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 AIC Home   >   People   >   Dr Pauline M Berry
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Dr Pauline M Berry

Senior Computer Scientist
Artificial Intelligence Center

SRI International
Room EK203
333 Ravenswood Avenue
Menlo Park, CA 94025-3493
USA

Phone:  (650) 859-2159
Fax:  (650) 859-3735

Email: 

Home Page: http://www.ai.sri.com/~berry

   Research interests

Dr. Pauline M. Berry is a Computer Scientist at the AI Center, SRI International. Areas of experience include process control, scheduling, constraint-based search, distributed and multiagent problem-solving and AI approaches to the management of information. She has applied her work to problems in the military domain, manufacturing industry and to problems involving the distribution of information, manpower and services in the service industries. Dr. Berry has led advanced research projects in the USA, UK, and Switzerland. She was Principal Investigator on the project "Intelligent Workflow for Collection Management" under the DARPA-AIM program. She received her Ph.D. in computer science from the University of Strathclyde, UK. Her Ph.D. dissertation was in the area of knowledge-based reasoning for resource management and was titled "A predictive Model for Satisfying Conflicting Objectives in Scheduling Problems".

   Current Projects

CALO

Cognitive Assistant that Learns and Organizes
As part of DARPA’s Perceptive Agent that Learns (PAL) program, SRI and team members are working on developing a next-generation "Cognitive Agent that Learns and Organizes" (CALO).
 

Neptune

Neptune
A user-centric, integrated planning and scheduling system that assists the user in exploring the rich space of plans and associated resource assignment options in complex, real-world domains. Each component of the system (user, planner, and scheduler) reacts to the actions of the other, resolving conflicts, and iteratively refining the solution until acceptable.
 

   Past Projects

DCOPE

Distributed Continual Planning and Execution for Autonomous Air Vehicles
This goal of this project is to develop a new software capability for the reprogrammable, coordinated command and control of teams of autonomous unmanned combat air vehicles (UCAVs) and unmanned ground vehicles (UGVs).
 

ENDSTATE

ENDSTATE
The objectives of the ENDSTATE initiative aim to provide new and innovative tools to aid in understanding and exploiting the vulnerabilities created by increasingly interconnected and interdependent physical network infrastructures. The vision was to bring together different but consistent model structures and analysis technologies so as to provide an insight into the vulnerabilities of the infrastructure. The insight could be used to derive the appropriate course of action, given desired effect
 

DERBI

Explaining and Recovering from Computer Break-Ins
A system for forensic analysis of computer break-ins.
 

SWIM

Intelligent Workflow for Collection Management
The objective of this project was to develop a revolutionary approach to workflow management within the Intelligence Survaillance and Reconnaissance (ISR) domain. The SWIM system was developed and demonstrated advanced workflow management techniques and intelligent reactive control. Research was conducted in the following areas. Information flow control, reasoning about agent capabilities and delegation, and algorithms necessary for dynamic process creation, adaptation and load balancing.
 

MPA

Multiagent Planning Architecture
MPA is an open planning architecture that facilitates incorporation of new plan-related technologies, capitalizing on the benefits of distributed computing for efficiency and robustness. MPA provides protocols to support the sharing of knowledge and capabilities among agents involved in cooperative problem solving. MPA has been demonstrated in the air campaign planning domain, and was used as the infrastructure for the flagship demonstration of the DARPA Planning Initiative.
 

DTT Task Management

Task Management for Dynamic Tactical Targeting
Provide task refinement, management and reasoning capabilities to support the Dynamic Sensor Management system being developed by Alphatech
 

TEAMBOTICA

TEAMBOTICA: A Robotic framework for integrated teaming, tasking, networking and control
Teambotica is a research initiative to develop the computational framework necessary for intelligent, autonomous teams of robots to operate in hostile environments.
 

LAW

The Link Analysis Workbench
The goal of this project is to develop the Link Analysis Workbench (LAW), a Web-accessible tool where analysts and machines collaboratively perform link analysis by defining hierarchical and temporal patterns, that include uncertain and qualitative elements, and by defining search strategies for pattern application, through a graphical user interface that supports direct graphical browsing and editing of patterns, search strategies, and summaries and details of resulting matches.
 

   Publications

The following are in reverse chronological order of publication.

Showing most recent 5 out of 24  [View All]

  • Berry, P. and Bulka, B. and Peintner, B. and Roberts, M. and Yorke-Smith, N. Neptune: A Mixed-Initiative Environment for Planning and Scheduling, in Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS’08), Coconut Grove, FL, May 2008.  [Details]

  • Berry, P. M. and Moffitt, M. D. and Peintner, B. and Yorke-Smith, N. The Design of a User-Centric Scheduling System for Multi-Faceted Real-World Problems, in Proceedings of ICAPS’07 Workshop on Moving Planning and Scheduling Systems into the Real World, Providence, RI, Sep 2007.  [PDF, Details]

  • Berry, P. M. and Gervasio, M. and Peintner, B. and Yorke-Smith, N. A Preference Model for Over-Constrained Meeting Requests, in Proceedings of AAAI 2007 Workshop on Preference Handling for Artificial Intelligence, Vancouver, Canada, pp. 7–14, Jul 2007.  [PDF, Details]

  • Berry, P. and Peintner, N. and Yorke-Smith, N. Bringing the User Back into Scheduling: Two Case Studies of Interaction with Intelligent Scheduling Assistants, in Proceedings of AAAI Spring Symposium on Interaction Challenges for Artificial Assistants, AAAI Press, pp. 10–12, Mar 2007.  [PDF, Details]

  • Berry, P. and Gervasio, M. and Peintner, B. and Yorke-Smith, N. Balancing the Needs of Personalization and Reasoning in a User-Centric Scheduling Assistant, Technical Note 561. Artificial Intelligence Center, SRI International, 333 Ravenswood Ave, Menlo Park, CA 94025, Feb 2007.  [PDF, Details]

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