Kill Webs by Collaborative & Self-organizing Agents (CSOAs)

Abstract

A single agent represents a single system capable of ingesting local data, indexing, cataloging information, performing knowledge pattern discovery, and separating patterns and anomalies from data. Multiple agents work collaboratively in a peer-to-peer network. Each agent has a peer list. Such multiple agents' collaboration can be modeled as cooperative games. Each agent optimizes its own objective locally. We show that each agent self-organizes or converges to its best value and the whole agent network achieves the best social welfare based on both the quantum adiabatic evolution transformation (QAET), and quantum intelligence game (QIG) or the QAET-QIG framework. We apply the QAET-QIG framework to the kill web concept that can potentially improve the traditional kill chain process or the find, fix, track, target, engage, and assess (F2T2EA) process. The improvement is measured in the values of powerful global optimization, distributed lethality, and load balancing. We show a use case of the QAET-QIG frame in a potential application of mixed sensors, platforms, weapons, and effects.