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dc.contributor.authorConitzer, V.en
dc.contributor.authorVidali, A.en
dc.date.accessioned2017-10-17T09:55:01Z
dc.date.available2017-10-17T09:55:01Z
dc.date.issued2014-06-21
dc.identifier.citationConitzer, V. and Vidalli, A. (2014) Mechanism Design for Scheduling with Uncertain Execution Time. AAAI Conference on Artificial Intelligence, North America,en
dc.identifier.urihttps://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8162
dc.identifier.urihttp://hdl.handle.net/2086/14632
dc.description.abstractWe study the problem where a task (or multiple unrelated tasks) must be executed, there are multiple machines/agents that can potentially perform the task, and our objective is to minimize the expected sum of the agents' processing times. Each agent does not know exactly how long it will take him to finish the task; he only knows the distribution from which this time is drawn. These times are independent across agents and the distributions fulfill the monotone hazard rate condition. Agents are selfish and will lie about their distributions if this increases their expected utility. We study different variations of the Vickrey mechanism that take as input the agents' reported distributions and the players' realized running times and that output a schedule that minimizes the expected sum of processing times, as well as payments that make it an ex-post equilibrium for the agents to both truthfully report their distributions and exert full effort to complete the task. We devise the ChPE mechanism, which is uniquely tailored to our problem, and has many desirable properties including: not rewarding agents that fail to finish the task and having non-negative payments.en
dc.publisherAAAI Publicationsen
dc.subjectmechanism designen
dc.subjectschedulingen
dc.subjectmonotone hazard rate distributionen
dc.subjectVickrey mechanismen
dc.subjectex-post truthfulen
dc.subjectexecution uncertaintyen
dc.titleMechanism Design for Scheduling with Uncertain Execution Time.en
dc.typeConferenceen
dc.researchgroupCyber Security Centreen
dc.peerreviewedYesen
dc.funderNSF and AROen
dc.projectidgrants CCF- 1101659, IIS-0953756, CCF-1337215, W911NF-12-1-0550, and W911NF-11-1-0332en
dc.cclicenceCC BYen
dc.date.acceptance2014-07-04en


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