concurrent problem-solving effort involved, and in realistic representation and modeling.5 Software for complex systems also presents challenges in reasoning under uncertainty.6 Adaptive software presents challenges in machine learning and reasoning under uncertainty. Interface technology develops collaborative (with the user) problem solving and virtual reality. Speech synthesis and recognition present challenges in collaborative problem solving, machine learning/adaptive systems, reasoning under uncertainty, and virtual worlds. Neural networks present challenges in machine learning, reasoning under uncertainty, and neurophysiological models of cognition.
The sixth topic in the terms of reference was facilities, and the related questions were ''What facilities are the highest priority to emphasize in furthering the unique strengths that a government laboratory brings to this field? Which facilities are the most appropriate at a university?" This topic is addressed in the presentation of each grand challenge area to the extent possible on the basis of the panel's collective experience.
Computer Science and Technology Board, National Research Council, Scaling Up: A Research Agenda for Software Engineering, National Academy Press, Washington, D.C., 1989, p. 4.
John E. Hopkins and Kenneth Kennedy, Computer Science, Achievements and Opportunities, Society for Industrial and Applied Mathematics, Philadelphia, Pa., 1989, p. 73.