Learning Dynamic Time Preferences in Multi-Agent Meeting Scheduling

Abstract

In many organizations, people are faced with the task of scheduling meetings subject to conflicting time constraints and preferences. We are working towards multi-agent scheduling systems in which each person has an agent that negotiates with other agents to schedule meetings. Such agents need to model the scheduling preferences of their users in order to make effective scheduling decisions. We consider that a user's preferences over meeting times are of two kinds: static time-of-day preferences, e.g., morning versus afternoon times; and dynamic preferences which change as meetings are added to a calendar, e.g., preferences to schedule meetings back-to-back (i.e. in succession). The dynamic nature of preferences has been understudied in previous work. In this paper, we present an algorithm that effectively learns static time-of-day preferences, as well as two important classes of dynamic preferences: back-to-back preferences and spread-out preferences (i.e. preferences for having gaps between meetings).

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2005
Accession Number
ADA457066

Entities

People

  • Elisabeth Crawford
  • Manuela M. Veloso

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Computer Science
  • Electronic Mail
  • Explosives Initiators
  • Frequency
  • Learning
  • Machine Learning
  • Multiagent Systems
  • Natural Languages
  • Negotiations
  • Probability
  • Reinforcement Learning
  • Scheduling (Production)
  • Test Sets
  • Training

Fields of Study

  • Psychology

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Systems Analysis and Design