Antibiotics Time Machine is NP-hard

TL;DR

This paper proves that designing optimal antibiotic treatment plans using the antibiotics time machine model is computationally NP-hard, indicating no efficient solution exists for the general case.

Contribution

The paper establishes the NP-hardness of the antibiotics time machine problem, a variation of Markov decision processes, highlighting computational complexity challenges.

Findings

Proves NP-hardness of the antibiotics time machine problem

Shows computational difficulty in designing optimal antibiotic treatments

Highlights limitations of efficient algorithms for this problem

Abstract

The antibiotics time machine is an optimization question posed by Mira \latin{et al.} on the design of antibiotic treatment plans to minimize antibiotic resistance. The problem is a variation of the Markov decision process. These authors asked if the problem can be solved efficiently. In this paper, we show that this problem is NP-hard in general.