Scheduling with uncertain resources: Representation and utility function

1. RADAR Reflective Agent withDistributed Adaptive Reasoning Scheduling with uncertain resources: Representation and utility function Ulas Bardak, Eugene Fink, and Jaime Carbonell

2. Help not only in routine situations ,but also under crisis conditions MILITARY-SETTING MOTIVATION VIDEO Purpose • Automation of office tasks, such as scheduling and resource allocation

3. Challenges • Intelligent performance ofoffice-management tasks • Dealing with uncertaintyand unexpected situations • Collaboration with users

4. Conference planning Scheduling of talks at a conference, and related allocation of rooms and equipment, in a crisis situation. DEMO • Unexpected major change inspace availability; for example,closing of a building • Continuous stream of minor changes;for example, schedule changes and unforeseen equipment needs

5. Parser Optimizer Info elicitor Updateresourceallocation Chooseand sendquestions Graphicaluser interface Administrator Architecture Top-level control and learning Processnew info

6. Uncertainty The system allows uncertainty in the representation of all variables and functions in optimization problems. • Uncertain nominals • Uncertain integers • Uncertain utility

7. Uncertain nominals An uncertain nominal value is either a complete unknown or a set of possible values and their probabilities. Example:We have ordered vegetarian meals, but there is a chance that we will receive meals of a wrong type. Meal-type: 0.90 chance: vegetarian 0.05 chance: regular 0.05 chance: vegan

8. Proba- bility 0.006 0.004 0.002 0 0 200 400 600 800 Room Size Uncertain integers An uncertain integer is either a complete unknown or a probability-density function represented by a set of uniform distributions. Example:An auditorium has about 600 seats. Room-size: 0.2 chance: [450..549] 0.6 chance: [550..650] 0.2 chance: [651..750]

9. Piecewise-linear function with uncertain y-coordinates • Set of possible piecewise-linear functions and their probabilities 0.2 chance 1.0 0.5 Quality 0.8 chance 0.0 Room Size 0 200 400 600 800 Uncertain utilities An uncertain utility function may be represented in three ways. • Complete unknown

10. Optimization The optimization algorithm is based on randomized hill-climbing. • Search for a schedule with the greatest expected quality • At each step, reschedule one event • Stop after finding a local maximumor reaching a time limit

11. Manual and auto scheduling Search time ScheduleQuality ScheduleQuality 0.83 0.83 0.80 0.78 0.72 Auto Auto Auto 0.63 Manual 0.9 Manual Manual 0.8 0.7 0.6 4 1 3 9 2 5 6 7 8 10 14 rooms 84 events 5 rooms 32 events 9 rooms 62 events Time (seconds) 14 rooms 84 events problem size Experiments without uncertainty with uncertainty

12. Limitation: We assume that all probability distributions are independent. Current work: • Learning of typical requirementsand default user preferences • Contingency scheduling Conclusions Results: • Optimization based on uncertainresources and constraints • Collaboration with the user