The Role of Intelligent Systems in the National Information Infrastructure, страница 28

3.9.3 Research Opportunities

Much of the low-level image-understanding technology is relatively mature. For example, there are fairly reliable techniques for recovering visual motion, stereo reconstruction, and determining structure from motion. Similarly, in graphics, low-level processes such as rendering are almost-solved problems. These successes account for the positive capabilities listed earlier. Increasingly, the research challenge and opportunity is in higher-level processing, representation, and reasoning about visual and geometric information. Only at higher levels is there enough information to counter the noise and ambiguities that are the source of brittleness in current techniques. For example, emerging AI areas such as artificial life and evolutionary computation are beginning to show promise for such representation and reasoning problems. The key challenge in this area will be to build on the prior results of AI, vision, robotics, and graphics, and incorporate new concepts and technologies.

4. Conclusions

The National Information Infrastructure (NII) promises to deliver to people in their homes and businesses a vast array of information in many forms. It could significantly improve many aspects of citizens’ lives. However, for the NII to realize its potential, it must provide efficient and easy access without requiring specialized training. The field of AI is positioned to make substantial contributions to the NII. AI techniques can play a central role in the development of a useful and usable NII by (1) enabling the construction of human-computer interface systems that are goal-oriented, cooperative, and customizable; allow users to communicate in natural ways in a variety of modalities; and provide a consistent interface to the full range of NII services; (2) providing services for data and knowledge management integration and translation, and knowledge discovery in support of a more flexible infrastructure; and, (3) assisting in the development of more powerful software tools and environments by adding a range of advanced capabilities to rapid prototyping systems; enabling the development of intelligent project management aids; and supporting the construction of simulation systems that include more sophisticated simulated agents, including characters that act in human-like ways.

This report recommends support of AI research in eight key areas, each of which has substantial promise for high payback to the NII effort: knowledge representation; learning and adaptation; reasoning about plans, programs, and actions; plausible reasoning; agent architecture; multiagent coordination and collaboration; development of ontologies; speech and text processing; and, image understanding and synthesis.

Notes

[1] Grosz, B., and Davis, R., eds. 1994. A Report to ARPA on Twenty-First Century Intelligent Systems. AI Magazine 15(3): 10-20. http://www.aaai.org

[2] Clement, M., Katz, R., and Chien, Y., eds. 1994. Information Infrastructure Technology and Applications. http://www.hpcc.gov/reports/reports-nco/iita.rpt.php

[3] The Innovative Applications of Artificial Intelligence Conference Proceedings series (Menlo Park, California: AAAI Press) contains numerous examples of successfully deployed expert systems.

[4] Committee on Physical, Mathematical, and Engineering Sciences (CPMES) and the Federal Coordinating Council for Science, Engineering, and Technology (FCCSET). High Performance Computing and Communications: Toward a National Information Infrastructure. Washington, D.C.: Office of Science and Technology Policy, 1994; and IITA Task Group. Information Infrastructure Technology and Applications. Washington, D.C.: Office of Science and Technology Policy, February 1994.

[5] We use the term "NLP" here both for speech and text processing, although speech and text processing have distinct histories, research communities, and terminology.

Report Contributors:

Ruzena Bajcsy (University of Pennsylvania)

Ronald J. Brachman (AT&T Bell Labs)

Bruce Buchanan (University of Pittsburgh)

Randall Davis (Massachusetts Institute of Technology)

Thomas L. Dean (Brown University)

Johan de Kleer (Xerox Palo Alto Research Center)

Jon Doyle (Massachusetts Institute of Technology)

Oren Etzioni (University of Washington)

Richard Fikes (Stanford University)

Kenneth Forbus (Northwestern University)

Barbara Grosz (Harvard University)

Steve Hanks (University of Washington)

Julia Hirshberg (AT&T Bell Labs)

Ed Hovy (University of Southern California

Information Sciences Institute)

Daniel Huttenlocher (Cornell University)

Robert E. Kahn (CNRI)

Jean-Claude Latombe (Stanford University)

Yvan LeClerc (SRI International)

Thomas Mitchell (Carnegie Mellon University)

Ramesh Patil (University of Southern California

Information Sciences Institute)

Judea Pearl (University of California, Los Angeles)

Fernando C. N. Pereira (AT&T Bell Labs)

Paul S. Rosenbloom (University of Southern California

Information Sciences Institute)

Stuart J. Russell (University of California, Berkeley)

Katia Sycara (Carnegie Mellon University)

Bonnie L. Webber (University of Pennsylvania)

Beverly Woolf (University of Massachusetts)

NSF Observers

Su-Shing Chen

Y. T. Chien

Acknowledgments

This report was created with support from the Information Technology and Organizations Program of the IRIS Division of the National Science Foundation. The opinions presented in the report do not represent the views of the National Science Foundation.

Paul Beame, Pat Hayes, Richard Korf, Ed Lazowska, Aaron Sloman, and Lynn Stein gave many thoughtful suggestions for ways to improve this report. Many thanks also to Alicen Smith, Sunny Ludvik, and Carol Hamilton who helped with proofreading and copyediting.