A Framework for Control Strategies in Uncertain Inference Networks

TL;DR

This paper develops and analyzes control strategies for hierarchical probabilistic inference networks, introducing the Depth Vector concept to optimize decision-making in uncertain inference tasks.

Contribution

It introduces the Depth Vector concept and formalizes control strategies using staged look-ahead and subgoal focus for hierarchical inference networks.

Findings

Strategies vary based on Depth Vector and node consideration

INFERENTI system simulates and compares strategy performance

Control strategies improve inference efficiency in uncertain networks

Abstract

Control Strategies for hierarchical tree-like probabilistic inference networks are formulated and investigated. Strategies that utilize staged look-ahead and temporary focus on subgoals are formalized and refined using the Depth Vector concept that serves as a tool for defining the 'virtual tree' regarded by the control strategy. The concept is illustrated by four types of control strategies for three-level trees that are characterized according to their Depth Vector, and according to the way they consider intermediate nodes and the role that they let these nodes play. INFERENTI is a computerized inference system written in Prolog, which provides tools for exercising a variety of control strategies. The system also provides tools for simulating test data and for comparing the relative average performance under different strategies.