HUGIN: a shell for building Bayesian belief universes for expert systems

TLDR

Causal probabilistic networks effectively model domains with causal relationships and propagate uncertainty. This paper presents the HUGIN shell, an implementation for managing domain models expressed as causal probabilistic networks. The HUGIN shell constructs an interactive graph of nodes and arcs, enforces acyclicity, and converts the network into a junction tree for efficient inference. The shell’s effectiveness is demonstrated through a genetic breeding example and the MUNIN electromyography expert system, showing efficient belief propagation and evidence integration.

Abstract

Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN shell - for handling a domain model expressed by a causal probabilistic network. The only topological restriction imposed on the network is that, it must not contain any directed loops. The approach is illustrated step by step by solving a genetic breeding problem. A graph representation of the domain model is interactively created by using instances of the basic network components-- nodes and arcs--as building blocks. This structure, together with the quantitative relations between nodes and their immediate causes expressed as conditional probabilities, are automatically transformed into a tree structure, a junction tree. Here a computationally efficient and conceptually simple algebra of Bayesian belief universes supports incorporation of new evidence, propagation of information, and calculation of revised beliefs in the states of the nodes in the network. Finally, as an exam ple of a real world application, MUNIN an expert system for electromyography is discussed.