Role-Based Multi-Agent Systems

2011 ◽  
pp. 254-285 ◽  
Author(s):  
Haibin Zhu ◽  
MengChu Zhou
Keyword(s):  
System Design ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Systems ◽  
Collaborative Behavior ◽  
Cooperative Agents ◽  
Multi Agent ◽  
Role Based ◽  
Information Personalization

Agent system design is a complex task challenging designers to simulate intelligent collaborative behavior. Roles can reduce the complexity of agent system design by categorizing the roles played by agents. The role concepts can also be used in agent systems to describe the collaboration among cooperative agents. In this chapter, we introduce roles as a means to support interaction and collaboration among agents in multi-agent systems. We review the application of roles in current agent systems at first, then describe the fundamental principles of role-based collaboration and propose the basic methodologies of how to apply roles into agent systems (i.e., the revised E-CARGO model). After that, we demonstrate a case study: a soccer robot team designed with role specifications. Finally, we present the potentiality to apply roles into information personalization.

AN INFRASTRUCTURE FOR ENGINEERING COOPERATIVE AGENTS

2000 ◽  
Vol 10 (06) ◽  
pp. 681-711 ◽  
Author(s):  
SOE-TSYR YUAN ◽  
ZENG-LUNG WU
Keyword(s):  
Success Factors ◽  
Critical Mass ◽  
Success Factor ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Bottom Up ◽  
Agent Systems ◽  
Cooperative Agents ◽  
Multi Agent

Currently, systems of cooperative agents (multi-agent systems), possessing the capabilities of autonomy, adaptation, and cooperation, are being used in an increasingly wide variety of application areas, and the conversation-based multi-agent system design is the major design for those multi-agent systems. Supposedly, conversation-based multi-agent systems should have been prevailing enough for tackling dynamic aspects of problems in a variety of domains. However, for industries, multi-agent systems are still found to be in the birth stage where they only show their new values in anticipation for further explorations and improvements in order to attract critical mass of users of information executives or software developers. Nevertheless, what are the success factors that can result in a critical mass of multi-agent system designers? This paper shows one possible success factor — an infrastructure for the bottom-up design of multi-agent systems. The bottom-up design makes it possible for agents to be reassembled into multi-agent systems and reused as needed. However, what do we need to successfully support the bottom-up design? This paper is the first attempt to present a tool that fully supports the bottom-up design of multi-agent systems. The tool has three parts. The first part is a wrapper that wraps each agent so that it exempts the designers from the careful detailed deployment of the inter-relationships between cooperation knowledge and task knowledge inside the agent. This wrapper should be independent of the functions of agents. The second part is an environment that can support the wrapper to automate the cooperation process on behalf of agents. The third part is a graphical assembly panel for developers to visually configure wrapped agents residing at different places of the Internet into a working multi-agent system.

scholarly journals A Programming Approach to Collective Autonomy

2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Roberto Casadei ◽  
Gianluca Aguzzi ◽  
Mirko Viroli
Keyword(s):  
Autonomous Vehicles ◽  
Autonomic Computing ◽  
Autonomous Agents ◽  
Programming Approach ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Simulated Case ◽  
Complex Settings

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

Exploring management strategies for community-based forests using multi-agent systems: A case study in Palawan, Philippines

2009 ◽  
Vol 90 (11) ◽  
pp. 3607-3615 ◽  
Author(s):  
Paolo C. Campo ◽  
Guillermo A. Mendoza ◽  
Philippe Guizol ◽  
Teodoro R. Villanueva ◽  
François Bousquet
Keyword(s):  
Management Strategies ◽  
Multi Agent Systems ◽  
Community Based ◽  
Agent Systems ◽  
Multi Agent

A Clustering Approach Based on Cooperative Agents to Improve Decision Support in ERP

2019 ◽  
pp. 1-18
Author(s):  
Nadjib Mesbahi ◽  
Okba Kazar ◽  
Saber Benharzallah ◽  
Merouane Zoubeidi ◽  
Djamil Rezki
Keyword(s):  
Decision Support ◽  
Intelligent Systems ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Cooperative Agents ◽  
Distributed Problem Solving ◽  
Multi Agent ◽  
Complete Set ◽  
Clustering Approach

Multi-agent systems (MAS) are a powerful technology for the design and implementation of autonomous intelligent systems that can handle distributed problem solving in a complex environment. This technology has played an important role in the development of data mining systems in the last decade, the purpose of which is to promote the extraction of information and knowledge from a large database and to make these systems more scalable. In this chapter, the authors present a clustering system based on cooperative agents through a centralized and common ERP database to improve decision support in ERP systems. To achieve this, they use multi-agent system paradigm to distribute the complexity of k-means algorithm in several autonomous entities called agents, whose goal is to group records or observations on similar objects classes. This will help business decision makers to make good decisions and provide a very good response time by the use of the multi-agent system. To implement the proposed architecture, it is more convenient to use the JADE platform while providing a complete set of services and have agents comply with the specifications FIPA.

