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.

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.

scholarly journals MAPSS: An Intelligent Architecture for the Pedagogical Support

2019 ◽  
Vol 14 (14) ◽  
pp. 19
Author(s):  
Najoua Hrich ◽  
Mohamed Lazaar ◽  
Mohamed Khaldi
Keyword(s):  
Artificial Intelligence ◽  
Learning Environments ◽  
Theoretical Foundation ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
E Learning ◽  
Multi Agent ◽  
Learning Platforms ◽  
The Subject ◽  
Competency Evaluation

The multi-agent systems (MAS) are a part of artificial intelligence (AI), they have emerged today in the development of major e-learning platforms. Their integration has given new impetus to learning environments by the possibility of integrating new parameters (psychological, pedagogical, ergonomic…) favoring a better adaptation to the learner. In addition, the multiagent approach offers the possibility to design flexible solutions based on a set of agents which are in continuous communication to accomplish the tasks entrusted to them. In this paper, we propose a model of pedagogical support based on a coupling of ontology and multi-agent systems for a synergy of their forces and the important contribution they can make to improve the learning-teaching process. Previous work has been the subject of theoretical foundation related to competency evaluation, and development of an ontology and an algorithm for evaluating competency. As a continuity, we present the design of Multiagent Pedagogical Support System (MaPSS) and the different scenarios of its utilization.

scholarly journals Role-Based Embedded Domain-Specific Language for Collaborative Multi-Agent Systems through Blockchain Technology

2021 ◽  
Author(s):  
Orcun Oruc
Keyword(s):  
Object Oriented ◽  
Object Oriented Programming ◽  
Domain Specific Language ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Domain Specific ◽  
Analysis Phase ◽  
Multi Agent ◽  
Role Based ◽  
Abstraction Levels

Multi-agent systems have evolved with their complexities over the past few decades. To create multi-agent systems, developers should understand the design, analysis, and implementation together. Agent-oriented software engineering applies best practices through mainly software agents with abstraction levels in the multi-agent systems. However, abstraction levels take a considerable amount of time due to the design complexity and adversity of the analysis phase before implementing them. Moreover, trust and security of multi-agent systems have never been detailed in the design and analysis phase even though the implementation of trust and security on the tamper-proof data are necessary for developers. Nonetheless, object-oriented programming is the right way to do it, when implementing complex software agents, one of the major problems is that the object-oriented programming approach still has a complex process-interaction and a burden of event-goal combination to represent actions by multi-agents. Designated roles with their relationships, invariants, and constraints of roles can be constructed based on blockchain contracts between agents. Furthermore, in the case of new agents who participate in an agent network, decentralization and transparency are two key parameters, which agents can exchange trusted information and reach a consensus aspect of roles. This study will take the software agent development as a whole with analysis, design, and development with role-object pattern in terms of smart contract applications. In this paper, we aim to propose a role-based domain-specific language that enables smart contracts which can be used in agent-oriented frameworks. Furthermore, we would like to refer to methodology, results of the research, and case study to enlighten readers in a better way. Finally, we summarize findings and highlight the main research points by inferencing in the conclusion section.

Assessment of BAR

2017 ◽  
Vol 5 (3) ◽  
pp. 1-17
Author(s):  
Shivashish Jaishy ◽  
Yoshiki Fukushige ◽  
Nobuhiro Ito ◽  
Kazunori Iwata ◽  
Yoshinobu Kawabe
Keyword(s):  
Collision Avoidance ◽  
Dynamic Environment ◽  
Current Concept ◽  
Multi Agent Systems ◽  
Role Assignment ◽  
Agent Systems ◽  
Multi Agent ◽  
The Impact

In the Multi-Agent Systems, many agents work together towards achieving a defined goal. As it may be difficult for the agents to work in a dynamic environment, the current concept is trying to focus on the issues of situation where there may be cases of agent breaking down. This algorithm will distinguish and groupify the breakdown agents from the active agents. The authors are focusing on this scenario and replacement of breakdown agents by active agents by implementing the SCRAM- Scalable Collision-avoiding Role Assignment with Minimal-makespan, which has generalized to many Multi-Agent Systems specifically focusing on the collision avoidance among the agents. The authors are trying to address the impact and fate of breakdown agents, which otherwise is not yet addressed in SCRAM, through a new algorithm. This paper is designed to allow the generalization of the concept of SCRAM without any collision and disturbances even in the case of agent breakdown.

Role-Based Modeling for Designing Agent Behavior in Self-Organizing Multi-Agent Systems

2018 ◽  
Vol 28 (01) ◽  
pp. 79-96
Author(s):  
Kemas M. Lhaksmana ◽  
Yohei Murakami ◽  
Toru Ishida
Keyword(s):  
Autonomous Systems ◽  
Systems Design ◽  
Role Modeling ◽  
Modeling Method ◽  
Multi Agent Systems ◽  
Smart Transportation ◽  
Agent Systems ◽  
Multi Agent ◽  
Role Based ◽  
Self Organizing

Self-organization has been proposed to be implemented in complex systems which require the automation capabilities to govern itself and to adapt upon changes. Self-organizing systems can be modeled as multi-agent systems (MAS) since they share common characteristics in that they consist of multiple autonomous systems. However, most existing MAS engineering methodologies do not fully support self-organizing systems design since they require predefined goals and agent behaviors, which is not the case in self-organizing systems. Another feature that is currently not supported for designing self-organizing MAS is the separation between the design of agent behaviors and behavior adaptation, i.e. how agents adapt their behaviors to respond upon changes. To tackle these issues, this paper proposes a role modeling method, in which agent behaviors are represented as roles, to design how agents perform behavior adaptation at runtime by switching between roles. The applicability of the proposed role modeling method is evaluated in a case study of a self-organizing smart transportation system.

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