Knowledge, action, and the frame problem

By R. Scherl, Hector J. Levesque. Artificial Intelligence, 2003

321 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bb850443ddf97e514e14f4e448b7192f5ce025ca

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In Artificial Intelligence

Citations in the corpus

A logical theory of robot localization (2014, background): “The BHL model of belief enriches the standard situation calculus to reason about noisy sensors and belief change, by building on a treatment of knowledge by Scherl and Levesque [37].”
Reasoning about Probabilities in Dynamic Systems using Goal Regression (2013, background): “Ours, on the other hand, continues in the tradition of the epistemic situation calculus (Scherl and Levesque, 2003) by extending those intuitions to probabilistic belief and noisy sensing.”
Complex Epistemic Modalities in the Situation Calculus (2008, methodology): “For any epistemic path π , uniform formula φ and set of actions c : The mechanics of this proof mirror the corresponding proof from (Scherl & Levesque 2003): we expand the PKnows macro, apply Theorem 2 as a successor state axiom for KDo , re-arrange to eliminate existential quantiﬁers, then collect…”
Multiagent Only Knowing in Dynamic Systems (2014, methodology): “Roughly speaking, these correspond to the class of regressable formulas in the epistemic situation calculus (Scherl & Levesque, 2003).”
Semantical considerations on multiagent only knowing (2015, methodology): “This intuition has encouraged extensions of OL towards the design of practical cognitive agents [40,42,43], by introducing the appropriate variants of K and O in the language of the situation calculus [41,44,45].”
The Situation Calculus: A Case for Modal Logic (2010, methodology): “To this end, Scherl and Levesque [46] came up with a successor state axiom for K, which in its simplest form looks like this: K(s′′, do(a, s)) ≡ ∃s′.s′′ = do(a, s′) ∧K(s′, s) ∧ Poss(a, s′).”
How to Progress Beliefs in Continuous Domains (2014, background): “It is easy to see that this framework generalizes the categorical knowledge treatment of (Scherl and Levesque 2003).”