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Article
The token reification approach to temporal reasoning.
Faculty Publications
  • Lluis Vila
  • Han Reichgelt
SelectedWorks Author Profiles:

Han Reichgelt

Document Type
Article
Publication Date
1996
Date Issued
January 1996
Date Available
July 2014
Disciplines
Abstract
The approach to temporal reasoning which has proven most popular in AI is the reified approach. In this approach, one introduces names for events and states and uses special predicates to assert that an event or state occurs or holds at a particular time. However, recently the reified approach has come under attack, both on technical and on ontological grounds. Thus, it has been claimed that at least some reified temporal logics do not give one more expressive power than provided by alternative approaches. Moreover, it has been argued that the reification of event and state types in reified temporal logics, rather than event and state tokens, makes the ontology more complicated than necessary. In this paper, we present a new reified temporal logic, called TRL, which we believe avoids most of these objections. It is based on the idea of reifying event tokens instead of event types. However, unlike other such attempts, our logic contains “meaningful” names for event tokens, thus allowing us to quantify over all event tokens that meet a certain criterion. The resulting logic is more expressive than alternative approaches. Moreover, it avoids the ontologically objectionable reification of event types, while staying within classical first-order predicate logic.
Comments
Citation only. Full-text article is available through licensed access provided by the publisher. Published in Artificial Intelligence, 83, 59-74. doi: 10.1016/0004-3702(94)00093-X. Members of the USF System may access the full-text of the article through the authenticated link provided.
Language
en_US
Publisher
Elsevier
Creative Commons License
Creative Commons Attribution-Noncommercial-No Derivative Works 4.0
Citation Information
Vila, L., & Reichgelt, H., (1996). The token reification approach to temporal reasoning. Artificial Intelligence, 83, 59-74. doi: 10.1016/0004-3702(94)00093-X