Self-organizing and Load-Balancing via Quantum Intelligence Game for Peer-to-Peer Collaborative Learning Agents and Flexible Organizational Structures

Abstract

Military operations, particularly in the littorals, have been contested and challenging. It is imperative to develop tools and methods to help tactical units execute distributed operations. Distributed, collaborative and networked agents have been associated with peer-to-peer models. The military operation applications require each tactical unit to prioritize requests, recommend content and services to balance the load of a whole peer-to-peer network without the total knowledge and communication with the whole network, therefore, effectively reduce the operation signatures of individual agents and avoid the detection by the adversaries. This objective can not be achieved using the traditional collaborative filters or distributed bandit algorithms. We show innovative collaborative learning agents (CLAs) applied to distributed operations. In a distributed operation, each unit or node is represented as a single CLA. A unit can be a knowledge supplier (e.g., capability or service), or knowledge consumer (e.g., a demand or request from its peer units or environment). When a unit receives a new request, it searches its peer network for the best match to fulfil the request, meanwhile the whole network constantly self-organizes and balances the word load of the nodes, lowers the signatures, and avoids detection. By employing CLAs, we first map the need of lowering operation signatures to a load-balancing problem, then apply lexical link analysis (LLA) and principle of quantum entanglement and superposition into a framework of LLA quantum intelligence game (LLAQIG). LLAQIG optimizes the value of an agent itself and in the same time helps a peer network achieve the Nash equilibrium and attain the optimal total social welfare. We show a use case and data set for distributed transportation units to handle transportation movement requests. We demonstrate that the resulted newly formed peer groups using LLAQIG, which have the characteristics of smaller load mean range between peer groups, lower load standard deviation within groups, higher number of unique equipment utilized than other methods and random configurations. These discovered metrics indicate new peer groups are less likely to be detected with less movement of units within peer groups, therefore maintaining lower operation signatures. The military tactical units can potentially leverage the results for flexible command and control (C2), organizational structures, and modernization. A peer-to-peer military distributed operation equipped with CLAs allow units, with traditional warfare capabilities of sensors, platforms, networks, weapons, and emerging technologies, optimize their value or load in an autonomous and self-organizing fashion with smaller footprint.