Action Networks: A Framework for Reasoning about Actions and Change under Uncertainty
This work addresses the challenge of representing and reasoning with actions in uncertain domains, which is incremental as it builds upon existing probabilistic causal networks.
The paper tackles the problem of reasoning about actions and change under uncertainty by proposing action networks, a framework that extends probabilistic causal networks with controllable and persistent variables to model actions and persistence, and it introduces methods for quantifying uncertainty beyond traditional probabilities.
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.