The DOI matches its Crossref record; 5 of the 7 references it cites were found in a registry or an index.
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Abstract
Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Finally, we chose to use A* (Hart, Nilsson, and Raphael 1968) as part of the approach to creating ERCs over other popular algorithms like Monte Carlo Tree Search (Chaslot et al. 2006) due to its more consistent and reproducible behavior.”
From Expressive Response Curves: Testing Expressive Game Feel with A* (2023) · cites this paper as Hart, Nilsson, and Raphael 1968
In the citing paper
“A* uses a breadth-first approach to visiting graph nodes and a priority queue based on cost and heuristic values to guide the search towards a specific goal (Hart, Nilsson, and Raphael 1968).”
From Expressive Response Curves: Testing Expressive Game Feel with A* (2023) · cites this paper as Hart, Nilsson, and Raphael 1968
In the citing paper
“Unlike breadth-first search, A* is not guaranteed to visit every node in a graph, nor is it guaranteed to find an optimal path without an admissible heuristic, a heuristic function which never over-estimates the distance to the goal (Hart, Nilsson, and Raphael 1968).”
From Expressive Response Curves: Testing Expressive Game Feel with A* (2023) · cites this paper as Hart, Nilsson, and Raphael 1968
Citations
Cites
Freimer, M. L., Bellman, R., & Dreyfus, S. E. (1964) · Journal of the American Statistical Association · doi:10.2307/2282884 · Cited by 2,173
Bellman, R., & Dreyfus, S. E. (1962) · Princeton University Press eBooks · doi:10.1515/9781400874651 · Cited by 2,027
The shortest path through a maze
Moore, E. (1959) · Medical Entomology and Zoology · Cited by 756
Solutions of the Shortest-Route Problem—A Review
Pollack, M., & Wiebenson, W. (1960) · Operations Research · doi:10.1287/opre.8.2.224 · Cited by 114
Finite Graphs and Networks, An Introduction with Applications.
Harary, F., Busacker, R. G., & Saaty, T. L. (1967) · American Mathematical Monthly · doi:10.2307/2314099 · Cited by 104
Flagged first, then by citations
2 of the 7 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Busacker, 1965, 3
- Gelernter, 1963, Realization of a geometry-theorem proving machine
- Samuel, 1963, Some studies in machine learning using the game of checkers
- 1959, The Annals of the Computation Laboratory of Harvard University, 30
- Feigenbaum, 1963, Computers and Thought
Cited by
Sorted by citations
Items 501–600 of 1,018
Global Path Planning of Unmanned Surface Vehicle Based on Improved A-Star Algorithm
Zhang, H., Tao, Y., & Zhu, W. (2023) · Sensors · doi:10.3390/s23146647 · Cited by 98
A shortest path algorithm with novel heuristics for dynamic transportation networks
Huang, B., Wu, Q., & Zhan, F. B. (2007) · International Journal of Geographical Information Systems · doi:10.1080/13658810601079759 · Cited by 98
Heuristics for model checking Java programs
Groce, A., & Visser, W. (2004) · International Journal on Software Tools for Technology Transfer · doi:10.1007/s10009-003-0130-9 · Cited by 98
Moving Agent Pedestrians Through Space and Time
Torrens, P. M. (2011) · Annals of the Association of American Geographers · doi:10.1080/00045608.2011.595658 · Cited by 98
Optimization of Crowd Evacuation With Leaders in Urban Rail Transit Stations
Zhou, M., Dong, H., Zhao, Y., Ioannou, P., & Wang, F. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2886415 · Cited by 97
Pressman, A., Liu, Z., Janzen, E., Blanco, C., Müller, U. F., Joyce, G. F., Pascal, R., & Chen, I. A. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.8b13298 · Cited by 100
Density gradient-RRT: An improved rapidly exploring random tree algorithm for UAV path planning
Huang, T., Fan, K., & Sun, W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124121 · Cited by 97
Path Planning and Motion Coordination in Multiple Mobile Robot Teams
Parker, L. E. (2009) · Cited by 97
BidirectionalA* search on time‐dependent road networks
Nannicini, G., Delling, D., Schultes, D., & Liberti, L. (2011) · Networks · doi:10.1002/net.20438 · Cited by 97
