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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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Combining static concurrency analysis with symbolic execution
Young, M., & Taylor, R. N. (1988) · IEEE Transactions on Software Engineering · doi:10.1109/32.6195 · Cited by 91
Clausal intuitionistic logic I. fixed-point semantics
McCarty, L. T. (1988) · The Journal of Logic Programming · doi:10.1016/0743-1066(88)90005-2 · Cited by 91
HARP: A tableau-based theorem prover
Oppacher, F., & Suen, E. (1988) · Journal of Automated Reasoning · doi:10.1007/bf00244513 · Cited by 91
Modeling production rules by Means of Predicate Transition networks
Giordana, A. (1985) · Information Sciences · doi:10.1016/0020-0255(85)90039-8 · Cited by 91
ARTIFICIAL INTELLIGENCE AND ITS APPLICABILITY TO GEOGRAPHICAL PROBLEM SOLVING
Smith, T. R. (1984) · The Professional Geographer · doi:10.1111/j.0033-0124.1984.00147.x · Cited by 90
Autonomous Learning of High-Level States and Actions in Continuous Environments
Mugan, J., & Kuipers, B. J. (2011) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2011.2160943 · Cited by 91
“There are two broad planning frameworks within AI: STRIPS-based goal regression [29], and Markov Decision Process (MDP) planning [30].”
BackgroundTrial-Based Heuristic Tree Search for Finite Horizon MDPs
Keller, T., & Helmert, M. (2013) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v23i1.13557 · Cited by 90
“A finite-horizon MDP induces an acyclic AND/OR graph which can be solved by the AO * algorithm (e.g. Nilsson 1980).”
MethodProgramming Constraint Services
Schulte, C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45945-6 · Cited by 87
Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search
Nguyen, X., Subbarao Kambhampati, B., & Sanchez, R. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00158-8 · Cited by 88
“Planning can be naturally seen as a search through the space of world states [39].”
BackgroundIntegration of acoustic information in a large vocabulary word recognizer
Gupta, V., Lennig, M., & Mermelstein, P. G. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1987.1169578 · Cited by 89
“The fast matcher extends the most likely paths using the stack algorithm (Jelinek 1976), also known as the A* algorithm in the artificial intelligence literature ( Nilsson 1980 ).”
MethodAI modelling of animal movements in a heterogeneous habitat
Folse, L. J., Packard, J. M., & Grant, W. E. (1989) · Ecological Modelling · doi:10.1016/0304-3800(89)90069-0 · Cited by 88
Rapid physics-based rough-terrain rover planning with sensor and control uncertainty
Iagnemma, K. D., Génot, F., & Dubowsky, S. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1999.770446 · Cited by 88
“The first step uses the A* algorithm and an estimate of the local terrain roughness to rapidly plan a path through the range map from the rover start position to the goal position [14].”
MethodVideo indexing and similarity retrieval by largest common subgraph detection using decision trees
Shearer, K., Bunke, H., & Venkatesh, S. (2001) · Pattern Recognition · doi:10.1016/s0031-3203(00)00048-0 · Cited by 113
“The algorithms used for comparison in this section are Ullman's algorithm for exact graph isomorphism detection [30], the A* algorithm for inexact graph isomorphism detection [35], and the decomposition network algorithms of Messmer and Bunke [28].”
MethodHow Machine Learning is Changing e-Government
Alexopoulos, C., Lachana, Z., Androutsopoulou, A., Diamantopoulou, V., Charalabidis, Y., & Loutsaris, M. A. (2019) · Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance · doi:10.1145/3326365.3326412 · Cited by 88
Mechanical genders: how do humans gender robots?
Søraa, R. A. (2017) · Gender Technology and Development · doi:10.1080/09718524.2017.1385320 · Cited by 87
“This is because robots do not have feelings – they can be programmed to feel, but artificial intelligence has not (yet) achieved this, but this is strongly debated (Hinds, 1975; McCarthy, 1987, 2007; McCarthy & Hayes, 1969; Nilsson, 2014).”
