Improving the Scalability of Multi-Agent Systems

There is an increasing demand for designers and developers to construct ever larger multi-agent systems. Such systems will be composed of hundreds or even thousands of autonomous agents. Moreover, in open and dynamic environments, the number of agents in the system at any one time will fluctuate significantly. To cope with these twin issues of scalability and variable numbers, we hypothesize that multi-agent systems need to be both /self-building/ (able to determine the most appropriate organizational structure for the system by themselves at run-time) and /adaptive/ (able to change this structure as their environment changes). To evaluate this hypothesis we have implemented such a multi-agent system and have applied it to the domain of automated trading. Preliminary results supporting the first part of this hypothesis are presented: adaption and self-organization do indeed make the system better able to cope with large numbers of agents

On MAS Scalability

In open dynamic multi-agent environments the number of agents can vary significantly within very short periods of time. Very few (if any) current multi-agent systems have, however, been designed to cope with large-scale distributed applications. Scalability requires increasing numbers of new agents and resources to have no noticeable effect on performance nor to increase administrative complexity. In this paper a number of implications for techniques and management are discussed. Current research on agent middleware is briefly described.

Centralized Versus Decentralized Team Coordination Using Dynamic Scripting

Computer generated forces (CGFs) must display realistic behavior for tactical training simulations to yield an effective training experience. Tradionally, the behavior of CGFs is scripted. However, there are three drawbacks, viz. (1) scripting limits the adaptive behavior of CGFs, (2) creating scripts is difficult and (3) it requires scarce domain expertise. A promising machine learning technique is the dynamic scripting of CGF behavior. In simulating air combat scenarios, team behavior is important, both with and without communication. While dynamic scripting has been reported to be effective in creating behavior for single fighters, it has not often been used for team coordination. The dynamic scripting technique is sufficiently flexible to be used for different team coordination methods. In this paper, we report the first results on centralized coordination of dynamically scripted air combat teams, and compare these results to a decentralized approach from earlier work. We find that using the centralized approach leads to higher performance and more efficient learning, although creativity of the solutions seems bounded by the reduced complexity

DESIGNING A FRAMEWORK FOR RESTFUL MULTI-AGENT SYSTEMS

Nowadays there are many systems that require some degree of automation. To attain this automation, agent technology has generally been found to be a promising approach. An agent is a piece of software that does activities on behalf of a user or another program. However, designing and deploying an agent infrastructure that achieves scalability is still a major challenge. In this thesis, a pattern for designing agents following RESTful principles is proposed in an effort to address the aforementioned challenges. In addition, the pattern will follow the FIPA Abstract Architecture; which is aimed at developing intelligent agents and supporting interoperability among agents and agent-based systems. Furthermore, an evaluation is done to investigate the scalability of the deployment of a RESTful multi-agent system

A reconfigurable distributed multiagent system optimized for scalability

This thesis proposes a novel solution for optimizing the size and communication overhead of a distributed multiagent system without compromising the performance. The proposed approach addresses the challenges of scalability especially when the multiagent system is large. A modified spectral clustering technique is used to partition a large network into logically related clusters. Agents are assigned to monitor dedicated clusters rather than monitor each device or node. The proposed scalable multiagent system is implemented using JADE (Java Agent Development Environment) for a large power system. The performance of the proposed topology-independent decentralized multiagent system and the scalable multiagent system is compared by comprehensively simulating different fault scenarios. The time taken for reconfiguration, the overall computational complexity, and the communication overhead incurred are computed. The results of these simulations show that the proposed scalable multiagent system uses fewer agents efficiently, makes faster decisions to reconfigure when a fault occurs, and incurs significantly less communication overhead. The proposed scalable multiagent system has been coupled with a scalable reconfiguration algorithm for an electric power system attempting to minimize the number of switch combination explored for reconfiguration. The reconfiguration algorithm reconfigures a power system while maintaining bus voltages within limits specified by constraints