Engineering Fast Route Planning Algorithms
Sanders, P., & Schultes, D. (2007) · Springer eBooks · doi:10.1007/978-3-540-72845-0_2 · Cited by 97
Artificial intelligence and brain theory: Unities and diversities
Arbib, M. A. (1975) · Annals of Biomedical Engineering · doi:10.1007/bf02390972 · Cited by 98
Monitoring path nearest neighbor in road networks
Chen, Z., Shen, H. T., Zhou, X., & Yu, J. X. (2009) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1559845.1559907 · Cited by 96
A Distributed Algorithm for the Replica Placement Problem
Zaman, S., & Grosu, D. (2011) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2011.27 · Cited by 96
Shakey: From Conception to History
Kuipers, B. J., Feigenbaum, E. A., Hart, P. E., & Nilsson, N. J. (2017) · AI Magazine · doi:10.1609/aimag.v38i1.2716 · Cited by 96
Self-citationShip predictive collision avoidance method based on an improved beetle antennae search algorithm
Xie, S., Chu, X., Zheng, M. Y., & Liu, C. (2019) · Ocean Engineering · doi:10.1016/j.oceaneng.2019.106542 · Cited by 96
Patel, U., Kumar, N. K. S., Sathyamoorthy, A. J., & Manocha, D. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561462 · Cited by 101
Dynamic Path Planning for Forklift AGV Based on Smoothing A* and Improved DWA Hybrid Algorithm
Wu, B., Chi, X., Zhao, C., Zhang, W., Lu, Y., & Jiang, D. (2022) · Sensors · doi:10.3390/s22187079 · Cited by 96
A new approach to multiobjective A* search
la Cruz, J. L. P. ́. D., & Mandow, L. (2005) · International Joint Conference on Artificial Intelligence · Cited by 96
Casalino, G., Turetta, A., & Simetti, E. (2009) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceanse.2009.5278104 · Cited by 96
Design and field experimentation of a prototype Lunar prospector
Wettergreen, D. S., Moreland, S. J., Skonieczny, K., Jonak, D., Kohanbash, D., & Teza, J. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910370217 · Cited by 96
Multi-hop driver-parcel matching problem with time windows
Chen, W., Mes, M., & Schutten, J. M. J. (2017) · Flexible Services and Manufacturing Journal · doi:10.1007/s10696-016-9273-3 · Cited by 96
Reliable Shortest Path Problems in Stochastic Time-Dependent Networks
Chen, B. Y., Lam, W. H. K., Sumalee, A., Li, Q., & Tam, M. L. (2013) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2013.806851 · Cited by 95
Variational Curve Skeletons Using Gradient Vector Flow
Hassouna, M. S., & Farag, A. A. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.271 · Cited by 95
Radiation search operations using scene understanding with autonomous UAV and UGV
Christie, G. A., Shoemaker, A., Kochersberger, K. B., Tokekar, P., McLean, L., & Leonessa, A. (2017) · Journal of Field Robotics · doi:10.1002/rob.21723 · Cited by 95
Csaszar, F. A., & Steinberger, T. (2021) · Academy of Management Annals · doi:10.5465/annals.2020.0192 · Cited by 109
Parallel RRT-based path planning for selective disassembly planning
Aguinaga, I., Borro, D., & Matey, L. (2007) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-007-0930-2 · Cited by 95
Computing Many-to-Many Shortest Paths Using Highway Hierarchies
Knopp, S., Sanders, P., Schultes, D., Schulz, F., & Wagner, D. (2007) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611972870.4 · Cited by 95
Finding Optimal Solutions to the Twenty-Four Puzzle
Korf, R. E., & Taylor, L. A. (1996) · Cited by 95
Ishida, T., & Korf, R. E. (1992) · Cited by 95
A Constraint-Aware Heuristic Path Planner for Finding Energy-Efficient Paths on Uneven Terrains
Ganganath, N., Cheng, C., & Tse, C. K. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2413355 · Cited by 94
Lovreglio, R., Ronchi, E., Maragkos, G., Beji, T., & Merci, B. (2016) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2016.06.015 · Cited by 94
Quantum compiling by deep reinforcement learning
Moro, L. D., Paris, M. G. A., Restelli, M., & Prati, E. (2021) · Communications Physics · doi:10.1038/s42005-021-00684-3 · Cited by 115
Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star
Zhang, J., Wu, J., Shen, X., & Li, Y. (2021) · International Journal of Advanced Robotic Systems · doi:10.1177/17298814211042730 · Cited by 94
