A Multi-Agent Framework for Collaborative Engineering Design and Optimization

2004 ◽  
Author(s):  
Qi Hao ◽  
Weiming Shen ◽  
Zhan Zhang ◽  
Seong-Whan Park ◽  
Jai-Kyung Lee
Keyword(s):  
Engineering Design ◽  
Rapid Development ◽  
Autonomous Agent ◽  
Multi Agent Systems ◽  
Engineering Project ◽  
Development Environment ◽  
Collaborative Engineering ◽  
Heterogeneous Agent ◽  
Design And Optimization ◽  
Multi Agent

Agent technology is playing an increasingly important role in developing intelligent, distributed and collaborative applications. The innate difficulties of interoperation between heterogeneous agent communities and rapid construction of multi-agent systems have motivated the emergence of FIPA specifications and the proliferation of multi-agent system platforms or toolkits that implement FIPA specifications. In this paper, a FIPA compliant multi-agent framework called AADE (Autonomous Agent Development Environment) is presented. This framework, originating from the engineering fields, can facilitate the rapid development of collaborative engineering applications (especially in engineering design and manufacturing fields) through the provision of reusable packages of agent-level components and programming tools. An agent oriented engineering project on the development of an e-engineering design and optimization environment is designed and developed based on the facilities provided by the AADE framework.

Evolutionary Multi-Agent Systems: An Adaptive Approach to Optimization in Dynamic Environments

2008 ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Engineering Design ◽  
Dynamic Environments ◽  
Optimization Process ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Engineering Design Problems ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a team of autonomous software agents to be effective in unknown and changing optimization environments by evolving to use the most successful algorithms at the points in the optimization process where they will be the most effective. We present the core framework and methodology which has potential applications in layout, scheduling, manufacturing, and other engineering design areas. The communal agent team organizational structure employed allows cooperation of agents through the products of their work and creates an ever changing set of individual solutions for the agents to work on. In addition, the organizational structure allows the framework to be adaptive to changes in the design space that occur during the optimization process — making our approach extremely flexible to the kinds of dynamic environments encountered in engineering design problems. An evolutionary approach is used, but evolution occurs at the strategic, rather than solution level — where the strategies of agents in the team (the decisions for picking, altering, and inserting a solution) evolve over time. As an application of this approach, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, each agent running a different solution strategy, must evolve to apply the solution strategies which are most useful given the set at any point in the process. As a team, the evolutionary agents produce better solutions than any individual algorithm. We discuss the extensions to our preliminary work that will make our framework highly useful to the design and optimization community.

scholarly journals COMPOSITIONAL SPECIFICATION AND REUSE OF A GENERIC COOPERATIVE AGENT MODEL

2000 ◽  
Vol 09 (03) ◽  
pp. 171-207 ◽  
Author(s):  
FRANCES M. T. BRAZIER ◽  
FRANK CORNELISSEN ◽  
CATHOLIJN M. JONKER ◽  
JAN TREUR
Keyword(s):  
Engineering Design ◽  
Call Center ◽  
Generic Model ◽  
Multi Agent Systems ◽  
Collaborative Engineering ◽  
Agent Model ◽  
Development Method ◽  
Agent Systems ◽  
Multi Agent ◽  
Agent Cooperation

In this paper, one of the informally described models of agent cooperation (Jennings, 1995) has been used to develop and formally specify a generic model of a cooperative agent (GCAM). The compositional development method for multi-agent systems DESIRE supported the principled design of this model of cooperation. To illustrate reusability of the generic model, two application domains have been addressed: collaborative engineering design, and Call Center support.

