# Iterated Belief Revision Under Resource Constraints: Logic as Geometry

## TL;DR

This paper introduces the universal memory architecture (UMA), a geometry-based belief revision method for resource-constrained settings like mobile robots, offering computational efficiency and model comparison capabilities.

## Contribution

It develops the formalism of UMA, linking inference to geometry via duality, and analyzes its complexity, learning guarantees, and practical viability through simulations.

## Key findings

- UMA reduces computational costs in belief revision.
- The duality framework enables model space comparisons.
- Simulation results demonstrate UMA's practical effectiveness.

## Abstract

We propose a variant of iterated belief revision designed for settings with limited computational resources, such as mobile autonomous robots. The proposed memory architecture---called the {\em universal memory architecture} (UMA)---maintains an epistemic state in the form of a system of default rules similar to those studied by Pearl and by Goldszmidt and Pearl (systems $Z$ and $Z^+$). A duality between the category of UMA representations and the category of the corresponding model spaces, extending the Sageev-Roller duality between discrete poc sets and discrete median algebras provides a two-way dictionary from inference to geometry, leading to immense savings in computation, at a cost in the quality of representation that can be quantified in terms of topological invariants. Moreover, the same framework naturally enables comparisons between different model spaces, making it possible to analyze the deficiencies of one model space in comparison to others. This paper develops the formalism underlying UMA, analyzes the complexity of maintenance and inference operations in UMA, and presents some learning guarantees for different UMA-based learners. Finally, we present simulation results to illustrate the viability of the approach, and close with a discussion of the strengths, weaknesses, and potential development of UMA-based learners.