Intelligent agents must also adapt to their environment. As network utilities become heavily loaded or raise prices, the agent should shift to alternative services. When new NII databases and facilities are introduced or upgraded, it should explore them (or consult a broker) to determine applicability.
Machine learning techniques (Subsection 3.2) are crucial for building adaptive interfaces. Learning programs could unobtrusively watch over a user’s shoulder during his or her normal interaction with the computer and later could generalize from its observations to customize the software. Prototypes of such interfaces have already been implemented. For example, an intelligent scheduler learns preferred meeting times and locations, and a correspondence assistant learns from a user’s behavior how best to prioritize email messages. Future applications include personalized news streams as well as shopping assistants that learn a user’s tastes and price ranges during the course of home shopping, access online consumer reports, and suggest new items for purchase.
2.1.4 Virtual Reality, Telepresence, and
In many cases, the best interface gives the impression of directly manipulable, three-dimensional, physical reality. For example, an advanced CAD system might provide an automobile designer with the sensation of walking around, climbing inside, or driving a new vehicle around the test track--before a prototype is ever built. Virtual environments could have application in education, training, and entertainment as well. For example, virtual Japanese shopkeepers in a virtual Tokyo could provide students with an opportunity for language immersion without the expense of a trip to Japan. Telepresence might allow people to manipulate hazardous environments and, thus, safely perform tasks such as undersea exploration or nuclear reactor maintenance.
At present, virtual environments provide only a limited approximation of reality, and advances in both interface hardware and software are required for wide-scale use. Subsection 2.3.3 elaborates on the major problems in the development of virtual environments and the potential solutions to which AI can contribute. Populating a virtual environment with seemingly intelligent agents will require substantial advances in all areas of AI, especially the real-time issues of agent architecture (Subsection 3.5). Synthesizing realistic facial expressions that synchronize with spoken language and provide feedback about an agent’s understanding during dialogue will demand progress in both language and image processing (Subsections 3.8 and 3.9). Advances in both knowledge representation and ontological development (Subsection 3.1 and 3.7) will enable constructing synthetic environments that require specification of large amounts of knowledge about all aspects of the world.
2.2 Information Infrastructure Services
Advanced infrastructure services are a necessary prerequisite to the development of intelligent NII interfaces (Subsection 2.1) and the construction of National Challenge applications such as health care and electronic commerce. Each of these applications involve network-resident services. Just as the nation’s road system would be a confusing maze without such services as maps, gas stations, and signposts, the NII will be unusable without an advanced infrastructure. Because the NII will be dramatically larger and more complex than the nation’s road system, the demand for sophisticated NII infrastructure services will be vast.
As a simple example, a monolithic map will not suffice for NII navigation, because users will want customized directions that are sensitive to factors such as individual objectives and local network congestion. Instead of being forced to rely on general-purpose signposts, users will prefer customized maps and signs that emphasize information relevant to the user’s objectives. Agent-oriented navigation tools could also be popular; customized tour guides could take into account an individual’s special interests and actively search for the best routes. The challenge for infrastructure developers is to provide efficient, effective search methods that can navigate the network and transact queries only where appropriate, handling a vast variety of interfaces to resources, and interpreting and collating the results.
Чтобы распечатать файл, скачайте его (в формате Word).
Ссылка на скачивание - внизу страницы.