Journal of the ACM (JACM)
Utility of pathmax in partial order heuristic research
Information Processing Letters
On preference-based search in state space graphs
Eighteenth national conference on Artificial intelligence
Multiobjective heuristic state-space planning
Artificial Intelligence
Multicriteria Optimization
Search for Compromise Solutions in Multiobjective State Space Graphs
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Thinking Too Much: Pathology in Pathfinding
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
A new approach to multiobjective A* search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multiobjective A* search with consistent heuristics
Journal of the ACM (JACM)
When is it better not to look ahead?
Artificial Intelligence
A note on the complexity of some multiobjective A* search algorithms
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
An empirical comparison of some multiobjective graph search algorithms
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Multi-Objective Four-Dimensional Vehicle Motion Planning in Large Dynamic Environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This article considers the performance of the MOA* multiobjective search algorithm with heuristic information. It is shown that in certain cases blind search can be more efficient than perfectly informed search, in terms of both node and label expansions. A class of simple graph search problems is defined for which the number of nodes grows linearly with problem size and the number of nondominated labels grows quadratically. It is proved that for these problems the number of node expansions performed by blind MOA* grows linearly with problem size, while the number of such expansions performed with a perfectly informed heuristic grows quadratically. It is also proved that the number of label expansions grows quadratically in the blind case and cubically in the informed case.