James Hendler - US grants

This workshop on "Artificial Intelligence in Real-Time" is aimed at examining how AI systems can both be supported and can help to support real-time operating systems. This area is of critical importance as AI systems need to function in the support of such critical applications as nuclear power plant control, aircraft operation, hospital life support systems, and military command and control, among others. The workshop, to be held at the University of Maryland in the Fall of 1992, brings together members of both AI and real-time communities to explore issues of mutual interest. A report, produced by this workshop, will aid in the planning activities of the KMCS, RMI, and ITO Programs. according to NSF Manual 10,122.1.3 (b), such proposals are exempt from peer review.

9306580 Nau This is the first year funding of a three-year continuing award. A primary goal of the proposed work is to define, analyze, and explicate features of the design of HTN planning systems. The proposed work will have the following benefits: It will correctly characterize HTN planning and provide a framework which accounts for many features used in existing planning systems, but not yet completely formalized (including reduction, critics, commitment choices and filter conditions). It will provide a clear understanding of the efficiency and expressivity of HTN planning, and how these compare with the efficiency and expressivity of other approaches to planning (such as planning with STRIPS-style operators). It will provide ways to determine whether a given set of methods and critics correctly describe the planning problem they were meant to solve. It will describe the characteristics of planning domains in which HTN planners can operate efficiently, and develop efficient planning systems for those domains.

CDA 9529444 Nau, Dana Hendler, James University of Maryland at College Park CISE Research Instrumentation for AI Planning Research The Department of Computer Science and the Institute for Systems Research at the University of Maryland will purchase computing machining equipment, including a small multipurpose machine tool, a pentium-based personal computer for solid modeling and input to the tool, and a large disk drive for keeping large case-based planning libraries, all of which will be dedicated to support artificial intelligence research in the area of computer and information science and engineering. The equipment will be used for several research projects, including in particular: *AI-based planning for manufacturing, *High Performance Support for Large Knowledge Bases, *Combination of generative and case-based reuse techniques in AI planning, and *Low-cost robotics. In addition, a CAD tool will be donated by Bentley Systems Inc. to facilitate experimentation with the AI planning techniques in the manufacturing domain.

This grant supports participants in two one-day meetings to be held at the University of Maryland during the Fall of 2004 and Winter of 2005. The purpose of these meetings will be to discuss and plan for symposia, workshops, and other events in 2006 to mark the approximately 50 years of research and experience in the field of Artificial Intelligence. Participants will discuss ways to mark progress in AI, assess the current state of the art, and single out key areas for future work. In addition to planning how to mark a historical moment with appropriate activities, the participants in these meetings will discuss activities that can serve as a springboard for the field to focus on new challenges and directions. The results of these meetings will have impact across the entire spectrum of the AI community, including seasoned researchers, current doctoral, and undergraduate students, as well as other areas of computer science, historians of science, and the public at large, which is often not well versed in the goals, accomplishments, and history of AI.

This workshop is intended to refocus the field of artificial intelligence (AI) on its traditional goal: to understand the nature of cognition and its embodiment in human and artificial systems. The success of the field of AI can be seen in major accomplishments throughout the past decade including the beating of the best human chess player by a computer, the autonomous operation of robots on Mars and the autonomous control of the Deep Space One asteroid and comet mission, the use of rule-based systems by tens of thousands of Americans in the preparation of their yearly income taxes and the use of natural language technology in numerous applications including web search engines and the world's most successful word processing software. Despite these successes, the intellectual mission of understanding the nature of cognition remains one of the great challenges in modern science.

The overall goal of this work is to develop scientific guidelines that will steer research in AI over the next several decades. The symposium will also help raise awareness by students and others both of the achievements to date of the AI field, and the exciting scientific challenges that still remain. Artificial intelligence technologies have been applied in a wide range of disciplines and are often at the core of interdisciplinary efforts in bioinformatics and ecoinformatics, efforts in genomics and elsewhere using robotic technologies, the advancement of database integration and semantic web activities in the homeland defense arena and elsewhere, and in a number of other fields where the need to manage or coordinate knowledge is crucial to success. Helping other scientists to understand the contributions of AI to date, and the potential for the future, can be expected to lead to further indisciplinary work and the greater application of AI techniques in the other sciences, as well as in priority areas such as e-commerce and homeland security.

The BD Hubs foster regional networks of stakeholders and cooperate nationally on US priorities of importance to a region and to the nation. The activities of the BD Hubs contribute to a vibrant national data innovation ecosystem. The Northeast Big Data Innovation Hub serves as a uniquely neutral entity within this ecosystem, harnessing the data revolution by building strategic partnerships that advance innovative solutions to a broad range of societal, scientific, and industry challenges. This vision is empowered and strengthened through the Hub's collaboration with a diverse community of partners, including underserved populations, world-class institutions, and people of all backgrounds who rely on or are impacted by big data.

Leveraging the distinctive characteristics and challenges of the northeastern United States, the Northeast Hub will design and facilitate multi-disciplinary, community-led activities and initiatives such as:

- Aggregating and helping to develop best practices for responsible data science;
- Creating frameworks for data fluency;
- Fostering better management of data security and privacy;
- Integrating health data from traditional and novel sources;
- Improving education through big data; and
- Reducing barriers for data sharing within and between different sectors.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.