# Signal Temporal Logic Task Decomposition via Convex Optimization

arXiv: https://arxiv.org/abs/2103.06047 · HTML: https://mlsanity.com/paper/2103.06047/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description](https://mlsanity.com/paper/2209.09114/) (2022)

## Abstract (arXiv)

In this paper we focus on the problem of decomposing a global Signal Temporal Logic formula (STL) assigned to a multi-agent system to local STL tasks when the team of agents is a-priori decomposed to disjoint sub-teams. The predicate functions associated to the local tasks are parameterized as hypercubes depending on the states of the agents in a given sub-team. The parameters of the functions are, then, found as part of the solution of a convex program that aims implicitly at maximizing the volume of the zero level-set of the corresponding predicate function. Two alternative definitions of the local STL tasks are proposed and the satisfaction of the global STL formula is proven when the conjunction of the local STL tasks is satisfied.