# Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis

## TL;DR

This paper introduces a cost-effective, optimized method for sequential model-based diagnosis that efficiently computes near-optimal measurement queries without inference engine calls, suitable for large and complex problems.

## Contribution

It presents a novel approach for measurement selection that decouples optimization dimensions and reduces search space, enabling fast, near-optimal query computation without inference engine calls.

## Key findings

- Computes nearly optimal queries instantly regardless of problem size.
- Requires only polynomial inferences, improving efficiency.
- Outperforms existing methods in real-world problem evaluations.

## Abstract

In this work we present strategies for (optimal) measurement selection in model-based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and optimized along two dimensions: expected number of queries and cost per query. By means of a suitable decoupling of two optimizations and a clever search space reduction the computations are done without any inference engine calls. For the full search space, we give a method requiring only a polynomial number of inferences and guaranteeing query properties existing methods cannot provide. Evaluation results using real-world problems indicate that the new method computes (virtually) optimal queries instantly independently of the size and complexity of the considered diagnosis problems.