in analog intelligence has been explored, and new cognitive simulations have been explored in depth [11].”
BackgroundLocating moving entities in indoor environments with teams of mobile robots
Rosencrantz, M., Gordon, G. J., & Thrun, S. (2003) · Adaptive Agents and Multi-Agents Systems · doi:10.1145/860575.860613 · Cited by 47
“standard path planning algorithms [21].”
A knowledge-based system for process-sequence design in axisymmetric sheet-metal forming
Sitaraman, S. K., Kinzel, G. L., & Altan, T. (1991) · Journal of Materials Processing Technology · doi:10.1016/0924-0136(91)90111-q · Cited by 47
Hao, Y., & Agrawal, S. K. (2005) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2005.01.001 · Cited by 47
An expert system for a resource allocation problem
Slagle, J. R., & Hamburger, H. (1985) · Communications of the ACM · doi:10.1145/4284.4291 · Cited by 47
Expert systems for decision support in EDP auditing
Hansen, J. V., & Messier, W. F. (1982) · International Journal of Parallel Programming · doi:10.1007/bf01001957 · Cited by 47
Learning Improved Integrity Constraints and Schemas From Exceptions in Data and Knowledge Bases
Borgida, A. T., Mitchell, T. M., & Williamson, K. E. (1986) · Topics in information systems · doi:10.1007/978-1-4612-4980-1_23 · Cited by 47
A distributed knowledge-based approach to flexible automation: The contract net framework
Shaw, M. J., & Whinston, A. B. (1988) · International Journal of Flexible Manufacturing Systems · doi:10.1007/bf00713160 · Cited by 47
Complexity results for blocks-world planning
Gupta, N. C., & Nau, D. S. (1991) · Cited by 47
Topological Value Iteration AlgorithmsPreprint
Dai, P., Mausam, Weld, D. S., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.3910 · Cited by 47
“The AO* algorithm (Nilson, 1980) solves acyclic MDPs, so it is not applicable to general MDPs.”
MethodGao, Z., Hau, W. K., Lu, M., Huang, W., Zhang, H., Wu, W., Liu, X., & Zhang, Y. (2015) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2015.03.022 · Cited by 46
A Heuristic Search Approach to Planning with Continuous Resources in Stochastic Domains
Meuleau, N., Benazera, E., Brafman, R. I., et al. (2010) · Cited by 46
Efficient attributed graph matching and its application to image analysis
Bunke, H., & Messmer, B. (1995) · Lecture notes in computer science · doi:10.1007/3-540-60298-4_235 · Cited by 46
“As the search space in error-tolerant matching is even larger than in regular subgraph or bidirectional subgraph isomorphism detection, the use of good heuristics together with A*-like search techniqus [ 18 ] becomes indispensable.”
MethodGoasdoué, F., & Rousset, M.-C. (2004) · ACM Transactions on Internet Technology · doi:10.1145/1013202.1013204 · Cited by 46
Efficient Channel Allocation Tree Generation for Data Broadcasting in a Mobile Computing Environment
Peng, W.-C., & Chen⋆, M.-S. (2003) · Wireless Networks · doi:10.1023/a:1021837531806 · Cited by 46
“and to ensure the optimality of the goal node reached [ 19 ].”
BackgroundMethodResultInfluentialPartial Order Reduction in Directed Model Checking
Lafuente, A. L., Edelkamp, S., & Leue, S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46017-9_10 · Cited by 46
“If h is a lower bound of the distance to a goal state, then A* is admissible, which means that it will always return the shortest path to a goal state [ 19 ].”
BackgroundA behavior-based architecture for autonomous underwater exploration
Rosenblatt, J. K., Williams, S. B., & Durrant-Whyte, H. F. (2002) · Information Sciences · doi:10.1016/s0020-0255(02)00224-4 · Cited by 46
“A random sample of pixels are then chosen from the image, and a dynamic programming search [5] 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 6a.”
MethodIs probabilistic congestion estimation worthwhile?
Westra, J., & Groeneveld, P. R. (2005) · International Workshop on System Level Interconnect Prediction (SLIP) · doi:10.1145/1053355.1053377 · Cited by 46
A branch-and-bound method for optimal atom-type assignment in de novo ligand design
Todorov, N., & Dean, P. M. (1998) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1007994827087 · Cited by 46
“A spanning tree is grown using a breadthfirst search method [ 25 ].”
BackgroundMethodQualitative computing: Approaches and issues
Brent, E. E. (1984) · Qualitative Sociology · doi:10.1007/bf00987106 · Cited by 46
“Logical Deduction. Many programs have been developed which use logical deduction to deduce the logical implications of knowledge and even to prove theorems {Bledsoe, 1977; Nilsson, 1980; Bobrow, 1980}.”
