A Multi-Agent Intelligent Environment for Rapid Assembly Design, Planning and Simulation

2004 ◽  
Author(s):  
Xuan F. Zha ◽  
Ling L. Li ◽  
Samuel Y. E. Lim
Keyword(s):  
Assembly System ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Knowledge Modeling ◽  
Intelligent Environment ◽  
Analysis And Design ◽  
Assembly Design ◽  
Multi Agent ◽  
Design Planning ◽  
Knowledge Intensive

This paper presents a knowledge-intensive concurrent integration framework for designing, planning, analysis and simulation of assemblies, which is built upon P/T nets and multi-agent systems in AI. In the paper, critical issues are addressed on the implementation of intelligent assembly modeling, analysis and design, planning and simulation using knowledge intensive P/T net models, e.g., knowledge modeling and acquisition, function-behavior-structure modeling, fuzzy relational database, case-based reasoning, and assembly design language, etc. The developed multi-agent intelligent environment, RAPID Assembly system, can process various types of knowledge in assembly design, process and task planning, and assembly system design and simulation so that intelligent design, planning and simulation can be implemented in a virtual environment. It assists designers and manufacturing engineers to design and evaluate the product and its assembly processes and assembly systems (e.g., assembly feasibility, sequences, trajectories, and execution strategies) without building physical prototypes. Design cases are provided to illustrate how the system works.

Containment analysis and design for general linear multi-agent systems with time-varying delays

2016 ◽  
Vol 173 ◽  
pp. 2062-2068 ◽  
Author(s):  
Xiwang Dong ◽  
Liang Han ◽  
Qingdong Li ◽  
Jian Chen ◽  
Zhang Ren
Keyword(s):  
General Linear ◽  
Time Varying ◽  
Multi Agent Systems ◽  
Analysis And Design ◽  
Agent Systems ◽  
Multi Agent

An agent-based supplier selection framework

2015 ◽  
Vol 8 (2/3) ◽  
pp. 180-205 ◽  
Author(s):  
Alireza Jahani ◽  
Masrah Azrifah Azmi Murad ◽  
Md. Nasir bin Sulaiman ◽  
Mohd. Hasan Selamat
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Information Overload ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Common Framework ◽  
Multi Agent ◽  
Case Based

Purpose – The purpose of this paper is to propose an approach that integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning. Unsatisfied customers, information overload and high uncertainty are the main challenges that are faced by today’s supply chains. In addition, a few existing agent-based approaches are tied to real-world supply chain functions like supplier selection. These approaches are static and do not adequately take the qualitative and quantitative factors into consideration. Therefore, an agent-based framework is needed to address these issues. Design/methodology/approach – The proposed approach integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning, as a common framework. These perspectives were rarely used together as a common framework in previous studies. Furthermore, an exploratory case study in an office furniture company is undertaken to illustrate the value of the framework. Findings – The proposed agent-based framework evaluates supply offers based on customers’ preferences, recommends alternative products in the case of stock-out and provides a collaborative environment among agents who represent different supply chain entities. The proposed fuzzy case-based reasoning (F-CBR) approach reduces the information overload by organizing them into the relevant cases that causes less overall search between cases. In addition, its fuzzy aspect addresses the high uncertainty of supply chains, especially when there are different customers’ orders with different preferences. Research limitations/implications – The present study does not include the functions of inventory management and negotiation between agents. Furthermore, only the case description and case retrieval phases of the case-based reasoning approach are investigated, and the remaining phases like case retaining, case reusing and case revising are not included in the scope of this paper. Originality/value – This framework balances the interests of different supply chain structural elements where each of them is represented by a specific agent for better collaboration, decision-making and problem-solving in a multi-agent environment. In addition, the supplier selection and order gathering mechanisms are developed based on customers’ orders.

Bayesian Agencies in Control

2011 ◽  
pp. 168-181
Author(s):  
Anet Potgieter ◽  
Judith Bishop
Keyword(s):  
Bayesian Networks ◽  
Autonomous Agents ◽  
Probabilistic Reasoning ◽  
Multi Agent Systems ◽  
Agent Architectures ◽  
Analysis And Design ◽  
Agent Systems ◽  
Interaction Protocols ◽  
Multi Agent ◽  
The Individual

Most agent architectures implement autonomous agents that use extensive interaction protocols and social laws to control interactions in order to ensure that the correct behaviors result during run-time. These agents, organized into multi-agent systems in which all agents adhere to predefined interaction protocols, are well suited to the analysis, design and implementation of complex systems in environments where it is possible to predict interactions during the analysis and design phases. In these multi-agent systems, intelligence resides in individual autonomous agents, rather than in the collective behavior of the individual agents. These agents are commonly referred to as “next-generation” or intelligent components, which are difficult to implement using current component-based architectures. In most distributed environments, such as the Internet, it is not possible to predict interactions during analysis and design. For a complex system to be able to adapt in such an uncertain and non-deterministic environment, we propose the use of agencies, consisting of simple agents, which use probabilistic reasoning to adapt to their environment. Our agents collectively implement distributed Bayesian networks, used by the agencies to control behaviors in response to environmental states. Each agency is responsible for one or more behaviors, and the agencies are structured into heterarchies according to the topology of the underlying Bayesian networks. We refer to our agents and agencies as “Bayesian agents” and “Bayesian agencies.”

