Bart Peintner

TIST Journal 2011 Journal Article

PTIME

• Pauline M. Berry
• Melinda Gervasio
• Bart Peintner
• Neil Yorke-Smith

In a world of electronic calendars, the prospect of intelligent, personalized time management assistance seems a plausible and desirable application of AI. PTIME ( Personalized Time Management ) is a learning cognitive assistant agent that helps users handle email meeting requests, reserve venues, and schedule events. PTIME is designed to unobtrusively learn scheduling preferences, adapting to its user over time. The agent allows its user to flexibly express requirements for new meetings, as they would to an assistant. It interfaces with commercial enterprise calendaring platforms, and it operates seamlessly with users who do not have PTIME. This article overviews the system design and describes the models and technical advances required to satisfy the competing needs of preference modeling and elicitation, constraint reasoning, and machine learning. We further report on a multifaceted evaluation of the perceived usefulness of the system.

AAMAS Conference 2006 Conference Paper

Deploying a Personalized Time Management Agent

• Pauline Berry
• Ken Conley
• Melinda Gervasio
• Bart Peintner
• Tomas Uribe
• Neil Yorke-Smith

AAAI Conference 2005 Conference Paper

Anytime, Complete Algorithm for Finding Utilitarian Optimal Solutions to STPPs

• Bart Peintner

We present a simple greedy algorithm and a novel complete algorithm for finding utilitarian optimal solutions to Simple Temporal Problems with Preferences. Unlike previous algorithms, ours does not restrict preference functions to be convex. We present experimental results showing that (1) a single iteration of the greedy algorithm produces high-quality solutions, (2) multiple iterations, bounded by the square of the number of constraints, produce near-optimal solutions, and (3) our complete, memory-boundable algorithm has compelling anytime properties and outperforms a branch-andbound algorithm.

ICAPS Conference 2005 Conference Paper

Solving Over-constrained Disjunctive Temporal Problems with Preferences

• Bart Peintner
• Michael D. Moffitt
• Martha E. Pollack

We present an algorithm and pruning techniques for efficiently finding optimal solutions to over-constrained instances of the Disjunctive Temporal Problem with Preferences (DTPP). Our goal is to remove the burden from the knowledge engineer who normally must reason about an inherent trade-off: including more events and tighter constraints in a DTP leads to higher-quality solutions, but decreases the chances that a solution will exist. Our method solves a potentially over-constrained DTPP by searching through the space of induced DTPPs, which are DTPPs that include a subset of the events in the original problem. The method incrementally builds an induced DTPP and uses a known DTPP algorithm to find the value of its optimal solution. Optimality is defined using an objective function that combines the value of a set of included events with the value of a DTPP induced by those events. The key element in our approach is the use of powerful pruning techniques that dramatically lower the time required to find an optimal solution. We present empirical results that show their effectiveness.

AAAI Conference 2004 Conference Paper

Low-cost Addition of Preferences to DTPs and TCSPs

• Bart Peintner
• Martha E. Pollack

We present an efficient approach to adding soft constraints, in the form of preferences, to Disjunctive Temporal Problems (DTPs) and their subclass Temporal Constraint Satisfaction Problems (TCSPs). Specifically, we describe an algorithm for checking the consistency of and finding optimal solutions to such problems. The algorithm borrows concepts from previous algorithms for solving TCSPs and Simple Temporal Problems with Preferences (STPPs), in both cases using techniques for projecting and solving component sub-problems. We show that adding preferences to DTPs and TCSPs requires only slightly more time than corresponding algorithms for TCSPs and DTPs without preferences. Thus, for problems where DTPs and TCSPs make sense, adding preferences provides a substantial gain in expressiveness for a marginal cost.

ICAPS Conference 2002 Conference Paper

Execution Monitoring with Quantitative Temporal Dynamic Bayesian Networks

• Dirk Colbry
• Bart Peintner
• Martha E. Pollack

The goal of execution monitoring is to determine whether a system or person is following a plan appropriately. Monitoring information may be uncertain, and the plan being monitored may have complex temporal constraints. We develop a new framework for reasoning under uncertainty with quantitative temporal constraints - Quantitative Temporal Bayesian Networks -- and we discuss its application to plan-execution monitoring. QTBNs extend the major previous approaches to temporal reasoning under uncertainty: Time Nets, Dynamic Bayesian Networks and Dynamic Object Oriented Bayesian Networks. We argue that Time Nets can model quantitative temporal relationships but cannot easily model the changing values of fluents, while DBNs and DOOBNs naturally model fluents, but not quantitative temporal relationships. Both capabilities are required for execution monitoring, and are supported by QTBNs.