Computer Science and Artificial Intelligence (1997) / Chapter Skim
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3 Grand Challenge Areas
3 Grand Challenge Areas
Pages 7-19
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From page 7... ...
Each of the six grand challenge areas is discussed in turn in this chapter. For the most part, these discussions include a description of the grand challenge area; research objectives over the short term (within the next 2 years)
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From page 8... ...
Facilities Major computational resources to execute and validate complex models: distributed workstations plus parallel computer available over the network could suffice. Cooperation opportunities Multiple groups must collaborate to model complex systems (e.g., a ship)
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From page 9... ...
A number of studies comparing problem solving with and without supporting technologies have been made over the past several years. Many of them have found that formal meetings using group decision support technologies improved the productivity of groups, shortened the time to final decisions, and in some cases quantitatively improved the decisions themselves.
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From page 10... ...
Researchers in organizational behavior and management should understand the real needs of formal processes of decision making, since these processes were developed to preserve information, force rigor on the process, and collocate stakeholders to ensure unanimity in the decision. The impact of decentralized organizations and the dissolution of hierarchical management techniques will have significant impacts on the technologies that were designed for hierarchical decision processes.
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From page 11... ...
The present situation, in which many methods such as neural networks, case-based reasoning, and more traditional statistical methods compete on the basis of hyperbole and performance on toy problems, cannot continue. The grand challenge in machine learning and adaptive systems is to identify a scientific methodology and theory that characterize classes of practical learning and adaptation problems.
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From page 12... ...
. Needed Resources For any effective research program in machine learning and adaptive systems, the types and combinations of resources required can be summarized as follows: Skills-Mathematicians, computer scientists, statisticians, engineers, and cognitive scientists need to cooperate in this effort.
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From page 13... ...
they are subject to little or no uncertainty in knowledge of the circumstances. The grand challenges address aspects of the solving of complex problems, through representation and modeling, collaborative solution, machine learning, and virtual worlds.
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From page 14... ...
Although accuracy is uncertainty-limited in this application, credible research must deal with realistic constraints and objects. A possible set of objectives can be delineated as follows for the midterm and Tong term: 14 Midterm Devise metrics appropriate to comparison of traditionally incommensurate information and devise computer-applicable representation algebraist that will permit manipulation of currently incommensurate sets of related data, which can probably reduce uncertainty in individual data, preferably using methods faster than enumeration.
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From page 15... ...
The goal is to harness the physiological capabilities and training that enable us to perform physical tasks effectively in three dimensions and apply them to develop effective user interfaces for computer-based tasks. For an information-intensive user interface to be effective for a wide range of situations and users, it must be able to Design and present information to people on-the-fly, using multiple output media and Understand user input couched in multiple input media.
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From page 16... ...
Determine how to map abstract task domains effectively to a threedimensional environment in which it will be possible to visualize and manipulate objects in the domain. Determine how to take advantage of the richness of three-dimensional gesture to reduce reliance on icons to express actions in current user interfaces.
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From page 17... ...
Watson virtual worlds; NASA Ames virtual worlds; University of North Carolina virtual worlds, three-dimensional Ul; University of Pennsylvania-threedimensional ur, virtual worlds; University of Washington virtual worlds; and Xerox Palo Alto Research Center three-dimensional UI. Needed Resources In order to do effective research in this area, the following types of resources are required: Skill bas~Computer scientists, cognitive scientists, electronic engineers, optoelectronic engineers, and application area specialists.
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From page 18... ...
Level of effort~ive senior scientists distributed over the technical areas, ten research associates, and five technicians are required for a sustained multiyear effort. Opportunities for NRL NRL has significant in-house expertise in neural networks, control, cognitive science, and instrumentation.
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From page 19... ...
Chapter 3 Grand Challenge Areas industrial interest in the six grand challenge areas is as follows: Representation and modeling of complex systems-considerable and increasing interest by industry, Collaborative problem solving-considerable and increasing interest by industry, Machine learning and adaptive systems significant interest and investment by industry, 19 Reasoning under uncertain~some interest by industry but no trend apparent in industry, Virtual Worlds (reality) significant interest and investment by industry and a growing consumer and commercial market envisioned by industry, and Neurophysiological models of cognition little interest by industry.
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