Temporally Autonomous Agent Interaction

2011 ◽  
pp. 19-37
Author(s):  
Adam J. Conover ◽  
Robert J. Hammell
Keyword(s):  
Autonomous Agent ◽  
Multi Agent Systems ◽  
Agent Behavior ◽  
Simulated Environments ◽  
Interacting Agents ◽  
Agent Interaction ◽  
Multi Agent ◽  
Global Coordination ◽  
Traditional Approaches ◽  
Emergent Behaviors

This work reflects the results of continuing research into “temporally autonomous” multi-agent interaction. Many traditional approaches to modeling multi-agent systems involve synchronizing all agent activity in simulated environments to a single “universal” clock. In other words, agent behavior is regulated by a global timer where all agents act and interact deterministically in time. However, if the objective of any such simulation is to model the behavior of real-world entities, this discrete timing mechanism yields an artificially constrained representation of actual physical agent interaction. In addition to the behavioral autonomy normally associated with agents, simulated agents must also have temporal autonomy in order to interact realistically. Intercommunication should occur without global coordination or synchronization. To this end, a specialized simulation framework is developed. Several simulations are conducted from which data are gathered and we subsequently demonstrate that manipulation of the timing variable amongst interacting agents affects the emergent behaviors of agent populations.

scholarly journals Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6953
Author(s):  
Sabyasachi Mondal ◽  
Antonios Tsourdos
Keyword(s):  
Genetic Algorithm ◽  
Optimal Topology ◽  
Autonomous Agent ◽  
Multi Agent Systems ◽  
Consensus Protocol ◽  
Consensus Control ◽  
New Agents ◽  
Communication Topology ◽  
Multi Agent ◽  
The Stability

This paper presents an idea of how new agents can be added autonomously to a group of existing agents without changing the existing communication topology among them. Autonomous agent addition to existing Multi-Agent Systems (MASs) can give a strategic advantage during the execution of a critical beyond visual line-of-sight (BVLOS) mission. The addition of the agent essentially means that new connections with existing agents are established. It is obvious that the consensus control energy increases as the number of agent increases considering a specific consensus protocol. The objective of this work is to establish the new connections in a way such that the consensus energy increase due to the new agents is minimal. The updated topology, including new connections, must contain a spanning tree to maintain the stability of the MASs network. The updated optimal topology is obtained by solving minimum additional consensus control energy using the Two-Dimensional Genetic Algorithm. The results obtained are convincing.

Simulation tools for a smart grid and energy management for microgrid with wind power using multi-agent system

2019 ◽  
Vol 44 (6) ◽  
pp. 661-672
Author(s):  
Mohamed Azeroual ◽  
Tijani Lamhamdi ◽  
Hassan El Moussaoui ◽  
Hassane El Markhi
Keyword(s):  
Smart Grid ◽  
Wind Power ◽  
Storage System ◽  
Electrical Power ◽  
Electrical Network ◽  
Multi Agent Systems ◽  
Development Environment ◽  
Simulation Tools ◽  
Multi Agent ◽  
And Storage

The smart grid concept is predicated upon the pervasive use of advanced digital communication, information techniques, and artificial intelligence for the current power system to be more characteristics on the real-time monitoring and controlling of the supply/demand. Therefore, in recent years, researchers increasingly couple distinct simulators to form novel “co-simulations.” In this article, we will present a survey of different electrical power and communication simulators, a literature survey of 20 smart grid co-simulations frameworks, and the characteristics of each platform applicable in the intelligent electrical network. Finally, we proposed multi-agent systems for controlling the microgrid that consists of wind power and storage system using MACSimJX co-simulation that combines Simulink simulator and JADE (Java Agent Development Environment).

ADE — AN ARCHITECTURE DEVELOPMENT ENVIRONMENT FOR VIRTUAL AND ROBOTIC AGENTS

2006 ◽  
Vol 15 (02) ◽  
pp. 251-285 ◽  
Author(s):  
VIRGIL ANDRONACHE ◽  
MATTHIAS SCHEUTZ
Keyword(s):  
Single Agent ◽  
Short Review ◽  
Multi Agent Systems ◽  
Agent Architecture ◽  
Development Environment ◽  
Agent Architectures ◽  
Agent Systems ◽  
Development Tools ◽  
Multi Agent ◽  
Architecture Development

In this paper we present the agent architecture development environment ADE, intended for the design, implementation, and testing of distributed agent architectures. After a short review of architecture development tools, we discuss ADE's unique features that place it in the intersection of multi-agent systems and development kits for single agent architectures. A detailed discussion of the general properties of ADE, its implementation philosophy, and its user interface is followed by examples from virtual and robotic domains that illustrate how ADE can be used for designing, implementing, testing, and running agent architectures.