BackgroundHypothesis integration in image understanding systems
Hwang, V. S.-S., Davis, L. S., & Matsuyama, T. (1986) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(86)90081-2 · Cited by 46
Blockchain-Enabled Intelligent Vehicular Edge Computing
Islam, S., Badsha, S., Sengupta, S., La, H. M., Khalil, I., & Atiquzzaman, M. (2021) · IEEE Network · doi:10.1109/mnet.011.2000554 · Cited by 45
“Next-generation smart vehicles are embedded with a multitude of resource-intensive artificial intelligence (AI) [2] applications, ranging from pedestrian detection to fuel scheduling.”
BackgroundYu, T. (2001) · Genetic Programming and Evolvable Machines · doi:10.1023/a:1012926821302 · Cited by 45
“In AI, “divide and conquer” has long been recognized as an important problem solving approach [ 32 ].”
BackgroundInterleaving a join sequence with semijoins in distributed query processing
Chen⋆, M.-S., & Yu, P. S. (1992) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/71.159044 · Cited by 45
On the complexity of domain-independent planning
Erol, K., Nau, D. S., & Subrahmanian, V. S. (1992) · Cited by 45
Adaptive mapping and navigation by teams of simple robots
Cohen, W. W. (1996) · Robotics and Autonomous Systems · doi:10.1016/0921-8890(96)00012-7 · Cited by 45
Failure driven dynamic search control for partial order planners: an explanation based approach
Kambhampati, S., Katukam, S., & Qu, Y. (1996) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00005-7 · Cited by 45
“43 From the EBL point of view, the only thing that is required is that the search tree explicitly model this instantiation process, so that explanations of failures containing precondition constraints can be appropriately regressed.”
BackgroundThree approaches to heuristic search in networks
Bagchi, A., & Mahanti, A. (1985) · Journal of the ACM · doi:10.1145/2455.2458 · Cited by 45
“The m’ethod has been described by Nilsson [ 8 ] in the discussion following Procedure AO*.”
MethodAsynchronous control of rotation and translation for a robot vehicle
Crowley, J. L., & Reignier, P. (1992) · Robotics and Autonomous Systems · doi:10.1016/0921-8890(92)90003-h · Cited by 45
Bootstrap Learning via Modular Concept Discovery
Dechter, E., Malmaud, J., Adams, R. P., & Tenenbaum, J. B. (2014) · Cited by 43
A novel feature selection method for microarray data classification based on hidden Markov model
Momenzadeh, M., Sehhati, M., & Rabbani, H. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103213 · Cited by 43
Decoding Substitution Ciphers by Means of Word Matching with Application to OCR
Nagy, G., Seth, S. C., & Einspahr, K. L. (1987) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1987.4767969 · Cited by 44
“RANKSYMBOLS and RANKLETTERS allow heuristic reordering of the search [12]-[14].”
BackgroundAn N-best candidates-based discriminative training for speech recognition applications
Chen, J.-K., & Soong, F. K. (1994) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.260363 · Cited by 44
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models
Mateescu, R., Dechter, R., & Marinescu, R. (2008) · CaltechAUTHORS (California Institute of Technology) · Cited by 44
Optimal learning in artificial neural networks: A review of theoretical results
Bianchini, M., & Gori, M. (1996) · Neurocomputing · doi:10.1016/0925-2312(95)00032-1 · Cited by 45
A statistical approach to solving the EBL utility problem
Greiner, R., & Jurišica, I. (1992) · Cited by 44
A Review of knowledge-based planning techniques
Tate, A. (1984) · The Knowledge Engineering Review · doi:10.1017/s0269888900000485 · Cited by 48
An approach towards the verification of expert systems using numerical petri nets
Liu, N. K., & Dillon, T. S. (1991) · International Journal of Intelligent Systems · doi:10.1002/int.4550060303 · Cited by 44
Developing visual sensing strategies through next best view planning
Dunn, E., van den Berg, J., & Frahm, J. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354179 · Cited by 42
“[ 8 ]. However, alternative path planning techniques can be considered based on computational cost and completeness considerations.”
Using artificial intelligence to overcome over-indebtedness and fight poverty
Ferreira, M. B., Pinto, D. C., Herter, M. M., Soro, J. C., Vanneschi, L., Castelli, M., & Peres, F. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.10.035 · Cited by 46
Optimal ship navigation with safety distance and realistic turn constraints
Arı, İ., Aksakallı, V., Aydogdu, V., et al. (2013) · European Journal of Operational Research · doi:10.1016/j.ejor.2013.03.022 · Cited by 43
“A⁄ algorithm (Hart et al., 1968, 1972; Nilsson, 1980) is an alternative to Dijkstra’s algorithm, which has the same worst-case complexity, but empirically runs faster.”
BackgroundMethodAssembly sequences planning for simultaneous engineering applications
Laperrière, L., & ElMaraghy, H. A. (1994) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/bf01751121 · Cited by 43
Coarse-to-fine dynamic programming
Raphael, C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.977562 · Cited by 43
“Bound” algorithm familiar from the AI literature [ 15 ], [16], [17].”
