# Towards Optimizing Reiter's HS-Tree for Sequential Diagnosis

## TL;DR

This paper introduces DynamicHS, an improved variant of Reiter's HS-Tree for sequential diagnosis, which maintains state across iterations to reduce redundant computations and costly reasoner calls, leading to significant efficiency gains.

## Contribution

DynamicHS extends HS-Tree by preserving state during sequential diagnosis, avoiding redundant tree rebuilds and reducing reasoning costs.

## Key findings

- DynamicHS achieves significant time savings.
- Reduces expensive reasoner calls.
- Maintains all desirable properties of HS-Tree.

## Abstract

Reiter's HS-Tree is one of the most popular diagnostic search algorithms due to its desirable properties and general applicability. In sequential diagnosis, where the addressed diagnosis problem is subject to successive change through the acquisition of additional knowledge about the diagnosed system, HS-Tree is used in a stateless fashion. That is, the existing search tree is discarded when new knowledge is obtained, albeit often large parts of the tree are still relevant and have to be rebuilt in the next iteration, involving redundant operations and costly reasoner calls. As a remedy to this, we propose DynamicHS, a variant of HS-Tree that avoids these redundancy issues by maintaining state throughout sequential diagnosis while preserving all desirable properties of HS-Tree. Preliminary results of ongoing evaluations in a problem domain where HS-Tree is the state-of-the-art diagnostic method suggest significant time savings achieved by DynamicHS by reducing expensive reasoner calls.