ABSTRACT
We propose a hybridization of heuristic search and the LPI algorithm. Our approach uses heuristic search to find paths to landmarks, and employs a small amount of landmark information to correct itself when the heuristic search deviates from the shortest path. The use of the heuristic allows lower memory usage than LPI, while the use of the landmarks permits the algorithm to operate effectively even with a poor heuristic. When the heuristic accuracy is very high, the algorithm tends towards greedy search; when the heuristic accuracy is low, the algorithm tends towards LPI. Experiments show that the memory usage of LPI can be reduced by more than half while preserving the accuracy of the solutions.
- Freeciv. www.freeciv.org.Google Scholar
- E. W. Dijkstra. A Note on Two Problems in Connexion with Graphs. Numerische Mathematik, pages 269--271, 1959.Google Scholar
- R. W. Floyd. Algorithm 97: Shortest path. Commun. ACM, 5(6):345, 1962. Google ScholarDigital Library
- A. V. Goldberg and C. Harrelson. Computing the Shortest Path: A* Search meets Graph Theory. In SODA '05: Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms, pages 156--165, 2005. Google ScholarDigital Library
- K. Grant and D. Mould. LPI: Approximating Shortest Paths using Landmarks. In Eighteenth European Conference on Artificial Intelligence - Workshop on AI and Games, 2008.Google Scholar
- P. E. Hart, N. J. Nilsson, and B. Raphael. A Formal Basis for the Heuristic Determination of Minimum Cost Paths in Graphs. IEEE Trans. Syst. Sci. and Cybernetics, SSC-4(2):100--107, 1968.Google Scholar
- D. B. Johnson. Efficient algorithms for shortest paths in sparse networks. J. ACM, 24(1):1--13, 1977. Google ScholarDigital Library
- D. Mould and M. Horsch. An Hierarchical Terrain Representation for Approximately Shortest Paths. In Proceedings of Ninth Pacific Rim International Conference on Artificial Intelligence (PRICAI), pages 104--113, 2004.Google ScholarDigital Library
- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Pearson Education, 2003. Google ScholarDigital Library
Index Terms
- Combining heuristic and landmark search for path planning
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