Journal of the ACM (JACM)
Utility of pathmax in partial order heuristic research
Information Processing Letters
On the robust shortest path problem
Computers and Operations Research
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Preference-based search and multi-criteria optimization
Eighteenth national conference on Artificial intelligence
On preference-based search in state space graphs
Eighteenth national conference on Artificial intelligence
Dynamic Programming
A new approach to multiobjective A* search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An axiomatic approach to robustness in search problems with multiple scenarios
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Frontier Search for Bicriterion Shortest Path Problems
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
On Finding Compromise Solutions in Multiobjective Markov Decision Processes
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Decision making with multiple objectives using GAI networks
Artificial Intelligence
K*: A heuristic search algorithm for finding the k shortest paths
Artificial Intelligence
Multiobjective heuristic search in road maps
Expert Systems with Applications: An International Journal
Data mobility in peer-to-peer systems to improve robustness
AP2PC'08 Proceedings of the 7th international conference on Agents and Peer-to-Peer Computing
A case of pathology in multiobjective heuristic search
Journal of Artificial Intelligence Research
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The aim of this paper is to introduce and solve new search problems in multiobjective state space graphs. Although most of the studies concentrate on the determination of the entire set of Pareto optimal solution paths, the size of which can be, in worst case, exponential in the number of nodes, we consider here more specialized problems where the search is focused on Pareto solutions achieving a well-balanced compromise between the conflicting objectives. After introducing a formal definition of the compromise search problem, we discuss computational issues and the complexity of the problem. Then, we introduce two algorithms to find the best compromise solution-paths in a state space graph. Finally, we report various numerical tests showing that, as far as compromise search is concerned, both algorithms are very efficient (compared to MOA*) but they present contrasted advantages discussed in the conclusion.