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.
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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
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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
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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
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Ramírez, M., & Geffner, H. (2009) · Cited by 244
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Albano, A., & Sapuppo, G. (1980) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1980.4308483 · Cited by 217
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Wallace, M. G. (1996) · Constraints · doi:10.1007/bf00143881 · Cited by 217
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Sandholm, T., Suri, S., Gilpin, A., & Levine, D. (2005) · Management Science · doi:10.1287/mnsc.1040.0336 · Cited by 216
Öztürk, Ü., Akdağ, M., & Ayabakan, T. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.111010 · Cited by 217
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Sezer, V., & Gökaşan, M. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.05.021 · Cited by 209
Boroujeni, S. P. H., Razi, A., Khoshdel, S., Afghah, F., Coen, J. L., O’Neill, L., Fulé, P. Z., Watts, A. C., Kokolakis, N.-M. T., & Vamvoudakis, K. G. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102369 · Cited by 228
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OPUS: An Efficient Admissible Algorithm for Unordered Search
Webb, G. I. (1995) · Journal of Artificial Intelligence Research · doi:10.1613/jair.227 · Cited by 207
Promises and Challenges of Big Data Computing in Health Sciences
Huang, T., Lan, L., Fang, X., An, P., Min, J., & Wang, F. (2015) · Big Data Research · doi:10.1016/j.bdr.2015.02.002 · Cited by 211
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Depth-first iterative-deepening: An optimal admissible tree search
(1986) · Artificial Intelligence · doi:10.1016/0004-3702(86)90035-4 · Cited by 206
Debnath, D., Vanegas, F., Sandino, J., Hawary, A. F., & Gonzalez, F. (2024) · Remote Sensing · doi:10.3390/rs16214019 · Cited by 228
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Xiao, Z., Fu, X., Zhang, L., & Goh, R. S. M. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2908191 · Cited by 205
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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
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