3.2.2 State of the Art
Practical methods for learning from large volumes of single-media data have been demonstrated in a number of areas. For example, methods such as decision tree learning, neural network learning, genetic algorithms, and Bayesian methods have been applied to data-mining problems such as assigning credit ratings based on bank records, recognizing human faces, and predicting medical treatment outcomes based on medical symptoms. New approaches have recently been developed, such as inductive logic programming, which enables learning more expressive hypotheses than earlier learning methods. Significant progress has occurred recently in developing a theory of machine learning. For example, there is now a quantitative understanding of how the error in learned hypotheses depends on the amount of training data provided and the complexity of the hypotheses considered by the learner. The field is moving forward rapidly, pushed by recent technical advances and the growing need for this technology.
3.2.3 Research Opportunities
Current learning methods already provide positive value, but basic research in new learning algorithms is likely to have significant payoff. Research is needed to develop methods that generalize more accurately from limited data, further develop the underlying theory of machine learning, and understand how to employ user-provided conjectures and background knowledge when analyzing available training data.
In addition, the opportunities for machine-learning applications in the NII suggest specific research directions. Methods for learning over multimedia data will be increasingly important. New methods will be needed for combining information from multiple databases. Given that much information on the NII will be in the form of text, one ripe area for basic research involves combining natural language-processing techniques with machine-learning methods. This area, which has been overlooked by researchers to a surprising degree in the past, seems especially important for the NII. A recognized but unfulfilled promise of machine learning is to aid the continual maintenance of large knowledge bases for knowledge-based assistant programs. Many systems are currently deployed throughout every sector of society; machine learning can help reduce the amount of labor needed for both development and maintenance.
An additional NII-related topic is social learning methods. Here, small amounts of information from multiple users are combined to provide individually customized advice to each. The recommendation of news articles to individuals provides an example. After observing a small number of articles that user A reads and likes, a social learning system might suggest additional articles by correlating A’s interests with other users, then recommending articles liked by the most similar other users.
3.3 Reasoning about Plans, Programs, and Action
The field of planning develops algorithms that automatically construct and execute sequences of primitive commands in order to achieve high-level goals. Research focuses on designing languages for modeling dynamic systems and devising algorithms that synthesize possible courses of action. Issues revolve around tradeoffs involving the expressiveness of modeling languages, the specification of performance measures, and the complexity of the underlying search problems.
3.3.1 Relevance to the NII
Using networked computing and information services effectively requires an understanding of their capabilities and the ability to chain services together to achieve complex objectives. For example, the Internet Netfind service can determine a person’s email address but only if provided with distinguishing information about the person, such as his or her city or institutional affiliation. AI planning systems can automatically reason about formal models of Netfind and other utilities to focus information-gathering activities in profitable directions (for example, first determining the person’s city, then calling Netfind). Because NII users will routinely generate information gathering tasks, AI planners can efficiently assist users in navigating networks and managing the costs of access and retrieval. In contrast, existing search tools (such as Archie, Veronica, and Anarchie) are limited by inflexible strategies and the lack of a predictive model of the dynamic environment in which they operate. This dynamic aspect is one of the most important and challenging features of the NII. The associated decision problems are extremely complex given the heterogeneous computing environment, scores of separate and largely incompatible databases, diverse methods of access, and complicated protocols for communication.
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