|Event: A change of state. The event itself has no duration, otherwise the beginning and the end of the event would have to have their own duration or the beginning and the end of an event in turn would be independent events. See also regress, process, flux, change, states._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. |
AI Research on Events - Dictionary of Arguments
Norvig I 446
Events/AI research/Russell/Norvig: [A] situation calculus represents actions and their effects. Situation calculus is limited in its applicability: it was designed to describe a world in which actions are discrete, instantaneous, and happen one at a time. Consider a continuous action, such as filling a bathtub.
Problem: Situation calculus can say that the tub is empty before the action and full when the action is done, but it can’t talk about what happens during the action. It also can’t describe two actions happening at the same time (…).
Solution/Event calculus: Event calculus reifies fluents ((s) state variables) and events. The fluent At(Shankar , Berkeley) is an object that refers to the fact of Shankar being in Berkeley, but does not by itself say anything about whether it is true. To assert that a fluent is actually true at some point in time we use the predicate T, as in T(At(Shankar , Berkeley), t).
Events are described as instances of event categories. The event E1 of Shankar flying from San Francisco to Washington, D.C. is described as
E1 ∈ Flyings ∧ Flyer (E1, Shankar ) ∧ Origin(E1, SF) ∧ Destination(E1,DC) .
Norvig I 470
The event calculus was introduced by Kowalski and Sergot (1986)(1) to handle continuous time, and there have been several variations (Sadri and Kowalski, 1995(2); Shanahan, 1997(3)) and overviews (Shanahan, 1999(4); Mueller, 2006(5)). van Lambalgen and Hamm (2005)(6) show how the logic of events maps onto the language we use to talk about events. An alternative to the event and situation calculi is the fluent calculus (Thielscher, 1999)(7). James Allen introduced time intervals for the same reason (Allen, 1984)(8), arguing that intervals were much more natural than situations for reasoning about extended and concurrent events. Peter Ladkin (1986a(9), 1986b(10)) introduced “concave” time intervals (intervals with gaps; essentially, unions of ordinary “convex” time intervals) and applied the techniques of mathematical abstract algebra to time representation. Allen (1991)(11) systematically investigates the wide variety of techniques available for time representation; van Beek and Manchak (1996)(12) analyze algorithms for temporal reasoning. There are significant commonalities between the event-based ontology given in this chapter and an analysis of events due to the philosopher Donald Davidson (1980)(13) (>Events/Davidson).
The histories in Pat Hayes’s (1985a)(14) ontology of liquids and the chronicles in McDermott’s (1985)(15) theory of plans were also important influences on the field (…).
1. Kowalski, R. and Sergot, M. (1986). A logic-based calculus of events. New Generation Computing,
2. Sadri, F. and Kowalski, R. (1995). Variants of the event calculus. In ICLP-95, pp. 67–81.
3. Shanahan, M. (1997). Solving the Frame Problem. MIT Press
4. Shanahan, M. (1999). The event calculus explained. In Wooldridge, M. J. and Veloso, M. (Eds.), Artificial Intelligence Today, pp. 409–430. Springer-Verlag.
5. Mueller, E. T. (2006). Commonsense Reasoning. Morgan Kaufmann.
6. van Lambalgen, M. and Hamm, F. (2005). The Proper Treatment of Events. Wiley-Blackwell.
7. Thielscher, M. (1999). From situation calculus to fluent calculus: State update axioms as a solution to the inferential frame problem. AIJ, 111(1–2), 277-299.
8. Allen, J. F. (1984). Towards a general theory of action and time. AIJ, 23, 123–154
9. Ladkin, P. (1986a). Primitives and units for time specification. In AAAI-86, Vol. 1, pp. 354–359.
10. Ladkin, P. (1986b). Time representation: a taxonomy of interval relations. In AAAI-86, Vol. 1, pp.
11. Allen, J. F. (1991). Time and time again: The many ways to represent time. Int. J. Intelligent systems, 6, 341-355
12. van Beek, P. and Manchak, D. (1996). The design and experimental analysis of algorithms for temporal reasoning. JAIR, 4, 1–18.
13. Davidson, D. (1980). Essays on Actions and Events. Oxford University Press.
14. Hayes, P. J. (1985a). Naive physics I: Ontology for liquids. In Hobbs, J. R. andMoore, R. C. (Eds.), Formal Theories of the Commonsense World, chap. 3, pp. 71–107. Ablex.
15. McDermott, D. (1985). Reasoning about plans. In Hobbs, J. and Moore, R. (Eds.), Formal theories of the commonsense world. Intellect Books._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition.
Stuart J. Russell
Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010