A Flexible Approach for Finding Optimal Paths with Minimal Conflicts

TL;DR

This paper introduces a scalable method combining theorem proving and constraint solving to find optimal, minimally conflicting paths in complex models, inspired by healthcare medication conflict detection.

Contribution

It presents a novel integrated approach using Isabelle and Z3 to identify optimal paths with minimal conflicts in systems where total consistency cannot be guaranteed.

Findings

Efficiently finds paths with minimal conflicts in complex models.

Scalable approach applicable to healthcare medication conflict detection.

Demonstrates practical utility in real-world healthcare scenarios.

Abstract

Complex systems are usually modelled through a combination of structural and behavioural models, where separate behavioural models make it easier to design and understand partial behaviour. When partial models are combined, we need to guarantee that they are consistent, and several automated techniques have been developed to check this. We argue that in some cases it is impossible to guarantee total consistency, and instead we want to find execution paths across such models with minimal conflicts with respect to a certain metric of interest. We present an efficient and scalable solution to find optimal paths through a combination of the theorem prover Isabelle with the constraint solver Z3. Our approach has been inspired by a healthcare problem, namely how to detect conflicts between medications taken by patients with multiple chronic conditions, and how to find preferable alternatives automatically.