The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - July-August (1997 vol.12)
pp: 56-64
ABSTRACT
<p>In recent years,AI researchers and system developers have grown increasingly interested in task-oriented approaches to problem solving. One task, temporal reasoning, is pervasive in many AI activities, including diagnosis, planning, scheduling, temporal database management, and natural-language understanding. These activities would benefit from a temporal knowledge server that could deal efficiently with various types of temporal information. Indeed, specialized temporal-information managers have emerged, and AI researchers have proposed several approaches for dealing with time in problem solving. </p> <p>Later (layered temporal reasoner), our general-purpose manager of temporal information, fills such a need. It exhibits the following characteristics: </p> <p><li>The Later knowledge server operates as a loosely coupled cooperative agent for use by various problem solvers (or applications) that need to deal with time.</li> <li>Its clear, easy-to-use interface language lets users easily manipulate and query a temporal knowledge base.</li> <li>Later's predictable behavior means that temporal reasoning is correct and complete and that reasoning is computationally tractable.</li> <li>Its query processing is efficient; in fact, query processing is the basis of the integration with other reasoning tasks. </li></p> <p>Users can exploit Later in problem solving by adopting a modular approach that loosely couples our system with other modules. After describing Later's architecture and operations, this article demonstrates its usefulness with a concrete example involving temporal model-based diagnosis. </p>
CITATION
Vittorio Brusoni, Luca Console, Paolo Terenziani, Barbara Pernici, "Later: Managing Temporal Information Efficiently", IEEE Intelligent Systems, vol.12, no. 4, pp. 56-64, July-August 1997, doi:10.1109/64.608197
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool