Myers, Karen


Learning by Demonstration for a Collaborative Planning Environment

AI Magazine

Learning by demonstration technology has long held the promise to empower non-programmers to customize and extend software. We describe the deployment of a learning by demonstration capability to support user creation of automated procedures in a collaborative planning environment that is used widely by the U.S. Army. This technology, which has been in operational use since the summer of 2010, has helped to reduce user workloads by automating repetitive and time-consuming tasks. The technology has also provided the unexpected benefit of enabling standardization of products and processes.


Learning by Demonstration to Support Military Planning and Decision Making

AAAI Conferences

While the concept of learning by demonstration has been around for many years, recent advances in artificial intelligence technology have led to a resurgence of work in the field. We describe the development and application of learning by demonstration technology to support user creation of automated procedures for a rich collaborative planning environment that is in widespread use by the U.S. Army. User feedback and evaluation results show that the technology can be used effectively by the target user community and that it has tremendous potential for improving the speed and quality of performance for a range of critical tasks.


Task Assistant: Personalized Task Management for Military Environments

AAAI Conferences

We describe an AI-enhanced task management tool developed for a military environment, which differs from office environments in important ways: differing time scales, a focus on teams collaborating on tasks instead of an individual managing her own set of diverse tasks, and a focus on tasklists and standard operating procedures instead of individual tasks. We discuss the Task Assistant prototype, our process for adapting it from an office environment to a military one, and lessons learned about developing AI technology for a high-pressure operational environment.


An Intelligent Personal Assistant for Task and Time Management

AI Magazine

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 (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.


An Intelligent Personal Assistant for Task and Time Management

AI Magazine

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 (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.


AAAI 2000 Workshop Reports

AI Magazine

The AAAI-2000 Workshop Program was held Sunday and Monday, 3031 July 2000 at the Hyatt Regency Austin and the Austin Convention Center in Austin, Texas. The 15 workshops held were (1) Agent-Oriented Information Systems, (2) Artificial Intelligence and Music, (3) Artificial Intelligence and Web Search, (4) Constraints and AI Planning, (5) Integration of AI and OR: Techniques for Combinatorial Optimization, (6) Intelligent Lessons Learned Systems, (7) Knowledge-Based Electronic Markets, (8) Learning from Imbalanced Data Sets, (9) Learning Statistical Models from Rela-tional Data, (10) Leveraging Probability and Uncertainty in Computation, (11) Mobile Robotic Competition and Exhibition, (12) New Research Problems for Machine Learning, (13) Parallel and Distributed Search for Reasoning, (14) Representational Issues for Real-World Planning Systems, and (15) Spatial and Temporal Granularity.


AAAI 2000 Workshop Reports

AI Magazine

The AAAI-2000 Workshop Program was held Sunday and Monday, 3031 July 2000 at the Hyatt Regency Austin and the Austin Convention Center in Austin, Texas. The 15 workshops held were (1) Agent-Oriented Information Systems, (2) Artificial Intelligence and Music, (3) Artificial Intelligence and Web Search, (4) Constraints and AI Planning, (5) Integration of AI and OR: Techniques for Combinatorial Optimization, (6) Intelligent Lessons Learned Systems, (7) Knowledge-Based Electronic Markets, (8) Learning from Imbalanced Data Sets, (9) Learning Statistical Models from Rela-tional Data, (10) Leveraging Probability and Uncertainty in Computation, (11) Mobile Robotic Competition and Exhibition, (12) New Research Problems for Machine Learning, (13) Parallel and Distributed Search for Reasoning, (14) Representational Issues for Real-World Planning Systems, and (15) Spatial and Temporal Granularity.


The Home-Vacuum Event

AI Magazine

After a summary of the rules, we outline the high and low points of the competition. Then we suggest ways such competitions could better accommodate new teams in the future.


The Home-Vacuum Event

AI Magazine

This article discusses the setup and results from the Home-Vacuum event of the 1997 AAAI Robot Competition and Exhibition. After a summary of the rules, we outline the high and low points of the competition. Then we suggest ways such competitions could better accommodate new teams in the future.


Carmel Versus Flakey: A Comparison of Two Winners

AI Magazine

The University of Michigan's CARMEL and SRI International's FLAKEY were the first- and second-place finishers, respectively, at the 1992 Robot Competition sponsored by the Association for the Advancement of Artificial Intelligence. The two teams used vastly different approaches in the design of their robots. Many of these differences were for technical reasons, although time constraints, financial resources, and long-term research objectives also played a part. This article gives a technical comparison of CARMEL and FLAKEY, focusing on design issues that were not directly reflected in the scoring criteria.