Action Networks: A Framework for Reasoning about Actions and Change under Uncertainty

TL;DR

This paper introduces action networks, a probabilistic framework that models actions, change, and uncertainty using controllable and persistent variables, enhancing reasoning about dynamic systems.

Contribution

It presents a novel framework combining controllable and persistent variables within probabilistic causal networks for reasoning under uncertainty.

Findings

Action networks effectively model actions and change under uncertainty.

They incorporate controllable and persistent variables for better representation.

The framework allows diverse methods for quantifying causal uncertainty.

Abstract

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.