BackgroundArchitecturing materials at mesoscale: some current trends
Estrin, Y., Beygelzimer, Y., Kulagin, R., Gumbsch, P., Fratzl, P., Zhu, Y. T., & Hahn, H. (2021) · Materials Research Letters · doi:10.1080/21663831.2021.1961908 · Cited by 89
“Efficient methods aimed at overcoming these difficulties have become available owing to recent Machine Learning (ML) studies [55].”
Strong planning under partial observability
Bertoli, P., Cimatti, A., Roveri, M., et al. (2006) · Artificial Intelligence · doi:10.1016/j.artint.2006.01.004 · Cited by 87
AND/OR graph heuristic search methods
Mahanti, A., & Bagchi, A. (1985) · Journal of the ACM · doi:10.1145/2455.2459 · Cited by 87
“One of these solution graphs is then output by the search algorithm. For examples see [8] and [ 9 ].”
BackgroundMethodResultInfluentialLindsay, R. (1988) · Cognition · doi:10.1016/0010-0277(88)90025-x · Cited by 87
Neural Networks for Mobile Robot Navigation: A Survey
Zou, A., Hou, Z., Fu, S., et al. (2006) · Lecture notes in computer science · doi:10.1007/11760023_177 · Cited by 86
“The existing path planning methods include A* algorithm [26], potential fields [27], BUG2 algorithm [28], and methods using intelligent control technique.”
MethodThe Measure of All Minds: Evaluating Natural and Artificial Intelligence
Hernández‐Orallo, J. (2017) · Cited by 88
The Delayed D* Algorithm for Efficient Path Replanning
Ferguson, D., & Stentz, A. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570414 · Cited by 86
“Such paths can be efficiently generated from a map of the environment using focussed algorithms such as A* [1].”
BackgroundA dynamic programming algorithm for constructing optimal prefix-free codes with unequal letter costs
Golin, M. J., & Rote, G. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.705558 · Cited by 86
“[17].”
Generality versus specificity: an experience with AI and OR techniques
Hentenryck, P. V., & Carillon, J.-P. (1988) · National Conference on Artificial Intelligence · Cited by 85
CADIAG: Approaches to computer-assisted medical diagnosis
Adlassnig, K.-P., Kolarz, G., Scheithauer, W., et al. (1985) · Computers in Biology and Medicine · doi:10.1016/0010-4825(85)90014-9 · Cited by 85
BS∗: An admissible bidirectional staged heuristic search algorithm
Kwa, J. B. H. (1989) · Artificial Intelligence · doi:10.1016/0004-3702(89)90069-6 · Cited by 82
Global optimization for satisfiability (SAT) problem
Gu, J. (1994) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.334864 · Cited by 84
Baruwa, O. T., & Piera, M. À. (2015) · International Journal of Production Research · doi:10.1080/00207543.2015.1087656 · Cited by 84
“A∗ (Nilsson 1980) is the commonly used baseline heuristic search method due to its completeness and optimality guarantee.”
MethodSAT-based planning in complex domains: Concurrency, constraints and nondeterminism
Castellini, C., Giunchiglia, E., & Tacchella, A. (2003) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00375-2 · Cited by 84
“In GPT, search is based on the A∗ algorithm [41], and each belief state is stored separately.”
MethodBibel, W. (1983) · Communications of the ACM · doi:10.1145/182.183 · Cited by 84
Space Discretization for Efficient Human Navigation
Bandi, S., & Thalmann, D. (1998) · Computer Graphics Forum · doi:10.1111/1467-8659.00267 · Cited by 83
“* (read “A-star”) search method [ Nil80 ]. The node representing the starting cell has zero cost.”
