Census Signal Temporal Logic Inference for Multi-Agent Group Behavior Analysis
This work addresses behavior analysis for multi-agent systems like sports teams, but it is incremental as it builds on existing signal temporal logic.
The paper tackles the problem of analyzing multi-agent group behavior by defining Census Signal Temporal Logic (CensusSTL) to count agents completing tasks, and presents a new inference algorithm to derive these formulae from trajectory data, applying it to soccer match data.
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.