Multi-Attribute Queries for Stochastic Multi Agent Systems over Short Time Horizons

Abstract

Statistical Model Checking (SMC) for the analysis of Multi-Agent Systems has been studied in the recent past. A feature peculiar to Multi-Agent Systems in the context of Statistical Model Checking is that of aggregate queries–temporal logic formula that involve a large number of agents. To answer such queries through Monte Carlo sampling, the statistical approach to model checking simulates the entire agent population and evaluates the query. This makes the simulation overhead significantly higher than the query evaluation overhead. To alleviate this problem, one strategy is to choose only a subset of the agents to simulate, through sampling. Further, this problem becomes particularly challenging when the model checking queries involve multiple attributes of the agents. We propose a population sampling algorithm that simulates only a subset of all the agents and scales to multiple attributes, thus making the solution generic. The population sampling approach results in increased efficiency (a gain in running time of 50% to 100% in most experiments) for a marginal loss in accuracy (between 1% to 5% in most experiments), especially for queries that involve limited time horizons.