Multi-Agent Systems as Computational Organizations

2011 ◽  
pp. 1658-1683
Author(s):  
Franco Zambonelli ◽  
Nicholas R. Jennings ◽  
Michael Wooldridge
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Systems Development ◽  
Multi Agent Systems ◽  
Design Development ◽  
Analysis And Design ◽  
Agent Systems ◽  
Multi Agent ◽  
Traditional Approaches ◽  
System Paradigm

The multi-agent system paradigm introduces a number of new design/development issues when compared with more traditional approaches to software development and calls for the adoption of new software engineering abstractions. To this end, in this chapter, we elaborate on the potential of analyzing and architecting complex multi-agent systems in terms of computational organizations. Specifically, we identify the appropriate organizational abstractions that are central to the analysis and design of such systems, discuss their role and importance, and show how such abstractions are exploited in the context of the Gaia methodology for multi-agent systems development.

scholarly journals KNOWLEDGE AND EXPERIENCE REUSE THROUGH COMMUNICATION AMONG COMPETENT (PEER) AGENTS

1999 ◽  
Vol 09 (03) ◽  
pp. 319-341 ◽  
Author(s):  
FRANCISCO J. MARTÍN ◽  
ENRIC PLAZA ◽  
JOSEP LLUÍS ARCOS
Keyword(s):  
Specific Problem ◽  
The Other ◽  
Multi Agent Systems ◽  
Knowledge Modeling ◽  
Agent Systems ◽  
Experience Reuse ◽  
Multi Agent ◽  
Cooperative Activity ◽  
Communication And Coordination ◽  
Competence Models

This article addresses an extension of the knowledge modeling approaches, namely to multi-agent systems where communication and coordination are necessary. We propose the notion of competent agent and define the basic capabilities of these agents for the extension to be effective. An agent is competent when it is capable of reasoning about its own competence and that of the other agents with which it cooperates in a given domain. In our framework, an agent has competence models of itself and of its acquaintances from which it can decide, for a specific problem to be solved, the type of cooperative activity it can request and from which agent. In this paper we focus on societies of peer agents, i.e. agents that are able to solve the same type of task but that may have different degrees of competence for specific problem ranges.

Caste-Centric Development of Agent-Oriented Information Systems

2007 ◽  
pp. 692-707 ◽  
Author(s):  
L. Shan ◽  
R. Shen ◽  
J. Wang
Keyword(s):  
Information Systems ◽  
Formal Specification ◽  
Process Model ◽  
Formal Specifications ◽  
Multi Agent Systems ◽  
Collaborative Development ◽  
Analysis And Design ◽  
Agent Systems ◽  
Multi Agent ◽  
Formal Specification Language

Based on the meta-model of information systems presented in Zhu (2006), this chapter presents a caste-centric agent-oriented methodology for evolutionary and collaborative development of information systems. It consists of a process model called growth model, and a set of agent-oriented languages and software tools that support various development activities in the process. At the requirements analysis phase, a modelling language and environment called CAMLE supports the analysis and design of information systems. The semi-formal models in CAMLE can be automatically transformed into formal specifications in SLABS, which is a formal specification language designed for formal engineering of multi-agent systems. At implementation, agent-oriented information systems are implemented directly in an agent-oriented programming language called SLABSp. The features of agent-oriented information systems in general and our methodology in particular are illustrated by an example throughout the chapter.

scholarly journals ORGANISATIONAL RULES AS AN ABSTRACTION FOR THE ANALYSIS AND DESIGN OF MULTI-AGENT SYSTEMS

2001 ◽  
Vol 11 (03) ◽  
pp. 303-328 ◽  
Author(s):  
FRANCO ZAMBONELLI ◽  
NICHOLAS R. JENNINGS ◽  
MICHAEL WOOLDRIDGE
Keyword(s):  
Role Models ◽  
Organisational Structure ◽  
Multi Agent Systems ◽  
Analysis And Design ◽  
Agent Systems ◽  
Full Picture ◽  
Multi Agent ◽  
Organisational Structures ◽  
Definition Of ◽  
Complete Specification

Multi-agent systems can very naturally be viewed as computational organisations. For this reason, we believe organisational abstractions offer a promising set of metaphors and models that can be exploited in the analysis and design of such systems. To this end, the concept of role models is increasingly being used to specify and design multi-agent systems. However, this is not the full picture. In this paper we introduce three additional organisational concepts — organisational rules, organisational structures, and organisational patterns — and discuss why we believe they are necessary for the complete specification of computational organisations. In particular, we focus on the concept of organisational rules and introduce a formalism, based on temporal logic, to specify them. This formalism is then used to drive the definition of the organisational structure and the identification of the organisational patterns. Finally, the paper sketches some guidelines for a methodology for agent-oriented systems based on our expanded set of organisational abstractions.

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