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Scheduling with uncertain resources Elicitation of additional data

Scheduling with uncertain resources Elicitation of additional data. Ula ş Bardak, Eugene Fink, Chris Martens, and Jaime Carbonell Carnegie Mellon University. Problem. Scheduling a conference under uncertainty Uncertain room properties Uncertain equipment needs Uncertain speaker preferences.

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Scheduling with uncertain resources Elicitation of additional data

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  1. Scheduling with uncertain resourcesElicitation of additional data Ulaş Bardak, Eugene Fink, Chris Martens, and Jaime Carbonell Carnegie Mellon University

  2. Problem Scheduling a conference under uncertainty • Uncertain room properties • Uncertain equipment needs • Uncertain speaker preferences The automated scheduler needs to collaborate with the human user.

  3. FOR EXAMPLE... Problem • The system may not have enough data for producing a good schedule • The user may be able to obtain some of the missing data, but not all data The system should identify critical missing data and ask the user only for these data.

  4. Initial schedule: Posters Talk Initial schedule Available rooms: 2 1 3 • Events and constraints: • Invited talk, 9–10am: Needs big room • Poster session, 9–11am: Needs a room • Missing info: • Invited talk: – Projector need • Poster session: – Room size – Projector need • Assumptions: • Invited talk: – Needs a projector • Poster session: – Small room is OK – Needs no projector

  5. Initial schedule: Posters Talk Useless info: There are no large rooms w/o a projector × Useless info: There are no unoccupied larger rooms × √ Potentially useful info Choice of questions 2 1 3 • Candidate questions: • Invited talk: Needs a projector? • Poster session:Needs a larger room? Needs a projector? • Events and constraints: • Invited talk, 9–10am: Needs a large room • Poster session, 9–11am: Needs a room

  6. Initial schedule: 2 1 Posters 3 Talk New schedule: 2 1 3 Talk Improved schedule • Events and constraints: • Invited talk, 9–10am: Needs a large room • Poster session, 9–11am: Needs a room Info elicitation: System: Does the poster sessionneed a projector? Posters User:A projector may be useful,but not really necessary.

  7. Parser Optimizer Info elicitor Update theschedule Choosequestions Graphicaluser interface Administrator Architecture Top-level control and learning Processnew info

  8. Choice of questions • For each candidate question, estimate theprobabilities of possible answers • For each possible answer, compute the respective change of the schedule quality • For each question, compute its expected impact on the schedule quality, and select questions with large expected impacts

  9. 0.72 0.68 0.61 Auto withElicitation Auto w/oElicitation ManualScheduling ScheduleQuality Experiments Scheduling of a large conference • 14 available rooms • 84 conference sessions • 700 uncertain variables

  10. Experiments optimal schedule 0.72 actual 0.68 estimated Schedule Quality 0.50 0 10 20 30 40 50 Numberof Questions

  11. Extensions • Game-tree search for themost important questions • Fast heuristics for pruning unimportant questions • Learning new strategiesfor question selection

  12. Conclusions We have developed a system that analyzes the importance of missing data, identifies critical uncertainties, and asks the user to obtain related additional data. It usually finds a near-optimal solution after asking 2% to 6% of all potential questions. The developed technique does not rely on specific properties of scheduling tasks, and it is applicable to a variety of problems that involve optimization under uncertainty.

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