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Later: Managing Temporal Information Efficiently
July-August 1997 (vol. 12 no. 4)
pp. 56-64

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.

Later (layered temporal reasoner), our general-purpose manager of temporal information, fills such a need. It exhibits the following characteristics:

  • 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.
  • Its clear, easy-to-use interface language lets users easily manipulate and query a temporal knowledge base.
  • Later's predictable behavior means that temporal reasoning is correct and complete and that reasoning is computationally tractable.
  • Its query processing is efficient; in fact, query processing is the basis of the integration with other reasoning tasks.
  • 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.

    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-Aug. 1997, doi:10.1109/64.608197
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