MethodIdentification of all steady states in large networks by logical analysis
Devloo, V. (2003) · Bulletin of Mathematical Biology · doi:10.1016/s0092-8240(03)00061-2 · Cited by 83
“5.4.1. Backtracking. Backtracking ( Nilsson, 1980 )i s am ethod of solving constraint satisfaction problems by incrementally extending a partial solution that specifies consistent values for some of the variables, towards a complete solution, by repeatedly choosing a value for another variable consistent with the valu…”
BackgroundInfluentialGO‐MOKU SOLVED BY NEW SEARCH TECHNIQUES
Alus, L. V., van den Herik, H. J., & Huntjens, M. P. H. (1996) · Computational Intelligence · doi:10.1111/j.1467-8640.1996.tb00250.x · Cited by 83
“This means that the threat-space search procedure seen as a heuristic evaluation procedure is admissible (Nilsson 1980).”
MethodAgent-based distributed architecture for mobile robot control
Posadas-Yagüe, J.-L., Poza-Luján, J.-L., Simó, J. E., Benet, G., & Blanes, F. (2007) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2007.07.008 · Cited by 83
“This behaviour is known as deliberative behaviour (Nilsson, 1980).”
BackgroundGarcia-Molina, Lipton, & Valdés (1984) · IEEE Transactions on Computers · doi:10.1109/tc.1984.1676454 · Cited by 83
Resolution vs. cutting plane solution of inference problems: Some computational experience
Hooker, J. (1988) · Operations Research Letters · doi:10.1016/0167-6377(88)90044-2 · Cited by 83
A Profile of Drivers' Map-Reading Abilities
Streeter, L. A., & Vitello, D. (1986) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/001872088602800210 · Cited by 83
Representational and advisory guidance for students learning scientific inquiry
Suthers, D., Connelly, J., Lesgold, A. M., Paolucci, M., Toth, E. E., Toth, J., & Weiner, A. W. (2001) · ScholarSpace (University of Hawaii at Manoa) · Cited by 81
Evaluation of a method for controlling molecular scaffold diversity in de novo ligand design
Todorov, N., & Dean, P. M. (1997) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008042711516 · Cited by 81
“For example, branch-and-bound and A* algorithms [ 29 ] have been successful for conformational optimization, genetic algorithms [30] have been tailored to a number of drug design problems, and simulated annealing [31] has been used for molecular similarity and atom assignment problems.”
BackgroundResourceful systems for fault tolerance, reliability, and safety
Abbott, R. (1990) · ACM Computing Surveys · doi:10.1145/78949.78951 · Cited by 81
User modeling in intelligent information retrieval
Brajnik, G., Guida, G., & Tasso, C. (1987) · Information Processing & Management · doi:10.1016/0306-4573(87)90020-3 · Cited by 81
Bonet, B., & Geffner, H. (2006) · Cited by 80
Developing scheduling systems for Daewoo Shipbuilding: DAS project
Lee, J. K., Lee, J. K., Lee, K. J., et al. (1997) · European Journal of Operational Research · doi:10.1016/s0377-2217(96)00205-6 · Cited by 80
“The graph search expands nodes, and selects a proper node possibly using an evaluation function (Nilsson, 1980).”
MethodGeneration of free gait-a graph search approach
Pal, P. K., & Jayarajan, K. (1991) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.88139 · Cited by 80
Proof procedure and answer extraction in Petri net model of logic programs
Peterka, G., & Murata, T. (1989) · IEEE Transactions on Software Engineering · doi:10.1109/32.21746 · Cited by 80
Optimal and near-optimal test sequencing algorithms with realistic test models
Raghavan, V., Shakeri, M., & Pattipati, K. R. (1999) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/3468.736357 · Cited by 79
Understanding Action and Sensing by Designing Action-Based Sensors
Erdmann, M. A. (1995) · The International Journal of Robotics Research · doi:10.1177/027836499501400506 · Cited by 79
A model for deliberation, action, and introspection
Doyle, J. (1981) · ACM SIGART Bulletin · doi:10.1145/1056748.1056749 · Cited by 85
Harnessing Artificial Intelligence for Innovation in Education
Tan, S. (2023) · Springer eBooks · doi:10.1007/978-981-19-9201-8_8 · Cited by 101
A novel framework for automatic sorting of postal documents with multi-script address blocks
Basu, S., Das, N., Sarkar, R., et al. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.05.018 · Cited by 78
Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
Dechter, R. (2013) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00529ed1v01y201308aim023 · Cited by 77
FMS scheduling using Petri nets and heuristic search
Lee, D. Y., & DiCesare, F. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1992.220208 · Cited by 77
Reinforcement learning for the adaptive control of perception and action
Whitehead, S. D. (1992) · UR Research (University of Rochester) · Cited by 77
Refinement Planning as a Unifying Framework for Plan Synthesis
Subbarao Kambhampati, B. (1997) · AI Magazine · doi:10.21918/aimag.v18i2.1295 · Cited by 76
Sharma, B., Obaidat, M. S., Sharma, V., & Hsiao, K. (2020) · International Journal of Communication Systems · doi:10.1002/dac.4628 · Cited by 75
“Based on cost, A* algorithm is used for finding the path from the starting point to the ending point.(62,63) Some algorithms like ACO algorithms, Floyd algorithm, and Dijkstra algorithm are discussed for routing planning.”
MethodAutomated Route Generation for Avoiding Deterministic Weather in Transition Airspace
Krozel, J. A., Penny, S., Prete, J., et al. (2006) · Journal of Guidance Control and Dynamics · doi:10.2514/1.22970 · Cited by 75
Control strategies in HTN planning: theory versus practice
Nau, D. S., Smith, S. J., & Erol, K. (1998) · Cited by 75
Metagraphs: A Tool for Modeling Decision Support Systems
Basu, A., & Blanning, R. W. (1994) · Management Science · doi:10.1287/mnsc.40.12.1579 · Cited by 75
Search Algorithms Under Different Kinds of Heuristics—A Comparative Study
Bagchi, A., & Mahanti, A. (1983) · Journal of the ACM · doi:10.1145/322358.322359 · Cited by 75
Policy-Gradient Algorithms for Partially Observable Markov Decision Processes
Aberdeen, D. (2003) · ANU Open Research (Australian National University) · doi:10.25911/5d7a2b73cbb88 · Cited by 74
Weighted A ∗ search – unifying view and application
Ebendt, R., & Drechsler, R. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2009.06.004 · Cited by 83
“The more recent versions HSPr and HSP2 solve STRIPS planning problems by use of weighted A∗ , and they are both able to search backward from the goal to the initial state (known as “regression search” [69,85]).”
MethodHybrid Methods in Pattern Recognition
Horst Bunke, A. (1987) · Springer eBooks · doi:10.1007/978-3-642-83069-3_29 · Cited by 73
Pruning Improves Heuristic Search for Cost-Sensitive Learning
Zubek, V. B., & Dietterich, T. G. (2002) · Cited by 73
Constrained minimum vertex cover in bipartite graphs: complexity and parameterized algorithms
Chen, J., & Kanj, I. A. (2003) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2003.09.003 · Cited by 73
“In order to solve the min-cvcb problem, Hasan and Liu [10] introduced the concept of critical sets to develop a branch-and-bound algorithm solving the min-cvcb problem, based on the A algorithm [20].”
MethodAccelerating Partial-Order Planners: Some Techniques for Effective Search Control and Pruning
Gerevini, A., & Schubert, L. K. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.316 · Cited by 72
“First, in order for A*to guarantee discovery of an optimal plan (i.e., the \admissibility" condition), h(p) shouldnot overestimate the remaining solution cost (Nilsson, 1980).”
