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Dr Karen Myers

Lab Director & Principal Scientist
Artificial Intelligence Center

SRI International
Room EJ245
333 Ravenswood Avenue
Menlo Park, CA 94025-3493

Phone:  (650) 859-4833
Fax: (650) 859-3735

Home Page: 

Biography and Interests

Dr. Karen Myers is Director of the Intelligent Mixed-initiative Planning and Control Technologies (IMPACT) program within the AI Center at SRI International. She is also an SRI Principal Scientist. Dr. Myers joined SRI in 1991 after completing a Ph.D. in computer science at Stanford University. Her research interests include the areas of reactive control, multiagent systems, automated planning, advisable technologies, and mixed-initiative problem-solving. Her work in these areas spans the range of basic research, technology development, and applications building. Dr. Myers currently serves on the Executive Council for ICAPS and the AIJ Editorial Board. She recently completed terms on the Executive Council for AAAI and the Editorial Board for JAIR.

Current Projects


Policy-directed Autonomy for Coordinated Teams

PACT seeks to support coordination of mixed-autonomy teams that combine both autonomous platforms and humans for integrated problem solving.

Task Assistant

Task Assistant

Task Assistant is a flexible, web-based workflow application that enables an organization to move its operating procedures from manuals and human memories into living collaborative artifacts. Task Assistant is fully collaborative, allowing multiple users to simultaneous edit, build, or execute workflows.

Past Projects

Advisable Planners

Advisable Planners

The Advisable Planners project sought to make AI planning technology more accessible and controllable through the metaphor of advisability. User-provided advice specifies characteristics for both the desired solution and the problem-solving process to be employed during plan generation. Such advice is specified in a high-level language that is natural and intuitive for users, then operationalized into constraints that direct the underlying planning technology.

Rationale Capture

Automated Capture of Design Rationale

This project developed nondisruptive techniques for automatically acquiring rationale information for the detailed design process. The project produced a Rationale Construction Framework (RCF) system that monitors designer interactions with a CAD tool to produce a rich process history. This history is then structured and interpreted relative to a background theory of `design metaphors', thus enabling summarization and explanation of key elements of the design process.


Cognitive Assistant that Learns and Organizes

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


Coordination of Distributed Activities (CODA)

The CODA system provides targeted information dissemination among distributed planners as a way of improving team coordination. In CODA, an individual planner declares interest in different types of plan changes that could impact his local plan development. As a team of distributed users develop plans with a plan authoring tool, their activities are monitored; changes that match declared interests are forwarded automatically to the person who declared interest in them.



A system for extracting information from free text.


Inferring Intent of Attackers

CAPRE uses plan recognition techniques to automatically determining the intent behind a cluster of security alerts. This allows us to prioritize and explain alert clusters to users.


Integrated Battle Command Rolling Start

DARPA’s Integrated Battle Command program (IBC) aims to support the commander’s intuition, judgment, and creativity using flexible, intelligent decision aids. Unlike some previous efforts, IBC will focus on interactive methods that enable humans to guide the search for solutions.

Integrated Learning

Integrated Learning

The Integrated Learning project is focused on developing machine learning technology that would enable a system to learn general planning knowledge from user demonstrations of processes.

JFACC Continuous Planning

JFACC Continuous Planning and Execution

The main result of this project was the development of the Continuous Planning and Execution Framework (CPEF), which provides plan generation and replanning capabilities for situated agents in highly dynamic environments. Within CPEF, plans are treated as dynamic, open-ended artifacts that evolve in response to an ever-changing environment. In particular, plans must be updated in response to new information and requirements in a timely fashion to ensure their relevance and viability.


Learning from Experience to Enable Proactive Decision Support

The ELF project seeks to develop an experience-based learning framework that mines information from prior problem-solving activities to enable proactive information gathering in support of current tasks. This framework employs machine learning methods that generalize from past activities to identify preferred sources for specific information needs, to provide mechanisms for retrieving data required to support decision making, and to suggest processes in support of current tasks.


Mixed-Initiative Planning and Scheduling for Science Missions

We are developing mixed-initiative planning and scheduling technology that will enable NASA scientists to construct high quality mission plans that achieve as many of their science goals as possible, while satisfying all operations constraints. Our research will explore fundamental issues in how to reason about decisions and preferences from multiple sources, how to specify and utilize user preferences, and how to automate trade-off analysis. (With Ari Jonsson and John Bresina from NASA Ames)


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.


Plan Authoring System based on Sketches, Advice, and Templates (PASSAT)

PASSAT is a user-centric plan-authoring system grounded in the concepts of plan sketches, advice, and templates. PASSAT enables users to quickly develop plans that draw upon past experience encoded in templates, but that are customized to their individual preferences of a given user. The PASSAT core consists of an interactive plan authoring capability; tools for task management, constraint reasoning, plan sketching and causal reasoning provide provide complementary automated capabilities.

Brandeis PRIME

Privacy-preserving Information Mediation for Enterprises

The PRIME project seeks to create a flexible platform for research, development, experimentation, demonstration and deployment of techniques that will be important for learning how to build systems that share sensitive information across enterprises while protecting against undesirable uses of that information.


Semantically Enabled Assessment in Virtual Environments (SAVE)

The SAVE framework supports training in virtual environments through automated assessment of learner performance and tools for content authoring.


Tactical Mission Planning

SRI, in collaboration with NASA Ames, is developing mixed-initiative planning technology to support scientists in constructing tactical mission plans for future planetary exploration missions.


Taskable Reactive Agent Communities (TRAC)

The TRAC project developed mixed-initiative technology that enables flexible tasking and direction of agents by a user. Within TRAC, a user assigns tasks to agents along with guidance that imposes boundaries on agent behavior. During execution, the user manages agent activities in accord with a level of involvement that suits his individual needs. In essence, our work can be viewed as providing a form of process management technology that enables ready human control of agent communities.


The JFACC Planner/Scheduler

The main accomplishment on the project was the development of an integrated planning and scheduling capability that supports generation of tightly linked air operations plans and schedules, as well as their adaptation in response to changing tasks and resource availability. This effort built on existing planning (CPEF, from SRI) and scheduling (ACS, from CMU) technologies that provide core generation and repair techniques. (Joint work with Dr. Stephen F. Smith)


TransPAL: Transitioning PAL Technologies

The TransPAL project is transitioning a number of technologies developed on the DARPA PAL program for use by various members of the Armed Services.




Lumen is a agent framework in the Belief-Desire-Intention paradigm that supports autonomous task execution, optionally controlled by user advice.

PAL Framework

PAL Framework

PAL (Personalized Assistant that Learns) Framework website.

Plan Authoring System based on Sketches, Advice and Templates

Plan Authoring System based on Sketches, Advice and Templates

PASSAT is a user-centric plan-authoring system grounded in the concepts of plan sketches, advice, and templates.

 Procedural Reasoning System

Procedural Reasoning System

SRI's Procedural Reasoning System (PRS-CL) was developed for representing and using an expert's procedural knowledge for accomplishing goals and tasks.

SIPE-2: System for Interactive Planning and Execution

SIPE-2: System for Interactive Planning and Execution

SIPE-2 efficiently reasons about actions to generate a novel sequence of actions that responds precisely to the situation at hand. Supports interactive mixed-initiative planning with powerful GUI; Allows giving of advice to the planner; Plans hierarchically at different levels of abstraction.

SPARK: SRI Procedural Agent Realization Kit

SPARK: SRI Procedural Agent Realization Kit

SPARK is a Belief-Desire-Intention style agent framework grounded in a model of procedural reasoning.


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The following are in reverse chronological order of publication.
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