Census Signal Temporal Logic Inference for Multi-Agent Group Behavior Analysis

TL;DR

This paper introduces CensusSTL, a new logic framework for analyzing multi-agent group behavior by inferring temporal logic formulas from trajectory data, focusing on subgroup dynamics and task completion.

Contribution

The paper proposes a novel CensusSTL framework and inference algorithm to analyze multi-agent group behaviors, including subgroup identification and behavior inference from trajectory data.

Findings

Successfully inferred CensusSTL formulas from soccer match data.

Demonstrated subgroup behavior analysis using similarity and complementarity approaches.

Validated the effectiveness of CensusSTL in real-world multi-agent scenarios.

Abstract

In this paper, we define a novel census signal temporal logic (CensusSTL) that focuses on the number of agents in different subsets of a group that complete a certain task specified by the signal temporal logic (STL). CensusSTL consists of an "inner logic" STL formula and an "outer logic" STL formula. We present a new inference algorithm to infer CensusSTL formulae from the trajectory data of a group of agents. We first identify the "inner logic" STL formula and then infer the subgroups based on whether the agents' behaviors satisfy the "inner logic" formula at each time point. We use two different approaches to infer the subgroups based on similarity and complementarity, respectively. The "outer logic" CensusSTL formula is inferred from the census trajectories of different subgroups. We apply the algorithm in analyzing data from a soccer match by inferring the CensusSTL formula for different subgroups of a soccer team.