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.

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.

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.

Bounded consensus tracking control of second-order multi-agent systems with active leader and jointly connected topology

2016 ◽  
Vol 40 (2) ◽  
pp. 504-513 ◽  
Author(s):  
Lei Chen ◽  
Kaiyu Qin ◽  
Jiangping Hu
Keyword(s):  
Tracking Control ◽  
Sufficient Conditions ◽  
Interaction Network ◽  
Second Order ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Systems ◽  
Strongly Connected ◽  
Multi Agent

In this paper, we investigate a tracking control problem for second-order multi-agent systems. Here, the leader is self-active and cannot be completely measured by all the followers. The interaction network associated with the leader–follower multi-agent system is described by a jointly connected topology, where the topology switches over time and is not strongly connected during each time subinterval. We consider a consensus control of the multi-agent system with or without time delay and propose two categories of neighbour-based control rules for every agent to track the leader, then provide sufficient conditions to ensure that all agents follow the leader, and meanwhile, the tracking errors can be estimated. Finally, some simulation results are presented to demonstrate our theoretical results.

scholarly journals On Partially Controlled Multi-Agent Systems

1996 ◽  
Vol 4 ◽  
pp. 477-507 ◽  
Author(s):  
R. I. Brafman ◽  
M. Tennenholtz
Keyword(s):  
Expected Utility ◽  
The Other ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Direct Control ◽  
Agent System ◽  
Agent Systems ◽  
Multi Agent ◽  
Game Theoretic

Motivated by the control theoretic distinction between controllable and uncontrollable events, we distinguish between two types of agents within a multi-agent system: controllable agents, which are directly controlled by the system's designer, and uncontrollable agents, which are not under the designer's direct control. We refer to such systems as partially controlled multi-agent systems, and we investigate how one might influence the behavior of the uncontrolled agents through appropriate design of the controlled agents. In particular, we wish to understand which problems are naturally described in these terms, what methods can be applied to influence the uncontrollable agents, the effectiveness of such methods, and whether similar methods work across different domains. Using a game-theoretic framework, this paper studies the design of partially controlled multi-agent systems in two contexts: in one context, the uncontrollable agents are expected utility maximizers, while in the other they are reinforcement learners. We suggest different techniques for controlling agents' behavior in each domain, assess their success, and examine their relationship.

scholarly journals Multi-agent system for computer science and engineering education

2017 ◽  
Vol 58 ◽  
Author(s):  
Jaroslav Meleško ◽  
Eugenijus Kurilovas ◽  
Irina Krikun
Keyword(s):  
Learning Styles ◽  
Intelligent Agents ◽  
Educational Data Mining ◽  
Software Agent ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Systems ◽  
Multi Agent ◽  
Identification Software

The paper aims to analyse application trends of intelligent multi-agent systems to personalise learning. First of all, systematic literature review was performed. Based on the systematic review analysis, the main trends on applying multi-agent systems to personalise learning were identified. Second, main requirements and components for an educational multi-agent system were formulated. Third, based on these components a model of intelligent personalized system is proposed. The system employs five intelligent agents: (1) learning styles identification software agent, (2) learner profile creation software agent, (3) pedagogical suitability software agent, (4) optimal learning units/scenarios creation software agent, and (5) learning analytics/educational data mining software agent.

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

2011 ◽  
pp. 2338-2350
Author(s):  
Robert E. Smith ◽  
Claudia 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.

The architecture and functionality of the multi-agent system Navigation

2017 ◽  
pp. 083-096
Author(s):  
A.L. Yalovets ◽  
Keyword(s):  
System Architecture ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Design Development ◽  
Agent System ◽  
Language Implementation ◽  
Agent Systems ◽  
Multi Agent

The features of the design, development and functioning of the multi-agent system Navigation are investigated. System architecture and substantiate the choice of language implementation of the system are provided. The functionality of the subsystems of multi-agent systems Navigation is analyzed in detail. The results of multi-agent modelling of pursuit/escape processes by means of the multi-agent system in different modes of its functioning are compared on meaningful example.

ENABLING TECHNOLOGIES FOR THE CREATION AND RESTRUCTURING PROCESS OF EMERGENT ENTERPRISE ALLIANCES

2004 ◽  
Vol 03 (01) ◽  
pp. 33-60 ◽  
Author(s):  
MIHAELA ULIERU ◽  
RAINER UNLAND
Keyword(s):  
State Of The Art ◽  
Virtual Enterprise ◽  
Virtual Enterprises ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Systems ◽  
Restructuring Process ◽  
Multi Agent ◽  
Deep Integration

In today's world, it is of utterly importance for enterprises to react in a timely and flexible way to upcoming complex market demands. One solution is given by the concept of virtual enterprise and enterprise alliances, respectively. In order to function efficiently and flexibly such enterprises need to be deeply integrated. Based on previous work combining the concepts of virtual enterprises, holonic organizations and multi-agent systems to support such deep integration, the paper discusses in detail how well-suiting partners and contributors for a given (bunch of) task(s) can be found using today's state-of-the-art technologies. Mapping an enterprise alliance onto a multi-agent system is enabled by a methodology equipping each agent with the ability to deal and consider its own goals (goals of the unit it represents) as well as the goals of the unit in which it is integrated (the higher level unit).

scholarly journals Hybrid-order Network Consensus for Distributed Multi-agent Systems

2021 ◽  
Vol 70 ◽  
pp. 389-407
Author(s):  
Guangqiang Xie ◽  
Junyu Chen ◽  
Yang Li
Keyword(s):  
Higher Order ◽  
Distributed Artificial Intelligence ◽  
Order Structure ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Consensus Protocol ◽  
Consensus Control ◽  
Agent System ◽  
Agent Systems ◽  
Multi Agent

As an important field of Distributed artificial intelligence (DAI), multi-agent systems (MASs) have attracted the attention of extensive research scholars. Consensus as the most important issue in MAS, much progress has been made in studying the consensus control of MAS, but there are some problems remained largely unaddressed which cause the MAS to lose some useful network structure information. First, multi-agent consensus protocol usually proceeds over the low-order structure by only considering the direct edges between agents, but ignores the higher-order structure of the whole topology network. Second, the existing work assumes all the edges in a topology network have the same weight without exploring the potential diversity of the connections. In this way, multi-agent systems fail to enforce consensus, resulting in fragmentation into multiple clusters. To address the above issues, this paper proposes a Motif-aware Weighted Multi-agent System (MWMS) method for consensus control. We focus more on triangle motif in the network, but it can be extended to other kinds of motifs as well. First, a novel weighted network is used which is the combination of the edge-based lower-order structure and the motif-based higher-order structure, i.e., hybrid-order structure. Subsequently, by simultaneously considering the quantity and the quality of the connections in the network, a novel consensus framework for MAS is designed to update agents. Then, two baseline consensus algorithms are used in MWMS. In our experiments, we use ten topologies of different shapes, densities and ranges to comprehensively analyze the performance of our proposed algorithms. The simulation results show that the hybrid higher-order network can effectively enhance the consensus of the multi-agent system in different network topologies.

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