James Hendler - US grants

Efficient hierarchical planning, based on a task network, is the topic of this research. Goal and subgoal interactions will be investigated to achieve improved control strategies for planning. For example, it should be possible to improve both the quality of the plan and the efficiency of the planning process by developing a control strategy which selects among alternative task reductions in such a way as to avoid harmful interactions while encouraging helpful interactions. The following specific issues are central to this investigation: (1) how the various properties of interactions can guide the choice among alternative reductions in a task network; (2) how to improve the consistency of task network representations, in particular, how to identify situations where a seemingly unresolvable conflict can be resolved at a lower level in the task network; (3) how to take advantage of the fact that some planning tasks, while not independent in general, become independent once certain other plannng decisions have been made. The investigators anticipate that completion of the above research topics will result in significantly improved planning in a number of environments. To test this idea, they will implement a planning system incorporating the above ideas, and evaluate its performance on a number of domains.

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.

Many researchers are focusing attention on the need for much larger knowledge bases as Artificial Intelligence (AI) technologies scale up to realistically large real-world problems. This travel grant support 12 participants to attend the "International Conference on Building and Sharing of Very Large-Scale Knowledge Bases" and an accompanying workshop to be held in conjunction with the conference. The conference is held December 3-4, 1993. The conference and workshop takes place in Japan, at the Keio Plaza in Tokyo.//

9401151 Davis This award provides support for the acquisition of a distributed memory parallel computer together with support hardware for scientific visualization and storage of large image databases. The proposed system will be an experimental facility designed as a testbed for operating system and algorithm development, and will consequently run a wide range of experimental operating systems. The research topics to be explored span a broad range of applied research in high performance computing in three general categories: programming tools for HPC systems, parallel algorithms for scientific computing, and symbolic coding. A key component of the research program is the development of scalable parallel data structure and algorithms for the representation and analysis of global data sets arising from environmental science problems.

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 medium ITR project will develop a framework to facilitate science
research and education on the semantic web, and will implement and
evaluate prototype tools and applications for use in the biocomplexity
and biodiversity domains. These capabilities include the ability to
collaborate and convey meaning through the automatic and
semi-automatic semantic annotation of web documents; to improve
information retrieval using background knowledge and inference; and to
extract and fuse information from multiple, heterogeneous sources in
response to a query. A testbed for prototyping these capabilities will
be the web portal of the National Biological Information
Infrastructure (http://www.nbii.org/). The framework will include
specifications for ontologies, protocols, agents, and tools for
authoring, automated ingest, and annotation. These tools will leverage
collaboratively constructed ontologies to bring diverse communities
together and enable community construction of scientific
knowledge. Additional domain-independent, general purpose ontologies
will be developed to enable metadata about the contents and structure
of databases and other knowledge repositories to be expressed in
emerging knowledge markup languages such as RDF and OWL. This will
enable agents to both access and index the hidden web, and will also
support the data mining of diverse and distributed databases.

Tim Berners Lee has defined the semantic web to support machine understandable web pages. Much of the development in semantic web has been on standards for XML (eXtensible Markup Language), RDF (Resource Description Framework), Ontologies and Information Interoperability. For the semantic web to be useful in an operational environment, it has to be dependable. By a dependable semantic web we mean a semantic web that is secure, privacy-enhanced, manages trust, processes information in real-time and ensures high quality data.
The objective of this project will be to investigate policies for the semantic web. In particular, the web rules language will be expended to include the specification of security and privacy policies. The project will also develop an inference engine to reason about the policies. Privacy and trust policies for the semantic web and develop algorithms to enforce the policies.

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 project on the Theory and Practice of Accountable Systems investigates computational and social properties of information networks necessary to provide reliable assessments of compliance with rules and policies governing the use of information. In prior research, project leaders have demonstrated that achieving basic social policy goals in open information networks will require increased reliance on information accountability through after-the- fact detection of rule violations. This approach stands in contrast to the traditional mechanisms of policy compliance in network environments that rely on security technology to enforce rules by denial of access to resources at risk of abuse. So, access-based systems must be supplemented with accountability-based systems. To ensure that accountable systems can provide a stable, reliable, trustworthy basis on which to ground social policy arrangements in the future, it is necessary: 1) to research practical engineering approaches to designing these systems at scale, and 2) to develop a theory of the operating dynamics of accountable systems in order to establish what types of accountability assessments can be made, when those assertions are reliable, and what vulnerabilities accountable systems may have to attack, intrusion and manipulation. The key hypothesis to be tested regarding Information Accountability is that people are more likely to comply with rules (social or legal) if they believe that their non-compliance will be noticed. Successful study and development of accountable systems will ultimately enable real people, communities and institutions to take advantage of Information Accountability as a means of achieving better privacy and compliance with other information usage rules.

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.