Information and Computation
Theoretical Computer Science - Special issue on dynamic and on-line algorithms
Online computation and competitive analysis
Online computation and competitive analysis
Non-clairvoyant scheduling to minimize the average flow time on single and parallel machines
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
SIAM Journal on Computing
Scheduling jobs before shut-down
Nordic Journal of Computing
Online Parallel Heuristics and Robot Searching under the Competitive Framework
SWAT '02 Proceedings of the 8th Scandinavian Workshop on Algorithm Theory
Preemptive Scheduling in Overloaded Systems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Non-clairvoyant Scheduling for Minimizing Mean Slowdown
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Developments from a June 1996 seminar on Online algorithms: the state of the art
Scheduling search procedures: The wheel of fortune
Journal of Scheduling
Linear Programs for Hypotheses Selection in Probabilistic Inference Models
The Journal of Machine Learning Research
Ranking hypotheses to minimize the search cost in probabilistic inference models
Discrete Applied Mathematics
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We analyze preemptive on-line scheduling against randomized adversaries, with the goal to finish an unknown distinguished target job. Our motivation comes from clinical gene search projects, but the subject leads to general theoretical questions of independent interest, including some natural but unusual probabilistic models. We study problem versions with known and unknown processing times of jobs and target probabilities, and models where the on-line player gets some randomized extra information about the target. For some versions we get optimal competitive ratios, expressed in terms of given parameters of instances.