A Structured, Probabilistic Representation of Action

TL;DR

This paper introduces a probabilistic framework using belief networks to explicitly represent and reason about uncertainty in states and actions for planning in uncertain environments.

Contribution

It develops conditional belief networks (CBNs) to model probabilistic dependencies of action effects and environmental relationships, enabling explicit uncertainty reasoning in planning.

Findings

Probabilistic representation improves handling of uncertainty in planning.

A simple projection algorithm constructs successor state belief networks.

CBNs are suitable for various stages of the planning process.

Abstract

When agents devise plans for execution in the real world, they face two important forms of uncertainty: they can never have complete knowledge about the state of the world, and they do not have complete control, as the effects of their actions are uncertain. While most classical planning methods avoid explicit uncertainty reasoning, we believe that uncertainty should be explicitly represented and reasoned about. We develop a probabilistic representation for states and actions, based on belief networks. We define conditional belief nets (CBNs) to capture the probabilistic dependency of the effects of an action upon the state of the world. We also use a CBN to represent the intrinsic relationships among entities in the environment, which persist from state to state. We present a simple projection algorithm to construct the belief network of the state succeeding an action, using the environment CBN model to infer indirect effects. We discuss how the qualitative aspects of belief networks and CBNs make them appropriate for the various stages of the problem solving process, from model construction to the design of planning algorithms.