Probabilistic Reasoning as Information Compression by Multiple Alignment, Unification and Search: An Introduction and Overview

TL;DR

This paper presents a novel framework viewing probabilistic reasoning as information compression achieved through multiple alignment, unification, and search, supported by a software model demonstrating various reasoning tasks.

Contribution

It introduces the ICMAUS framework and a software model, SP61, to unify different types of probabilistic reasoning under a common information compression perspective.

Findings

The SP61 model effectively discovers good multiple alignments based on information compression.

The ICMAUS framework can model diverse probabilistic reasoning processes.

Demonstrates the applicability of the framework to Bayesian networks and default reasoning.

Abstract

This article introduces the idea that probabilistic reasoning (PR) may be understood as "information compression by multiple alignment, unification and search" (ICMAUS). In this context, multiple alignment has a meaning which is similar to but distinct from its meaning in bio-informatics, while unification means a simple merging of matching patterns, a meaning which is related to but simpler than the meaning of that term in logic. A software model, SP61, has been developed for the discovery and formation of 'good' multiple alignments, evaluated in terms of information compression. The model is described in outline. Using examples from the SP61 model, this article describes in outline how the ICMAUS framework can model various kinds of PR including: PR in best-match pattern recognition and information retrieval; one-step 'deductive' and 'abductive' PR; inheritance of attributes in a class hierarchy; chains of reasoning (probabilistic decision networks and decision trees, and PR with 'rules'); geometric analogy problems; nonmonotonic reasoning and reasoning with default values; modelling the function of a Bayesian network.