Emergent Intelligence via Self-Organization in a Group of Robotic Devices

Abstract

Networked systems control is a known problem complicated because of the need to work with large groups of elementary agents. In many applications, it is impossible (or difficult) to validate agent movement models and provide sufficiently reliable control actions at the elementary system components level. The evolution of agent subgroups (clusters) leads to additional uncertainty in the studied control systems. We focus on new decentralized control methods based on local communications in complex multiagent dynamical systems. The problem of intelligence in a complex world is considered in connection to multiagent network systems, including a system named airplane with feathers, load balancing, and the multisensor-multitarget tracking problem. Moreover, the new result concerning the emergency of intelligence in a group of robots is provided. All these methods follow the paradigm of the direct reaction of each element (agent) of the system to its sensory data of current situation observations and the corresponding data from a limited number of its neighbors (local communications). At the same time, these algorithms achieve a mutual goal at the macro level. All of the considered emergent intelligence appearances inspire the necessity to “rethink” the previously recognized concepts of computability and algorithm in computer science.

Emergent Intelligence in Smart Ecosystems: Conflicts Resolution by Reaching Consensus in Resource Management

“…In [26], EI is applied to routing packages through mobile Wi-Fi networks. In [27,28], EI is applied for distributed stochastic optimization and managing a swarm of mobile robots. In the case of optimization, it is distributed sequential subspace optimization where coordination is considered among neighbors.…”

Multiagent Control of Airplane Wing Stability with “Feathers” under the Flexural Torsional Flutter

“…Multiagent systems have many applications in civilian, security, and military areas [32,33]. A centralized approach to quantitative and qualitative modeling, analysis, constraint satisfaction, maintenance, and control seem to be too strict for these systems [34]. On the other hand, the distributed and incremental reasoning seems to be more scalable, robust and flexible.…”

“…The multiagent methodology can serve the general model of the interactions in a complex system [37][38][39]. An overview of publications considering the emergent intelligence and selforganization in groups of devices is provided in [34].…”