Intelligence Semantics

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Extra info for Artificial Intelligence Planning Systems. Proceedings of the First Conference (AIPS 92)

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Achieving several goals simulaneously. In Elcock, E. , editors, Machine Intelligence 8, pages 94-136. Ellis Horwood, Edinburgh. , 1990). edu Abstract Action models used in planning systems must necessarily be abstractions of reality. It is therefore natural to include estimates of ignorance and uncertainty as part of an action model. The standard approach of assigning a unique probability distribution over possible outcomes fares poorly in the presence of abstraction because many unmodeled variables are not governed by pure random chance.

Then sured, they are likely to be quite different from the rewriting the sum in (7): predicted distribution (one example of this discrepn ancy actually occurring appears in [Christiansen and Bel(A\Bel ) = £ £ (A\{bi} U B)m({6«} U B) Goldberg, 1990]). On the other hand, the lower probai=l BCBi bility approach gives very loose bounds in this case, efn fectively admitting that it doesn't know precisely what to expect. It is important to realize that the actual ac= £*e/(,l|ft() ^ m({6,}UB) curacy is equivalent in the two cases, being determined i=l BCB; not by the formalism, but by the level of abstraction in the models.

In DARPA Workshop on Innovative Approaches to Planning, Scheduling, and Control, San Diego, CA, November 1990. [Drummond and Bresina, 1990] Mark Drummond and John Bresina. Anytime synthetic projection: Maximizing the probability of goal satisfaction. In Eight National Conference on Artificial Intelligence, July 1990. [Fagin and Halpern, 1989] Ronald Fagin and Joseph Y. Halpern. A new approach to updating beliefs. Technical Report RJ 7222, IBM, May 1989. [Good, 1962] I. J. Good. Subjective probability as the measure of a non-measurable set.

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