BackgroundMethodCoordinaton of Action and Perception in a Surveillance Robot
Crowley, J. L. (1987) · IEEE Expert · doi:10.1109/mex.1987.5006532 · Cited by 73
“This search procedure is based on a version of the GraphSearch algorithm described in [Nilsson 80].”
MethodMachine learning algorithms, applications, and practices in data science
Yeturu, K. (2020) · Handbook of statistics · doi:10.1016/bs.host.2020.01.002 · Cited by 83
Navigating a Mobile Robot by a Traversability Field Histogram
Ye, C. (2007) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2006.883870 · Cited by 72
“A possible remedy to this problem is to evoke a global path-planning method such as A∗ [27] to generate some via points to get the robot out of the trap.”
MethodRoutines, Hierarchies of Problems, Procedural Behaviour: Some Evidence from Experiments
Egidi, M. (1995) · Unitn Eprints Research (Università Degli Studi di Trento) · Cited by 71
ARTIFICIAL INTELLIGENCE IN GEOGRAPHY: CONJECTURES ON THE SHAPE OF THINGS TO COME
Couclelis, H. (1986) · The Professional Geographer · doi:10.1111/j.0033-0124.1986.00001.x · Cited by 70
The generalized A* architecture
Felzenszwalb, P. F., & Mcallester, D. (2007) · Cited by 70
A structure-oriented approach to assembly sequence planning
Chakrabarty, S., & Wolter, J. (1997) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.554344 · Cited by 70
“To select the best parent plan with regard to the installed criteria, a simple best- rst search is done[16, 17].”
MethodComputational Complexity, Protein Structure Prediction, and the Levinthal Paradox
Ngo, J., Marks, J., & Karplus, M. (1994) · Birkhäuser Boston eBooks · doi:10.1007/978-1-4684-6831-1_14 · Cited by 70
de Mello, L. S. H., & Sanderson, A. C. (1991) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.97874 · Cited by 70
“1592 GRAPHSEARCH presented by Nilsson[ 8 ]. Instead of storing partial solutions, each node is associated with the value of the best solution from that node and with a pointer to the and-arc leading to that solution.”
BackgroundMethodInfluentialAn actor dependency model of organizational work
Yu, E. S. K., & Mylopoulos, J. (1993) · doi:10.1145/168555.168584 · Cited by 70
“linked to goals in a goal graph [ 28 ] (Figure 2). Each”
BackgroundAn algebraic approach to feature interactions
Karinthi, R. R., & Nau, D. S. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.126807 · Cited by 70
Rule-based optimization and query processing in an extensible geometric database system
Becker, L., & Güting, R. H. (1992) · ACM Transactions on Database Systems · doi:10.1145/128903.128905 · Cited by 70
RAO*: An Algorithm for Chance-Constrained POMDP's
Santana, P. R. Q. E. A., Thiébaux, S., & Williams, B. C. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10423 · Cited by 69
“Similar to AO∗ (Nilsson 1982), RAO∗ guides the search towards promising policies w.”
BackgroundMedical Image Segmentation: Methods and Applications in Functional Imaging
Wong, K. P. (2007) · Springer eBooks · doi:10.1007/0-306-48606-7_3 · Cited by 69
Solving #SAT and Bayesian inference with backtracking search
Bacchus, F., Dalmao, S., & Pitassi, T. (2009) · Cited by 69
Computational experience with an interior point algorithm on the satisfiability problem
Kamath, A. P., Karmarkar, N. K., Ramakrishnan, K. G., et al. (1990) · Annals of Operations Research · doi:10.1007/bf02283686 · Cited by 69
“Resolution is a widely used method for inference problems in propositional calculus [ 19 ].”
