Efficient Communication and Coordination for Large-Scale Multi-Agent Systems

The growth of the computational power of computers and the speed of networks has made large-scale multi-agent systems a promising technology. As the number of agents in a single application approaches thousands or millions, distributed computing has become a general paradigm in large-scale multi-agent systems to take the benefits of parallel computing. However, since these numerous agents are located on distributed computers and interact intensively with each other to achieve common goals, the agent communication cost significantly affects the performance of applications. Therefore, optimizing the agent communication cost on distributed systems could considerably reduce the runtime of multi-agent applications. Furthermore, because static multi-agent frameworks may not be suitable for all kinds of applications, and the communication patterns of agents may change during execution, multi-agent frameworks should adapt their services to support applications differently according to their dynamic characteristics. This thesis proposes three adaptive services at the agent framework level to reduce the agent communication and coordination cost of large-scale multi-agent applications. First, communication locality-aware agent distribution aims at minimizing inter-node communication by collocating heavily communicating agents on the same platform and maintaining agent group-based load sharing. Second, application agent-oriented middle agent services attempt to optimize agent interaction through middle agents by executing application agent-supported search algorithms on the middle agent address space. Third, message passing for mobile agents aims at reducing the time of message delivery to mobile agents using location caches or by extending the agent address scheme with location information. With these services, we have achieved very impressive experimental results in large- scale UAV simulations including up to 10,000 agents. Also, we have provided a formal definition of our framework and services with operational semantics

Distributed AdaptiveFault-Tolerant Control of Uncertain Multi-Agent Systems

This brief paper presents a distributed adaptive fault-tolerant leader-following consensus control scheme for a class of nonlinear uncertain multi-agent systems under a bidirectional communication topology with possibly asymmetric weights and subject to process and actuator faults. A local fault-tolerant control (FTC) component is designed for each agent using local measurements and suitable information exchanged between neighboring agents. Each local FTC component consists of a fault diagnosis module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault-tolerant controllers activated after fault detection and after fault isolation, respectively. By using an appropriately chosen Lyapunov function, the closed-loop stability and asymptotic convergence property of leader–follower consensus are rigorously established under different operating modes of the FTC system

Event-triggered output consensus for linear multi-agent systems via adaptive distributed observer

summary:This paper investigates the distributed event-triggered cooperative output regulation problem for heterogeneous linear continuous-time multi-agent systems (MASs). To eliminate the requirement of continuous communication among interacting following agents, an event-triggered adaptive distributed observer is skillfully devised. Furthermore, a class of closed-loop estimators is constructed and implemented on each agent such that the triggering times on each agent can be significantly reduced while at the same time the desired control performance can be preserved. Compared with the existing open-loop estimators, the proposed estimators can provide more accurate state estimates during each triggering period. It is further shown that the concerned cooperative output regulation problem can be effectively resolved under the proposed control scheme and the undesirable Zeno behavior can be excluded. Finally, the effectiveness of the proposed results is verified by numerical simulations

The Simulation Model Partitioning Problem: an Adaptive Solution Based on Self-Clustering (Extended Version)

This paper is about partitioning in parallel and distributed simulation. That means decomposing the simulation model into a numberof components and to properly allocate them on the execution units. An adaptive solution based on self-clustering, that considers both communication reduction and computational load-balancing, is proposed. The implementation of the proposed mechanism is tested using a simulation model that is challenging both in terms of structure and dynamicity. Various configurations of the simulation model and the execution environment have been considered. The obtained performance results are analyzed using a reference cost model. The results demonstrate that the proposed approach is promising and that it can reduce the simulation execution time in both parallel and distributed architectures