Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Lower bounds in on-line geometric searching
Computational Geometry: Theory and Applications
Real-Time Problem-Solving with Contract Algorithms
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Scheduling contract algorithms on multiple processors
Eighteenth national conference on Artificial intelligence
Optimal Sequencing of Contract Algorithms
Annals of Mathematics and Artificial Intelligence
Optimal scheduling of contract algorithms for anytime problems
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Optimal scheduling of contract algorithms with soft deadlines
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Contract algorithms and robots on rays: unifying two scheduling problems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
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In this paper we address the problem of designing an interruptible system in a setting in which n problem instances, all equally important, must be solved. The system involves scheduling executions of contract algorithms (which offer a trade-off between allowable computation time and quality of the solution) in m identical parallel processors. When an interruption occurs, the system must report a solution to each of the n problem instances. The quality of this output is then compared to the best-possible algorithm that has foreknowledge of the interruption time and must, likewise, produce solutions to all n problem instances. This extends the well-studied setting in which only one problem instance is queried at interruption time. We propose a schedule which we prove is optimal for the case of a single processor. For multiple processors, we show that the quality of the schedule is within a small factor from optimal.