Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Introduction to algorithms
Journal of the ACM (JACM)
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Partial order bounding: a new approach to evaluation in game tree search
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Algebra and algorithms for QoS path computation and hop-by-hop routing in the internet
IEEE/ACM Transactions on Networking (TON)
Soft Constraint Logic Programming and Generalized Shortest Path Problems
Journal of Heuristics
Directed explicit-state model checking in the validation of communication protocols
International Journal on Software Tools for Technology Transfer (STTT)
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A new approach to multiobjective A* search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IEEE Network: The Magazine of Global Internetworking
Survey on Directed Model Checking
Model Checking and Artificial Intelligence
Action Planning for Directed Model Checking of Petri Nets
Electronic Notes in Theoretical Computer Science (ENTCS)
Heuristic search for the analysis of graph transition systems
ICGT'06 Proceedings of the Third international conference on Graph Transformations
Towards cost-aware service recovery
Proceedings of the 9th international ACM Sigsoft conference on Quality of software architectures
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Heuristic search is used to efficiently solve the single-node shortest path problem in weighted graphs. In practice, however, one is not only interested in finding a short path, but an optimal path, according to a certain cost notion. We propose an algebraic formalism that captures many cost notions, like typical Quality of Service attributes. We thus generalize A*, the popular heuristic search algorithm. for solving optimal-path problem. The paper provides an answer to a fundamental question for AI search, namely to which general notion of cost, heuristic search algorithms can be applied. We proof correctness of the algorithms and provide experimental results that validate the feasibility of the approach.