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S Bringing the User Back into Scheduling: Two Case Studies of Interaction with Intelligent Scheduling Assistants www.sri.com Hypothesis: Real-World domains are better served by collaborative approaches that involve the user in the scheduling process than full-automation PTIME: Personalized Time Management 0 Elicit Preferences 3 Choose Request 1 Elicit Request Mixed Initiative Collaboration via 1. Intuitive elicitation of requests 4 Learn Preference Model 0 2. Unobtrusive adaptation to User’s personal preferences Elicit Preferenc e 1 Elicit Request 2 User Study 1.One-on-one vs. group 2.Mandatory vs. optional 3.Fixed vs. floating 1.Importance 2.Urgency 3.Interest (topic) 4.Relationships 5.Perturbation 6.Stability 7.Preferences Y3 Focus in Bold Perception of Event Process FOCI 1.Walk-in 2.Constraint satisfaction 3.Iterative refinement 4.Add-in Need Factors Preferences 3 Unobtrusive refinement of the preference model through natural interaction with the user Choose Schedule Constraint Reasoner Objective Function An SVM (QP-based) learner generalizes these specific comparisons to produce a second linear function 4 SVM Learner The two functions are combined in PLIANT to form the preference model for the user This dynamic model is used to derive the objective function by which scheduling options are evaluated, presented to the user, and scheduled Preference Model Scheduling Process Elicitation Process 1.Coordinating groups of busy people 2.Intelligent reminders 3.Transparency 4.Control Schedule Options/Selection Constraints LP-based Learning SVM Learner aizi + aijzizj bixi PLIANT 1.Meeting specific, e.g., day time 2.Calendar-wide e.g., density 3.How to relax meeting constraints 4.How to relax calendar-wide constraints Preference Model bixi + (1-) cixi Pisces: Large-Scale Logistics Scheduling • Problem Spec. (Goals) User • • User-enabled exploration of solution space User manipulation of solution and solution criteria Multiple and diagnostic views Pisces UI Problem Session Management Schedule Session State Scheduling Engine • Continuous, incremental schedule improvement – Rapid response, anytime solution generation • Support for complex evaluation functions – Resource balance, stability, activities included • Support for specialized query directives – Reduce peak in certain regions; Resource Flattening cixi Mixed Initiative Collaboration via 1. Direct manipulation of the scheduling problem 2. Facilitated exploration of the solution space – Keep activity in schedule; Fix an activity in time – Shift Left and Right Pauline Berry, Bart Peintner and Neil Yorke-Smith Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025 {berry, peintner, nysmith}@ai.sri.com Objective Function