Author: Melinda Gervasio

  • Mixed-Initiative Negotiation: Facilitating Useful Interaction Between Agent/Owner Pairs

    A mixed-initiative agent for personal time management interacts not only with its human owner but also with other agents and humans that share or depend on the same time commitments.

  • Iteration Learning By Demonstration

    We present a domain-independent approach to iteration learning by demonstration based on a dataflow model of user actions.

  • Question Asking to Inform Preference Learning: a Case Study

    This paper presents a case study that explores how to instantiate a question asking framework to select questions for a particular type of learner used within learning by demonstration systems, namely Alexo graphic preference learner.

  • What Were You Thinking? Filling in Missing Dataflow Through Inference in Learning from Demonstration

    This paper addresses the problem of learning from demonstrations involving unobservable (e.g., mental) actions. We explore the use of knowledge base inference to complete missing dataflow and investigate the approach in the context of the CALO cognitive personal desktop assistant.

  • What Were You Thinking? Filling in Missing Dataflow Through Inference in Learning from Demonstration

    This paper addresses the problem of learning from demonstrations involving unobservable (e.g., mental) actions. We explore the use of knowledge base inference to complete missing dataflow and investigate the approach in the context of the CALO cognitive personal desktop assistant.

  • Learning By Demonstration to Support Military Planning and Decision Making

    We describe the development and application of learning by demonstration technology to support user creation of automated procedures for a rich collaborative planning environment that is in widespread use by the U.S. Army.

  • Emma: An Event Management Assistant

    In this extended abstract we describe the motivation, design, deployment and evaluation of the Emma system.

  • Learning Email Procedures for the Desktop

    Abstract In this electronic age, we are all knowledge workers, tackling on a daily basis the information that flows through our email, file systems, web browsers, calendars and various other desktop desktop applications. Email has come to be the center of desktop activity for many of us: we set up meetings, exchange documents, manage projects,…

  • A Preference Model for Over-Constrained Meeting Requests

    We develop a preference model designed to capture user scheduling preferences for overconstrained meeting requests between multiple people, and a methodology for preference elicitation to initially populate this model.

  • An Intelligent Personal Assistant for Task and Time Management

    We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention agent system.

  • Deploying a Personalized Time Management Agent

    We report on our ongoing practical experience in designing, implementing, and deploying PTIME, a personalized agent for time management and meeting scheduling in an open, multi-agent environment. In developing PTIME as part of a larger assistive agent called CALO, we have faced numerous challenges, including usability, multi-agent coordination, scalable constraint reasoning, robust execution, and unobtrusive…

  • Multi-Criteria Evaluation in User-Centric Distributed Scheduling Agents

    This position paper discusses the problem of locally evaluating and comparing candidate schedules, in the context of a distributed scheduling task operating in unbounded environments in which each agent selfishly serves the desires and preferences of its own user.