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.

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.

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.

Rule Engines and Agent-Based Systems

2012 ◽  
pp. 211-218 ◽  
Author(s):  
Agostino Poggi ◽  
Michele Tomaiuolo
Keyword(s):  
Dynamic Environments ◽  
Multi Agent Systems ◽  
Rule Engine ◽  
Rule Based ◽  
Agent Based ◽  
Agent Systems ◽  
Loosely Coupled ◽  
Domain Specific ◽  
Multi Agent ◽  
Good Support

Expert systems are successfully applied to a number of domains. Often built on generic rule-based systems, they can also exploit optimized algorithms. On the other side, being based on loosely coupled components and peer to peer infrastructures for asynchronous messaging, multi-agent systems allow code mobility, adaptability, easy of deployment and reconfiguration, thus fitting distributed and dynamic environments. Also, they have good support for domain specific ontologies, an important feature when modelling human experts’ knowledge. The possibility of obtaining the best features of both technologies is concretely demonstrated by the integration of JBoss Rules, a rule engine efficiently implementing the Rete-OO algorithm, into JADE, a FIPA-compliant multi-agent system.

An overview of search strategies in distributed environments

2013 ◽  
Vol 29 (3) ◽  
pp. 281-313 ◽  
Author(s):  
E. Del Val ◽  
M. Rebollo ◽  
V. Botti
Keyword(s):  
Dynamic Environments ◽  
System Structure ◽  
Search Process ◽  
Global Information ◽  
Distributed Environments ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Service Oriented ◽  
Multi Agent ◽  
Proper Performance

AbstractDistributed systems are populated by a large number of heterogeneous entities that join and leave the systems dynamically. These entities act as clients and providers and interact with each other in order to get a resource or to achieve a goal. To facilitate the collaboration between entities, the system should provide mechanisms to manage the information about which entities or resources are available in the system at a certain moment, as well as how to locate them in an efficient way. However, this is not an easy task in open and dynamic environments where there are changes in the available resources and global information is not always available. In this paper, we present a comprehensive vision of search in distributed environments. This review not only considers the approaches of the peer-to-peer area, but also the approaches from three more areas: service-oriented environments, multi-agent systems, and complex networks. In these areas, the search for resources, services, or entities plays a key role for the proper performance of the systems built on them. The aim of this analysis is to compare approaches from these areas taking into account the underlying system structure and the algorithms or strategies that participate in the search process.

scholarly journals Special Issue “Multi-Agent Systems”: Editorial

2019 ◽  
Vol 9 (5) ◽  
pp. 954 ◽  
Author(s):  
Stefano Mariani ◽  
Andrea Omicini
Keyword(s):  
Everyday Life ◽  
Dynamic Environments ◽  
Multi Agent Systems ◽  
Special Issue ◽  
Agent Based ◽  
Agent Systems ◽  
Agent Based Modelling ◽  
Manufacturing Management ◽  
Multi Agent ◽  
Technical Systems

Multi-agent systems (MAS) allow and promote the development of distributed and intelligent applications in complex and dynamic environments. Applications of this kind have a crucial role in our everyday life, as witnessed by the broad range of domains they are deployed to—such as manufacturing, management sciences, e-commerce, biotechnology, etc. Despite heterogeneity, those domains share common requirements such as autonomy, structured interaction, mobility, and openness—which are well suited for MAS. Therein, in fact, goal-oriented processes can enter and leave the system dynamically and interact with each other according to structured protocols. This special issue gathers 17 contributions spanning from agent-based modelling and simulation to applications of MAS in situated and socio-technical systems.

scholarly journals Using Human-Agent Teams to Purposefully Design Multi-Agent Systems

2019 ◽  
Vol 63 (1) ◽  
pp. 1425-1429 ◽  
Author(s):  
Christopher Flathmann ◽  
Nathan McNeese ◽  
Lorenzo Barberis Canonico
Keyword(s):  
Dynamic Environments ◽  
Design Model ◽  
Individual Member ◽  
Multi Agent Systems ◽  
Dynamic Nature ◽  
Agent Systems ◽  
Model Based ◽  
Human Agent ◽  
Multi Agent ◽  
Agent Teams

With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams.

scholarly journals Deliberative Agents in Dynamic Environments, Using Jason and NARS

2021 ◽  
Author(s):  
valeria seidita ◽  
francesco lanza ◽  
Patrick Hammer ◽  
Antonio Chella ◽  
Pei Wang
Keyword(s):  
Dynamic Environments ◽  
General Intelligence ◽  
Multi Agent Systems ◽  
Bdi Agents ◽  
Agent Systems ◽  
Intelligence System ◽  
Artificial General Intelligence ◽  
Reasoning System ◽  
Multi Agent ◽  
High Level

This work explore the possibility to combine the Jason reasoning cycle with a Non-Axiomatic Reasoning System (NARS) to develop multi-agent systems that are able to reason, deliberate and plan when information about plans to be executed and goals to be pursued is missing or incomplete. The contribution of this work is a method for BDI agents to create high-level plans using an AGI (Artificial General Intelligence) system based on non-axiomatic logic.

A Virtual Engineering Tool for Product Design Using High Fidelity CFD Analysis Models

2005 ◽  
Author(s):  
Gengxun Huang ◽  
Angran Xiao ◽  
Kenneth M. Bryden
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Engineering Design ◽  
Direct Interaction ◽  
Design Tool ◽  
Optimization Process ◽  
Initial Population ◽  
High Fidelity ◽  
Design And Optimization ◽  
Virtual Engineering

Product design optimization is a complex decision-making process requiring intensive interactions between designers and the designed product. However, most current optimization tools do not support this type of direct interaction. Typically, resolving a converged result with an optimization tool takes a long solution time and high computing cost. However, designers are not involved in the optimization process and cannot control the quality of the so-called optimal result. In this paper, we introduce a virtual engineering design tool that expands the application scope of virtual reality from visualization to interaction and decision support. This design tool allows designers to easily experiment with different product designs using high fidelity CFD solver and observe the effects in an almost real-time manner. This can help designers understand the nature of the product and make superior decisions. Most importantly, the design tool enables designers to control the optimization computing process by selecting superior starting points or changing an obviously unpromising search direction. Hence, by adding human creativity and experience into the optimization process, designers can resolve the design optimization problem more efficiently. A coal pipe design and optimization scenario is presented to demonstrate the efficacy of this virtual engineering design tool. The goal of this tool is to enable a designer to modify the size and shape of a coal pipe to obtain evenly distributed coal at the outlet. In this tool after the initial population was chosen, a standard evolutionary algorithm was used to find the most superior pipe design within a much shorter time.

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