The Role of Intelligent Systems in the National Information Infrastructure, страница 16

3. Research Thrust Areas

Research on the underlying nature of intelligence and the development of practical algorithms necessary to reproduce rudimentary machine intelligence leaves the field of AI strategically situated to contribute to the design and construction of the NII. However, the full realization of this promise requires a concerted attack on a variety of fundamental scientific problems. This report recommends support of AI research in eight key areas, each of which has substantial promise for high payback to the NII effort.

In this section, we describe these key subareas:

Knowledge representation

Learning and adaptation

Reasoning about plans, programs, and actions

Plausible reasoning

Agent architecture

Multiagent coordination and collaboration

Ontological development

Speech and language processing

Image understanding and synthesis.

This list is not a comprehensive enumeration of every interesting AI research topic. Rather, it elaborates areas that clearly and obviously emerged as important to the discussion within Section 2. Each is relevant to the development of a flexible and adaptive NII.

3.1 Knowledge Representation

Work on knowledge representation seeks to discover expressive, convenient, efficient, and appropriate methods for representing information about all aspects of the world. Expressive here means being capable of capturing both general and specific information in broad and narrow domains; it also implies the ability to express weak or incomplete statements (for example, whoever is president of the company will chair the board of trustees) as easily as strong and concrete statements (for example Charles Diamond will chair the board of trustees). Convenient means permitting acquisition and reporting of the information in terms close to those used by either lay persons or experts. Efficient means supporting rapid extraction of common and important conclusions from the information. Appropriate means translating between component representations when the representations that maximize expressiveness, convenience, or efficiency differ. Unfortunately, there are usually tradeoffs between these properties; no known knowledge representation method scores well along all dimensions.

3.1.1 Relevance to the NII

Knowledge representation problems are important because almost every intelligent computational activity depends on solving them to some degree. Most information currently stored on the Internet uses one of two degenerate knowledge representation methods: databases or natural language text. We use the word degenerate because these representations are extreme points on the expressiveness-efficiency spectrum. When information can be encoded in a relational database, one can quickly answer any query expressible in a language such as SQL, but only restricted and concrete bodies of knowledge can be encoded in a database. Natural language text, however, is expressive enough to encode much of human knowledge, but no one has yet efficiently mechanized inference over unrestricted natural language text. No foolproof algorithms exist for answering questions or extracting conclusions from natural language documents. Because relational database and natural language representations are insufficient, new knowledge representation methods are required to achieve NII objectives, such as accurate location of relevant information, narrow casting, semantic translation, and reusable software libraries.

3.1.2 State of the Art

Knowledge representation formalisms have been applied successfully to a variety of commercial and government applications, such as configuration, scheduling, customer service support, financial management, and software information systems. In each case, the knowledge representation methods offered considerably more flexibility than relational database systems. In general, knowledge representation systems offer syntax and structure much closer to natural languages but provide the semantic precision and inferential capability of logical languages. Many years of theoretical and experimental work have refined the core of these systems into description logics, which structure knowledge into modular, multiply connected taxonomic hierarchies of concepts, abstractions, and approximations. These logics offer the benefits of object-oriented databases and the structuring capabilities of hypertext-based libraries but go far beyond them in their expressiveness and in the algorithms available for retrieving information about entries and revising the hierarchies as information is added or updated. Guaranteed polynomial-time retrieval and inference algorithms have been developed for useful classes of description languages; still-richer languages admit algorithms that are usually fast in practice.