Principles of artificial intelligence
Principles of artificial intelligence
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
A general scheme for automatic generation of search heuristics from specification dependencies
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Bucket elimination for multiobjective optimization problems
Journal of Heuristics
AND/OR search spaces for graphical models
Artificial Intelligence
Multi-objective Russian Doll search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Taking advantage of stable sets of variables in constraint satisfaction problems
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
AND/OR branch-and-bound for graphical models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Russian doll search for solving constraint optimization problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Bounded decentralised coordination over multiple objectives
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Efficient approximation algorithms for multi-objective constraint optimization
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Interactive algorithm for multi-objective constraint optimization
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Multi-objective optimization is concerned with problems involving multiple measures of performance which should be optimized simultaneously. In this paper, we extend AND/OR Branch-and-Bound (AOBB), a well known search algorithm, from mono-objective to multi-objective optimization. The new algorithm MO-AOBB exploits efficiently the problem structure by traversing an AND/OR search tree and uses static and dynamic mini-bucket heuristics to guide the search. We show that MO-AOBB improves dramatically over the traditional OR search approach, on various benchmarks for multi-objective optimization.