Global Shipping Emissions from a Well-to-Wake Perspective: The MariTEAM Model
Kramel, D., Muri, H., Kim, Y.-R., Lonka, R., Nielsen, J. B., Ringvold, A. L., Bouman, E. A., Steen, S., & Strømman, A. H. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c03937 · Cited by 94
Geographic proximity to treatment for early stage breast cancer and likelihood of mastectomy
Boscoe, F. P., Johnson, C. J., Henry, K., Goldberg, D. W., Shahabi, K., Elkin, E. B., Ballas, L., & Cockburn, M. (2011) · The Breast · doi:10.1016/j.breast.2011.02.020 · Cited by 97
Beam search algorithms for multilabel learning
Kumar, A., Vembu, S., Menon, A., & Elkan, C. (2013) · Machine-mediated learning · doi:10.1007/s10994-013-5371-6 · Cited by 97
A new basis for state-space learning systems and a successful implementation
Rendell, L. A. (1983) · Artificial Intelligence · doi:10.1016/0004-3702(83)90002-4 · Cited by 94
Complete and Interpretable Conformance Checking of Business Processes
García‐Bañuelos, L., van Beest, N., Dumas, M., Rosa, M. L., & Mertens, W. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2668418 · Cited by 93
Optimal path planning in cluttered environment using RRT*-AB
Noreen, I., Khan, A., Ryu, H., Doh, N. L., & Habib, Z. (2017) · Intelligent Service Robotics · doi:10.1007/s11370-017-0236-7 · Cited by 93
Autonomous Navigation for eVTOL: Review and Future Perspectives
Wei, H., Lou, B., Zhang, Z., Liang, B., Wang, F., & Lv, C. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3352613 · Cited by 93
An experimental study on hub labeling based shortest path algorithms
Li, Y., Leong Hou, U., Yiu, M. L., & Kou, N. M. (2017) · Proceedings of the VLDB Endowment · doi:10.1145/3186728.3164141 · Cited by 92
Path following and obstacle avoidance for an autonomous UAV using a depth camera
Iacono, M., & Sgorbissa, A. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.04.005 · Cited by 92
MAVBench: Micro Aerial Vehicle Benchmarking
Boroujerdian, B., Genc, H., Krishnan, S., Cui, W., Faust, A., & Reddi, V. J. (2018) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro.2018.00077 · Cited by 92
Long-Distance Path Planning for Unmanned Surface Vehicles in Complex Marine Environment
Shah, B. C., & Gupta, S. K. (2019) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2019.2909508 · Cited by 92
Multi-task Learning with Attention for End-to-end Autonomous Driving
Ishihara, K., Kanervisto, A., Miura, J., & Hautamäki, V. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00325 · Cited by 96
Ship route planning in Arctic Ocean based on POLARIS
Lee, H.-W., Roh, M.-I., & Kim, K.-S. (2021) · Ocean Engineering · doi:10.1016/j.oceaneng.2021.109297 · Cited by 92
Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors
Ren, Y., Zhu, F., Liu, W., Wang, Z., Lin, Y., Gao, F., & Zhang, F. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981518 · Cited by 92
Online Computation of Fastest Path in Time-Dependent Spatial Networks
Demiryurek, U., Banaei‐Kashani, F., Shahabi, C., & Ranganathan, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22922-0_7 · Cited by 92
A framed-quadtree approach for determining Euclidean shortest paths in a 2-D environment
Chen, D. Z., Szczerba, R. J., & Uhran, J. J. (1997) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.631228 · Cited by 92
A new approach based on Bezier curves to solve path planning problems for mobile robots
Duraklı, Z., & Nabiyev, V. V. (2021) · Journal of Computational Science · doi:10.1016/j.jocs.2021.101540 · Cited by 91
Fan, J., Chen, X., Wang, Y., & Chen, X. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105182 · Cited by 91
A heuristic search algorithm with modifiable estimate
Mérő, L. (1984) · Artificial Intelligence · doi:10.1016/0004-3702(84)90003-1 · Cited by 91
A generalization of Dijkstra's shortest path algorithm with applications to VLSI routing
Peyer, S., Rautenbach, D., & Vygen, J. (2009) · Journal of Discrete Algorithms · doi:10.1016/j.jda.2007.08.003 · Cited by 91
Breast Cancer Stage at Diagnosis: Is Travel Time Important?