MethodArtificial Intelligence in Agriculture
Zha, J. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1693/1/012058 · Cited by 68
Decision Support and Expert Systems in Auditing: A Review and Research Directions
Abdolmohammadi, M. J. (1987) · Accounting and Business Research · doi:10.1080/00014788.1987.9729795 · Cited by 68
Application of goal tree-success tree model as the knowledge-base of operator advisory systems
Kim, I. S., & Modarres, M. (1987) · Nuclear Engineering and Design · doi:10.1016/0029-5493(87)90304-9 · Cited by 68
Fast Graph-Search Algorithms for General-Aviation Flight Trajectory Generation
Rippel, E., Bar-Gill, A., & Shimkin, N. (2005) · Journal of Guidance Control and Dynamics · doi:10.2514/1.7370 · Cited by 67
“The major factor that slows down the Dijkstra algorithm is the large state space that needs to be explored.”
BackgroundABTWEAK: Abstracting a Non-Linear Least Commitment Planner
Yang, Q., & Tenenberg, J. (1990) · Cited by 67
Machine discovery of effective admissible heuristics
Prieditis, A. E. (1993) · Machine Learning · doi:10.1007/bf00993063 · Cited by 67
“Since accuracy determines pruning power ( Nilsson, 1980 ), generally the more abstract a problem the lower the pruning power of the resulting heuristic.”
BackgroundResultInfluentialSecure XML Publishing without Information Leakage in the Presence of Data Inference
Yang, X., & Li, C. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50012-7 · Cited by 66
Information Filtering: Selection Mechanisms in Learning Systems
Markovitch, S., & Scott, P. D. (1993) · Machine Learning · doi:10.1023/a:1022614725002 · Cited by 72
Mapping explanation-based generalization onto Soar
Rosenbloom, P. S., & Laird, J. E. (1986) · Cited by 66
Procedural control in production systems
Georgeff, M. (1982) · Artificial Intelligence · doi:10.1016/0004-3702(82)90039-x · Cited by 66
Rekursive Codes mit der Plotkin-Konstruktion und ihre Decodierung
Stolte, N. (2008) · elib (German Aerospace Center) · doi:10.26083/tuprints-00000183 · Cited by 66
Spoken language understanding: a survey
Mori, R. D. (2007) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2007.4430139 · Cited by 69
“The above frame can be derived [145], after skolemization from the following logic formula:”
BackgroundAnytime, Dynamic Planning in High-dimensional Search Spaces
Ferguson, D., & Stentz, A. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.363166 · Cited by 65
“Two very common such algorithms for planning in known environments are Dijkstra’s search [1] and A* [2].”
BackgroundPruning Duplicate Nodes in Depth-First Search
Taylor, L. A., & Korf, R. E. (1993) · Cited by 68
Generating Plans in Linear Logic : I. Actions as proofs
Vauzeilles, J., Masseron, M., & Tollu, C. (1993) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 65
Knowledge management support of decision making
Holsapple, C. W. (2001) · Decision Support Systems · doi:10.1016/s0167-9236(00)00115-9 · Cited by 65
A Knowledge Based System for Analysis of Gated Blood Pool Studies
Niemann, H., Bunke, H., Hofmann, I., et al. (1985) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1985.4767655 · Cited by 65
“The basic idea is to use optimal search techniques [4], [5] .”
Kulagin, R., Beygelzimer, Y., Estrin, Y., Schumilin, A., & Gumbsch, P. (2020) · Advanced Engineering Materials · doi:10.1002/adem.202001069 · Cited by 63
“Ef fi cient methods aimed at overcoming both aspects of the curse of dimensionality have been developed in the context of ML. [20] The ML approach consists in developing a model for Big Data, whose parameters are identi fi ed based on statistical analysis of a limited selection of its realizations.”
MethodNavigational path planning for a vision-based mobile robot
Arkin, R. C. (1989) · Robotica · doi:10.1017/s0263574700005038 · Cited by 62
“…can be used to affect the quality of a path Parodi 21 used a weighted cost function and dynamic programming techniques to search through the solution space of a regular grid and arrive at the best path The A* search algorithm 22 ' 23 is used in AuRA with heuristics that guarantee optimality.”
Method
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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