Title: Reasoning about
actions in a probabilistic setting
Authors: Chitta Baral, Nam Tran and Le-Chi Tuan
Abstract
In this paper we present a language to reason about actions in a
probabilistic setting and compare our work with earlier work by
Pearl.
The main feature of our language is its use of static and dynamic
causal laws, and use of unknown (or background) variables -- whose
values are determined by factors beyond our model -- in
incorporating probabilities. We use two kind of unknown variables:
inertial and non-inertial. Inertial unknown variables are helpful
in assimilating observations and modeling counterfactuals and
causality; while non-inertial unknown variables help characterize
stochastic behavior, such as the outcome of tossing a coin, that
are not impacted by observations. Finally, we give a glimpse of
incorporating probabilities into reasoning with narratives.