Showing papers by "Hector J. Levesque published in 2018"

Reasoning about Discrete and Continuous Noisy Sensors and Effectors in Dynamical Systems

Abstract: Among the many approaches for reasoning about degrees of belief in the presence of noisy sensing and acting, the logical account proposed by Bacchus, Halpern, and Levesque is perhaps the most expressive. While their formalism is quite general, it is restricted to fluents whose values are drawn from discrete finite domains, as opposed to the continuous domains seen in many robotic applications. In this work, we show how this limitation in that approach can be lifted. By dealing seamlessly with both discrete distributions and continuous densities within a rich theory of action, we provide a very general logical specification of how belief should change after acting and sensing in complex noisy domains.

Specifying Plausibility Levels for Iterated Belief Change in the Situation Calculus.

Abstract: We investigate augmenting a theory of belief and actions with qualitative plausibility levels. Shapiro et al. created a framework for modeling iterated belief revision and update which integrated those features with the well-developed theory of action in the situation calculus. However, applying their technique requires associating plausibility levels with initial situations, for which no very convenient mechanism had been proposed. Schwering and Lakemeyer proposed deriving these initial plausibility levels from a set of conditionals, similarly to how models are ranked in Pearl’s System Z. However, their approach inherits some limitations of System Z. We consider alternatives, and argue that a perspicuous approach is to measure plausibility by counting the abnormalities in a situation (similarly to cardinality-based circumscription). By allowing abnormalities to change over time, we can also model changing plausibility levels in a natural and simple way, which gives us a flexible approach for handling belief change about predicted and unpredicted exogenous actions.