Reasoning about Probabilities in Unbounded First-Order Dynamical Domains
By Vaishak Belle, Gerhard Lakemeyer. 2017
28 citations recorded by the source corpus.
When it comes to robotic agents operating in an uncer- tain world, a major concern in knowledge representation is to better relate high-level logical accounts of belief and action to the low-level probabilistic sensorimotor data. Perhaps the most general formalism for dealing with de- grees of belief and, in particular, how such beliefs should evolve in the presence of noisy sensing and acting is the account by Bacchus, Halpern, and Levesque. In this paper, we reconsider that model of belief, and pro- pose a new logical variant that has much of the expressive power of the original, but goes beyond it in novel ways. In particular, by moving to a semantical account of a modal variant of the situation calculus based on pos- sible worlds with unbounded domains and probabilistic distributions over them, we are able to capture the beliefs of a fully introspective knowledge base with uncertainty by way of an only-believing operator. The paper intro- duces the new logic and discusses key properties as well as examples that demonstrate how the beliefs of a knowl- edge base change as a result of noisy actions.
Subfields: Artificial Intelligence
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Citations in the corpus
- Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, background): “By defining epistemic operators that are interpreted over these worlds, we can talk about meta-beliefs over infinite histories [Belle and Lakemeyer, 2017].”