  This work proposes action networks as a semantically well-founded framework
for reasoning about actions and change under uncertainty. Action networks add
two primitives to probabilistic causal networks: controllable variables and
persistent variables. Controllable variables allow the representation of
actions as directly setting the value of specific events in the domain, subject
to preconditions. Persistent variables provide a canonical model of persistence
according to which both the state of a variable and the causal mechanism
dictating its value persist over time unless intervened upon by an action (or
its consequences). Action networks also allow different methods for quantifying
the uncertainty in causal relationships, which go beyond traditional
probabilistic quantification. This paper describes both recent results and work
in progress.