Robust Region-Tracking for Multi-Agent Systems Using Sliding Mode Control

2010 ◽  
Author(s):  
Mark Bacon ◽  
Nejat Olgac
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Autonomous Agent ◽  
Moving Target ◽  
Multi Agent Systems ◽  
Target Region ◽  
Agent Systems ◽  
Mode Control ◽  
Modeling Uncertainties ◽  
Multi Agent

Control of autonomous agent swarms is studied for targeted flocking exercises. The desired decentralized control also requires robustness against modeling uncertainties and bounded unknown forces. In this analysis, we consider the task of robustly driving multiple agents to a moving ‘target region’, as repulsive interactions help spread out the agents. An unconventional form of sliding mode control is implemented to provide the robust attraction towards the region’s center. For robustness a ‘boundary layer’ is conceived, which corresponds to the desired target region. The attraction is intentionally softened inside this target region, allowing agents to create a final formation utilizing their repulsion forces. Examples are given for moving circular and elliptical regions which illustrate the effectiveness of the proposed strategy.

Evolutionary Multi-Agent Systems: An Adaptive and Dynamic Approach to Optimization

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Optimization Design ◽  
Design Space ◽  
A Priori ◽  
Single Agent ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a virtual team of specialized strategic software agents to cooperate and evolve to adaptively search an optimization design space. Our goal is to demonstrate and understand how such dynamically evolving teams may search more effectively than any single agent or a priori set strategy. We present a core framework and methodology that has potential applications in layout, scheduling, manufacturing, and other engineering design areas. The communal agent team organizational structure employed allows cooperation of agents through the products of their work and creates an ever changing set of individual solutions for the agents to work on. In addition, the organizational structure allows the framework to be adaptive to changes in the design space that may occur during the optimization process. An evolutionary approach is used, but evolution occurs at the strategic rather than the solution level, where the strategies of agents in the team are the decisions for when and how to choose and alter a solution, and the agents evolve over time. As an application of this approach in a static domain, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, with each agent employing a different solution strategy, must evolve to apply the solution strategies, which are most useful given the solution set at any point in the process. We discuss the extensions to our preliminary work that will make our framework useful to the design and optimization community.

scholarly journals Supporting heterogeneous agent mobility with ALAS

2012 ◽  
Vol 9 (3) ◽  
pp. 1203-1229 ◽  
Author(s):  
Dejan Mitrovic ◽  
Mirjana Ivanovic ◽  
Zoran Budimac ◽  
Milan Vidakovic
Keyword(s):  
Development Process ◽  
Virtual Machines ◽  
Effective Solution ◽  
Multi Agent Systems ◽  
Heterogeneous Agent ◽  
Agent Systems ◽  
Mobility Problem ◽  
Multi Agent ◽  
Agent Development ◽  
Oriented Programming Language

Networks of multi-agent systems are considered to be heterogeneous if they include systems with different sets of APIs, running on different virtual machines. Developing an agent that can operate in this kind of a setting is a difficult task, because the process requires regeneration of the agent?s executable code, as well as modifications in the way it communicates with the environment. With the main goal of providing an effective solution to the heterogeneous agent mobility problem, a novel agent-oriented programming language, named ALAS, is proposed. The new language also provides a set of programming constructs that effectively hide the complexity of the overall agent development process. The design of the ALAS platform and an experiment presented in this paper will show that an agent written in ALAS is able to work in truly heterogeneous networks of multi-agent systems.

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