Henry, K., Boscoe, F. P., Johnson, C. J., Goldberg, D. W., Sherman, R., & Cockburn, M. (2011) · Journal of Community Health · doi:10.1007/s10900-011-9392-4 · Cited by 91
Tunable and stable real-time trajectory planning for urban autonomous driving
Gu, T., Atwood, J., Dong, C., Dolan, J. M., & Lee, J.-W. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353382 · Cited by 91
Artificial Intelligence Search Algorithms
Korf, R. E. (2009) · Chapman & Hall/CRC applied algorithms and data structures series · doi:10.1201/9781584888215-c22 · Cited by 91
Multiple representations of space underlying behavior
Lieblich, I., & Arbib, M. A. (1982) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00013959 · Cited by 91
On the optimal control of discrete event systems
Passino, K. M., & Antsaklis, P. J. (2003) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.1989.70672 · Cited by 91
The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data
Hunter, T., Abbeel, P., & Bayen, A. M. (2013) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-36279-8_36 · Cited by 90
Sánchez-López, J. L., Wang, M., Olivares-Méndez, M. A., Molina, M., & Voos, H. (2018) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-018-0809-5 · Cited by 90
Xu, L., Feng, C., Kamat, V. R., & Menassa, C. C. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.04.011 · Cited by 90
Topological Quantum Compiling with Reinforcement Learning
Zhang, Y., Zheng, P.-L., Zhang, Y., & Deng, D.-L. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.170501 · Cited by 106
On dynamic pickup and delivery vehicle routing with several time windows and waiting times
Fabri, A., & Recht, P. (2005) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2005.04.002 · Cited by 90
Towards Optimal Topology Aware Quantum Circuit Synthesis
Davis, M. G., Smith, E., Tudor, A., Sen, K., Siddiqi, I. A., & Iancu, C. (2020) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce49297.2020.00036 · Cited by 89
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding
Okumura, K. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i10.26377 · Cited by 89
Research on global path planning algorithm for mobile robots based on improved A*
Xu, X., Zeng, J., Zhao, Y., & Lü, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122922 · Cited by 89
Mining correlation between locations using human location history
Zheng, Y., Zhang, L., Xie, X., & Ma, W. (2009) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1653771.1653847 · Cited by 89
Liu, C., & Kroll, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29350-4_56 · Cited by 89
Real‐time density‐based crowd simulation
van Toll, W., Cook, A. F., & Gerærts, R. (2012) · Computer Animation and Virtual Worlds · doi:10.1002/cav.1424 · Cited by 88
Khan, A., Noreen, I., & Habib, Z. (2017) · Journal of information science and engineering · Cited by 88
Wu, X., Xu, L., Zhen, R., & Wu, X. (2020) · IEEE Access · doi:10.1109/access.2020.2990153 · Cited by 88
Energy-Efficient Path Planning of Reconfigurable Robots in Complex Environments
Kyaw, P. T., Le, A. V., Veerajagadheswar, P., Elara, M. R., Thu, T. T., Nhan, N. H. K., Phan, V.-D., & Minh, B. V. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3147408 · Cited by 88
Han, S., Wang, L., Wang, Y., & He, H. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.111655 · Cited by 88
Cai, J., Du, A., Liang, X., & Li, S. (2022) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0001056 · Cited by 88
On-line map building and navigation for autonomous mobile robots
Oriolo, G., Vendittelli, M., & Ulivi, G. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1995.525695 · Cited by 88
Optimally Solving Dec-POMDPs as Continuous-State MDPs
Dibangoye, J., Amato, C., Buffet, O., & Charpillet, F. (2016) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4623 · Cited by 88
An admissible and optimal algorithm for searching AND/OR graphs
Chang, C. L., & Slagle, J. R. (1971) · Artificial Intelligence · doi:10.1016/0004-3702(71)90006-3 · Cited by 88
Discretized Constrained Attitude Pathfinding and Control for Satellites
Kjellberg, H. C., & Lightsey, E. G. (2013) · Journal of Guidance Control and Dynamics · doi:10.2514/1.60189 · Cited by 87
OpenSHS: Open Smart Home Simulator
Alshammari, N. O., Alshammari, T. A., Sedky, M., Champion, J., & Bauer, C. (2017) · Sensors · doi:10.3390/s17051003 · Cited by 87
