Bounded Situation Calculus Action Theories

September 07, 2015 Β· Declared Dead Β· πŸ› Artificial Intelligence

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Authors Giuseppe De Giacomo, Yves LespΓ©rance, Fabio Patrizi arXiv ID 1509.02012 Category cs.AI: Artificial Intelligence Citations 40 Venue Artificial Intelligence Last Checked 4 months ago
Abstract
In this paper, we investigate bounded action theories in the situation calculus. A bounded action theory is one which entails that, in every situation, the number of object tuples in the extension of fluents is bounded by a given constant, although such extensions are in general different across the infinitely many situations. We argue that such theories are common in applications, either because facts do not persist indefinitely or because the agent eventually forgets some facts, as new ones are learnt. We discuss various classes of bounded action theories. Then we show that verification of a powerful first-order variant of the mu-calculus is decidable for such theories. Notably, this variant supports a controlled form of quantification across situations. We also show that through verification, we can actually check whether an arbitrary action theory maintains boundedness.
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