Reliable MAS performance prediction using queueing models

TLDR

MAS survivability in military logistics is challenged by dynamic battlefield stresses, requiring mechanisms to distribute knowledge for sustained performance. The study models a MAS using TechSpecs to predict performance and enable recovery from damages through knowledge distribution. A queueing model of the agent network, built from TechSpecs and implemented in Cougaar, allows dynamic real‑time development and analysis of analytical and simulation models. The models show strong correlation between simulated and experimental events, validating the approach.

Abstract

In this paper, we model a multi-agent system (MAS) in military logistics based on the systemic specifications of the capabilities and attributes of individual agents (TechSpecs). Assuring the survivability of the MAS that implements distributed planning and execution is a significant design-time and run-time challenge. Dynamic battlefield stresses in military logistics range from heavy computational loads (information warfare) to being destructive to infrastructure. In order to sustain and recover from damages to continuously deliver performance, a mechanism that distributes knowledge about the capabilities and strategies of the system is crucial. Using a queueing model to represent the network of distributed agents, strategies are developed for a prototype military logistics system. The TechSpecs contain the capabilities of the agents, play-books or rules, quantities to monitor, types of information flow (input/output), measures of performance (quality of service) and their computation methods, measurement points, defenses against stresses and configuration details (to reflect command and control structure as well as task flow). With these details, models could be dynamically developed and analyzed in real-time for fine-tuning the system. Using a Cougaar (DARPA agent framework) based model for initial parameter estimation and analysis, we obtain an analytical and a simulation model and extract generic results. Results indicate strong correlation between experimental and actual events in the agent society.