VLDB 2026 Research / reviewers in the wild / expert
Bart Peintner
dblp:32/824
· DBLP profile ↗
9ranked-venue papers
4as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 7 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-authorSoftware engineering, systems software and programming languages · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 2Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
3 papers |
Knowledge representation and reasoning · 66% Planning, search and constraint satisfaction · 34% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › temporal reasoning
disjunctive temporal problem |
0.1 | 2 | 2005 | Augmenting Disjunctive Temporal Problems with Finite-Domain Constraints · AAAI 2005 Low-cost Addition of Preferences to DTPs and TCSPs · AAAI 2004 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
temporal reasoning |
0.1 | 2 | 2005 | Augmenting Disjunctive Temporal Problems with Finite-Domain Constraints · AAAI 2005 Low-cost Addition of Preferences to DTPs and TCSPs · AAAI 2004 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › heuristic search
anytime search |
0.1 | 1 | 2005 | Anytime, Complete Algorithm for Finding Utilitarian Optimal Solutions to STPPs · AAAI 2005 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
temporal planning |
0.1 | 1 | 2005 | Anytime, Complete Algorithm for Finding Utilitarian Optimal Solutions to STPPs · AAAI 2005 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › nonmonotonic reasoning
preference handling |
0.0 | 1 | 2004 | Low-cost Addition of Preferences to DTPs and TCSPs · AAAI 2004 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
soft constraints |
0.0 | 1 | 2004 | Low-cost Addition of Preferences to DTPs and TCSPs · AAAI 2004 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › temporal reasoning
temporal constraint satisfaction |
0.0 | 1 | 2004 | Low-cost Addition of Preferences to DTPs and TCSPs · AAAI 2004 |
Methods — techniques the papers use, named apart from their topics
dynamic programming · 0.1branch-and-bound · 0.1constraint satisfaction · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Evaluating intelligent knowledge systems: experiences with a user-adaptive assistant agent
Pauline M. Berry, Thierry Donneau-Golencer, Khang Duong, Melinda T. Gervasio, Bart Peintner, Neil Yorke-Smith |
Knowl. Inf. Syst. | 5 |
| 2011 | PTIME: Personalized assistance for calendaringabstractIn 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. Pauline M. Berry, Melinda T. Gervasio, Bart Peintner, Neil Yorke-Smith |
ACM Trans. Intell. Syst. Technol. | 3 |
| 2009 | Evaluating User-Adaptive Systems: Lessons from Experiences with a Personalized Meeting Scheduling Assistant
Pauline M. Berry, Thierry Donneau-Golencer, Khang Duong, Melinda T. Gervasio, Bart Peintner, Neil Yorke-Smith |
IAAI | 5 |
| 2009 | Task Assistant: Personalized Task Management for Military Environments
Bart Peintner, Jason Dinger, Andres C. Rodriguez, Karen L. Myers |
IAAI | 1 |
| 2007 | Strong Controllability of Disjunctive Temporal Problems with Uncertainty
Bart Peintner, K. Brent Venable, Neil Yorke-Smith |
CP | 1 |
| 2005 | Augmenting Disjunctive Temporal Problems with Finite-Domain Constraints
Michael D. Moffitt, Bart Peintner, Martha E. Pollack |
AAAI | 2 |
| 2005 | Anytime, Complete Algorithm for Finding Utilitarian Optimal Solutions to STPPs
Bart Peintner, Martha E. Pollack |
AAAI | 1 |
| 2005 | On Solving Soft Temporal Constraints Using SAT Techniques
Hossein M. Sheini, Bart Peintner, Karem A. Sakallah, Martha E. Pollack |
CP | 2 |
| 2004 | Low-cost Addition of Preferences to DTPs and TCSPs
Bart Peintner, Martha E. Pollack |
AAAI | 1 |