Balancing the Needs of Personalization and Reasoning in a User-Centric Scheduling Assistant

Abstract

We describe the interaction of three aspects core to a personalized scheduling task. First, we develop a preference model designed to capture user preferences for the task of scheduling a meeting request between multiple people, and a methodology for preference elicitation to initially populate this model. Second, we explain a natural-language-based elicitation of the meeting request details and constraints, and outline the solving of the resulting constrained scheduling problem (with preferences). Third, we describe the display of solutions to the scheduling problem to the user, as candidate scheduling options with explanations, and detail unobtrusive learning of revisions to the preference model from the user's choices among the candidates. We describe the user studies that informed our design choices, and assess the resulting system in terms of the quality of scheduling options presented, according to the user. The scheduling task enabled by the integration of these aspects has been implemented within a deployed application.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2007
Accession Number
ADA469282

Entities

People

  • Bart Peintner
  • Melinda Gervasio
  • Neil Yorke-smith
  • Pauline Berry

Organizations

  • SRI International

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Commerce
  • Computer Languages
  • Computers
  • Distance Learning
  • Engineering
  • Language
  • Machine Learning
  • Natural Languages
  • Personal Information Managers
  • Reasoning
  • Scheduling (Production)
  • Standards
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Operations Research
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.