Comparison between A* and RRT Algorithms for UAV Path Planning
Zammit, C., & Kampen, E.-J. V. (2018) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2018-1846 · Cited by 87
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
Ling, X., Wu, L., Wang, S., Ma, T., Xu, F., Liu, A. X., Wu, C., & Ji, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3102234 · Cited by 91
Zuo, L., Guo, Q., Xu, X., & Fu, H. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.09.092 · Cited by 87
Local Multiresolution Path Planning
Behnke, S. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25940-4_29 · Cited by 87
Programming Constraint Services
Schulte, C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45945-6 · Cited by 87
The neural basis of predictive pursuit
Yoo, S. B. M., Tu, J. C., Piantadosi, S. T., & Hayden, B. Y. (2020) · Nature Neuroscience · doi:10.1038/s41593-019-0561-6 · Cited by 86
Sánchez-Vaquerizo, J. A. (2021) · ISPRS Int. J. Geo Inf. · doi:10.3390/ijgi11010024 · Cited by 81
Strub, M. P., & Gammell, J. D. (2022) · The International Journal of Robotics Research · doi:10.1177/02783649211069572 · Cited by 86
Aslan, M. F., Durdu, A., & Sabancı, K. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110156 · Cited by 88
Microplanning with Communicative Intentions: The SPUD System
Stone, M., Doran, C., Webber, B., Bleam, T., & Palmer, M. S. (2003) · Computational Intelligence · doi:10.1046/j.0824-7935.2003.00221.x · Cited by 86
Multi-UAV path planning in obstacle rich environments using Rapidly-exploring Random Trees
Kothari, M., Postlethwaite, I., & Gu, D. (2009) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2009.5400108 · Cited by 86
Randomized Bidirectional B-Spline Parameterization Motion Planning
Elbanhawi, M., Simić, M., & Jazar, R. N. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2477355 · Cited by 86
The fluid dynamics applied to mobile robot motion: the stream field method
Keymeulen, D., & Decuyper, J.-C. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1994.351266 · Cited by 86
Techniques for Modeling Human Performance in Synthetic Environments: A Supplementary Review
Ritter, F. E., Shadbolt, N. R., Elliman, D. G., Young, R. M., Gobet, F. R., & Baxter, G. D. (2003) · Defense Technical Information Center (DTIC) · doi:10.21236/ada487721 · Cited by 86
RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots
Iocchi, L., Holz, D., Ruiz-del-Solar, J., Sugiura, K., & van der Zant, T. (2015) · Artificial Intelligence · doi:10.1016/j.artint.2015.08.002 · Cited by 86
Hallen, M. A., Keedy, D. A., & Donald, B. R. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24150 · Cited by 85
Coordinated Target Assignment and UAV Path Planning with Timing Constraints
Babel, L. (2018) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-018-0910-9 · Cited by 85
Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
Bai, Y., Ding, H., Gu, K., Sun, Y., & Wang, W. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5720 · Cited by 85
An efficient RRT cache method in dynamic environments for path planning
Yuan, C., Liu, G., Zhang, W., & Pan, X. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103595 · Cited by 85
Narazaki, Y., Hoskere, V., Chowdhary, G. V., & Spencer, B. F. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104214 · Cited by 85
Andreou, A., Mavromoustakis, C. X., Batalla, J. M., Markakis, E., Mastorakis, G. N., & Mumtaz, S. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3254604 · Cited by 85
Metadata
- Authors
- Hart, P., Nilsson, N., & Raphael, B.
- Affiliations
- SRI International (US)
- Venue
- IEEE Transactions on Systems Science and Cybernetics
- Published
- 1968
- Volume
- 4
- Issue
- 2
- Pages
- 100-107
- ISSN
- 0536-1567 (ISSN-L)
- OpenAlex
- W1969483458
- Cited by
- 12,738
- References
- 7
- Citations by year
- 2026: 965 · 2025: 1216 · 2024: 1078 · 2023: 893 · 2022: 855 · 2021: 939 · 2020: 707 · 2019: 610 · 2018: 584 · 2017: 493 · 2016: 471 · 2015: 456 · 2014: 437 · 2013: 466 · 2012: 364
- FWCI
- 7.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Science and Cybernetics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Keywords
- least-cost path, shortest path, graph search, heuristic search, search strategies, optimality, heuristic information
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 ›
Are you an author, or do you see a problem with this page? Request a review
