These techniques for structuring, using, and transforming representations form a solid basis for knowledge representation systems, but they do not address many problems of how to express specific types of knowledge. Thus, numerous researchers have searched for suitable representations for commonsense information, such as quantities, time, physics, uncertainty, and knowledge representations themselves. These efforts have resulted in several well-understood libraries of techniques, for example, temporal reasoning. Because these issues prove important in virtually all parts of AI, progress on them offers great advantage to the whole field.
3.1.3 Research Opportunities
Several knowledge representation research directions have the potential for exceptional payback for NII infrastructure and applications. The integration of description languages with object-oriented and relational databases could help provide value-added services on top of conventional data management platforms, examples of such services include more semantically oriented queries and knowledge discovery. The improvement of specialized languages for temporal, probabilistic, and nonmonotonic languages could provide support for natural language processing and information retrieval. The development of standard languages for encoding knowledge of scientific fields and World-Wide Web hypertext libraries could facilitate support of semantically rich queries. The elaboration of metalanguages for describing knowledge representation systems (for example, their accuracy, relevance, efficiency, and completeness) could expedite automatic translation and interoperability.
3.2 Learning and Adaptation
Machine learning addresses two interrelated problems: the development of software that improves automatically through experience; and the extraction of rules from a large volume of specific data. Machine learning is of growing importance because of the rapidly increasing quantities of diverse data on the NII and the expanding need for software that can automatically adapt to new or changing users and runtime environments. The central technical problem in machine learning is developing methods to automatically form general hypotheses from specific training examples.
3.2.1 Relevance to the NII
Machine-learning methods offer new capabilities for the NII that are unavailable using current software technology. Machine-learning algorithms identify general trends from specific training data, offering the promise of programs that examine gigabytes of network-accessible data to extract trends that would otherwise go unnoticed by people. Machine learning also offers approaches to automatically modeling the NII itself by learning probabilistic regularities in server loads, security breaches, correlations among user accesses to data repositories, and the identity of services that typically are appropriate for recurring information needs.
The usefulness of data mining (the extraction of general regularities from online data) may be illustrated by the problem of learning which medical treatments are most effective in particular situations or which land-zoning policies produce the best outcomes. Current learning methods are able to find regularities provided large data sets of single-media data (as opposed to mixtures of images, logical descriptions, text, and sound). New learning methods that address multimedia data and generalize more accurately could have a significant impact on our ability to use the ever-growing amount of data that will be available on the NII.
As a second application for machine learning, consider the problem each user faces in locating information in the flood of data that will be available on the NII. Machine-learning techniques could lead to electronic news readers that learn the interests of each user by observing what they read, then use this knowledge to automatically search thousands of news sources to recommend the ten most interesting articles. Similar applications include building intelligent agents that provide current awareness services, alerting users to new web pages of special interest, or providing "What’s New" services for digital libraries. Although information retrieval provides a baseline capability (such as keyword search on large stores of text), more accurate learning methods are needed.
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