Evolutionary Computation as a Paradigm for Engineering Emergent Behaviour in Multi-Agent Systems

2011 ◽  
pp. 118-136 ◽  
Author(s):  
Robert E. Smith ◽  
Claudio Bonacina
Keyword(s):  
Evolutionary Computation ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Future Directions ◽  
Agent Based ◽  
Agent Systems ◽  
Emergent Behaviour ◽  
Shaping System ◽  
Multi Agent

In the multi-agent system (MAS) context, the theories and practices of evolutionary computation (EC) have new implications, particularly with regard to engineering and shaping system behaviors. Thus, it is important that we consider the embodiment of EC in “real” agents, that is, agents that involve the real restrictions of time and space within MASs. In this chapter, we address these issues in three ways. First, we relate the foundations of EC theory to MAS and consider how general interactions among agents fit within this theory. Second, we introduce a platform independent agent system to assure that our EC methods work within the generic, but realistic, constraints of agents. Finally, we introduce an agent-based system of EC objects. Concluding sections discuss implications and future directions.

Engineering Adaptive Multi-Agent Systems

2011 ◽  
pp. 513-535
Author(s):  
Carole Bernon ◽  
Valérie Camps ◽  
Marie-Pierre Gleizes ◽  
Gauthier Picard
Keyword(s):  
Information System ◽  
Theoretical Background ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
The Third ◽  
Multi Agent

This chapter introduces the ADELFE methodology, an agent-oriented methodology dedicated to the design of systems that are complex, open, and not well-specified. The need for its development is justified by the theoretical background given in the first section, which also gives an overview of the concepts on which multi-agent systems developed with ADELFE are based. A methodology is composed of a process, a notation, and tools. Tools are presented in the second section and the process in the third one, using an information system case study to better visualize how to apply this process.

Application of Fuzzy Labeled Transition System to Contract Net Protocol

2015 ◽  
Vol 6 (3) ◽  
pp. 27-46 ◽  
Author(s):  
Sofia Kouah ◽  
Djamel Eddine Saïdouni
Keyword(s):  
Dynamic Systems ◽  
Transition System ◽  
Multi Agent Systems ◽  
Contract Net Protocol ◽  
Agent Systems ◽  
Contract Net ◽  
Multi Agent ◽  
Refinement Process ◽  
Labeled Transition System

For developing large dynamic systems in a rigorous manner, fuzzy labeled transition refinement tree (FLTRT for short) has been defined. This model provides a formal specification framework for designing such systems. In fact, it supports abstraction and enables fuzziness which allows a rigorous formal refinement process. The purpose of this paper is to illustrate the applicability of FLTRT for designing multi agent systems (MAS for short), among others collective and internal agent's behaviors. Therefore, Contract Net Protocol (CNP for short) is chosen as case study.

Adaptive Collaboration Based on the E-CARGO Model

2012 ◽  
Vol 4 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Haibin Zhu ◽  
Ming Hou ◽  
Mengchu Zhou
Keyword(s):  
Team Performance ◽  
Theoretical Foundation ◽  
Multi Agent Systems ◽  
Role Assignment ◽  
Future Studies ◽  
Agent Systems ◽  
Multi Agent ◽  
Computer Based ◽  
Single Controller ◽  
Role Based

Adaptive Collaboration (AC) is essential for maintaining optimal team performance on collaborative tasks. However, little research has discussed AC in multi-agent systems. This paper introduces AC within the context of solving real-world team performance problems using computer-based algorithms. Based on the authors’ previous work on the Environment-Class, Agent, Role, Group, and Object (E-CARGO) model, a theoretical foundation for AC using a simplified model of role-based collaboration (RBC) is proposed. Several parameters that affect team performance are defined and integrated into a theorem, which showed that dynamic role assignment yields better performance than static role assignment. The benefits of implementing AC are further proven by simulating a “future battlefield” of remotely-controlled robotic vehicles; in this scenario, team performance clearly benefits from shifting vehicles (or roles) using a single controller. Related research is also discussed for future studies.

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