MethodThe Temporal Knapsack Problem and Its Solution
Bartlett, M., Frisch, A. M., Hamadi, Y., et al. (2005) · Lecture notes in computer science · doi:10.1007/11493853_5 · Cited by 43
“To do this, we currently employ two strategies: the AO* algorithm described by Nilsson [9] (which is itself based on an algorithm of Martelli and Montanari [10]), and a depth-first algorithm.”
MethodOn the heuristics of A* or A algorithm in ITS and robot path-planning
Goto, T., Kosaka, T., & Noborio, H. (2004) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2003.1248802 · Cited by 43
“Unfortunately, the heuristics does not always maintain the admissibility, and thus our algorithm does not sometimes pick up the optimal path.”
BackgroundMethodToward agent programs with circuit semantics
Nilsson, N. J. (1992) · NASA STI Repository (National Aeronautics and Space Administration) · Cited by 43
Self-citationAPPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES TO REMOTE SENSING∗
Estes, J. E., Sailer, C. T., & Tinney, L. R. (1986) · The Professional Geographer · doi:10.1111/j.0033-0124.1986.00133.x · Cited by 43
Integer programming approach to process planning
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Explicit graphs in a functional model for spatial databases
Erwig, M., & Güting, R. H. (1994) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.317707 · Cited by 43
“The shortest_path operation is to be implemented by the A* algorithm [49] which is guided by a heuristic function that estimates the distance from a node encountered during the search to the target node.”
BackgroundMethodUnstructured tree search on SIMD parallel computers
Karypis, G., & Kumar, V. (1994) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/71.313122 · Cited by 43
“suits We solved various instances of the 15-puzzle problem [23] taken from [13], on a CM-2 massively parallel SIMD computer.”
Detecting Driver Behavior Using Stacked Long Short Term Memory Network With Attention Layer
Kouchak, S. M., & Gaffar, A. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2986697 · Cited by 43
“Memoryless methods, such as traditional neural networks, don’t consider past events, and information of each sample is used separately to tune the system [44], [51].”
BackgroundImpacts of artificial intelligence
Boden, M. A. (1984) · Futures · doi:10.1016/0016-3287(84)90007-7 · Cited by 42
Dynamic maintenance of directed hypergraphs
Ausiello, G., Nanni, U., & Italiano, G. F. (1990) · Theoretical Computer Science · doi:10.1016/0304-3975(90)90030-l · Cited by 42
A knowledge-based approach to ECG interpretation using fuzzy logic
Kundu, M., Nasipuri, M., & Basu, D. (1998) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.662764 · Cited by 42
“The basic cause of this limitation is that modus ponens [9] which is the rule of inference of two valued logic necessitates exact matching of facts with all preconditions of a rule to fire it.”
BackgroundAn intelligent collision avoidance system for AUVs using fuzzy relational products
Lee, Y.-I. (2003) · Information Sciences · doi:10.1016/j.ins.2003.07.003 · Cited by 42
“A search aims for global optimization which reflects the every obstacle arranged on the navigation environment while the heuristic method aims for local optimization which reflects the obstacles detected presently by the sensors [17,18].”
BackgroundFast place and route approaches for fpgas
Tessier, R., & Ward, S. A. (1999) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 42
Extracting Effective and Admissible State Space Heuristics from the Planning Graph
Nguyen, X., & Kambhampati, S. (2000) · Cited by 42
BI-POMDP: Bounded, incremental partially-observable Markov-model planning
Washington, R. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63912-8_105 · Cited by 42
“Thus the AO* algorithm [ 7 ] can be used to expand the tree.”
MethodJohnson, P. E., Jamal, K., & Berryman, R. G. (1989) · Accounting Organizations and Society · doi:10.1016/0361-3682(89)90035-4 · Cited by 42
Vasandani, V., & Govindaraj, T. (1995) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.391288 · Cited by 42
“Thus, domain knowledge is of two types: declarative and procedural (e.g., Nilsson 1980).”
BackgroundA general programming language for unified planning and control
Levinson, R. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(94)00075-c · Cited by 42
Guaranteeing serializable results in synchronous parallel production systems
Schmolze, J. G. (1991) · Journal of Parallel and Distributed Computing · doi:10.1016/0743-7315(91)90096-r · Cited by 42
Representing actions with an assumption-based truth maintenance system
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A review on ship motions and quiescent periods prediction models
Cademartori, G., Oneto, L., Valdenazzi, F., et al. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.114822 · Cited by 41
Learning from error and the design of task environments
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Tools and techniques for knowledge based expert systems for engineering design
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Enabling robotic social intelligence by engineering human social-cognitive mechanisms
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MM: A bidirectional search algorithm that is guaranteed to meet in the middle
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Task decomposition on abstract states, for planning under nondeterminism
Kuter, U., Nau, D. S., Pistore, M., et al. (2008) · Artificial Intelligence · doi:10.1016/j.artint.2008.11.012 · Cited by 41
“The terminal states of policy(π̄) are analogous to the fringe nodes of a partial solution tree in an AND/OR search algorithm such as AO* [11], in the sense that Yoyo will need to “solve” each of these states in order to find a solution to the planning problem.”
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
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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