Pauline M. Berry

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We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (a) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills,(More)
In a world of electronic calendars, the prospect of intelligent, personalized time management assistance seems a plausible and desirable application of AI. PTIME (<i>Personalized Time Management</i>) is a learning cognitive assistant agent that helps users handle email meeting requests, reserve venues, and schedule events. PTIME is designed to unobtrusively(More)
It is argued that to achieve the required balance between objectives, a scheduling system must have the ability to relate the consequence of a decision to the satisfaction of overall objectives. The dissertation introduces the concept of a preference capacity plan (PCP) in an attempt to give automated schedulers this ability. PCP takes cognizance of both(More)
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(More)
Many calendar tools have become available to organize, display, and track a user’s commitments. However, most people still spend a considerable amount of time personally organizing meetings and managing the constant changes and adjustments that must be made to their schedules. Our goal is to provide the technology necessary to manage an individual’s(More)
There is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents, both human and machine. We characterize the domainindependent challenges posed by this problem, and describe how properties of domains influence the challenges and their solutions. We will concentrate on dynamic, data-rich domains(More)
Pattern matching for intelligence organizations is a challenging problem. The data sets are large and noisy, and there is a flexible and constantly changing notion of what constitutes a match. We are developing the Link Analysis Workbench (LAW) to assist an expert user in the intelligence community in creating and maintaining patterns, matching those(More)
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(More)
This paper presents ongoing work to build the Personalized Time Manager (PTIME) system, a persistent assistant that builds on our previous work on a personalized calendar agent (PCalM) (Berry et al. 2004). PCalM was an early test of the hypothesis that in order to persist and be useful, an intelligent agent must learn and adapt to the user’s changing needs.(More)
We will demonstrate distributed conflict resolution in the context of personalized meeting scheduling. The demonstration will show how distributed constraint optimization can be used to facilitate interaction between cognitive agents and their users. The system is part of the CALO personal cognitive assistant that will also be explored during the(More)