A Conceptual Model for Situating Purposes in Artificial Institutions

{In multi-agent systems, artificial institutions connect institutional concepts, belonging to the institutional reality, to the concrete elements that compose the system. The institutional reality is composed of a set of institutional concepts, called Status-Functions. Current works on artificial institutions focus on identifying the status-functions and connecting them to the concrete elements. However, the functions associated with the status-functions are implicit. As a consequence, the agents cannot reason about the functions provided by the elements that carry the status-functions and, thus, cannot exploit these functions to satisfy their goals. Considering this problem, this paper proposes a model to express the functions -- or the purposes -- associated with the status-functions. Examples illustrate the application of the model in a practical scenario, showing how the agents can use purposes to reason about the satisfaction of their goals in institutional contexts.

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A Collaborative Multi-Agent Framework for Internet-Based Teleoperation Systems

International Journal of Agent Technologies and Systems ◽

10.4018/jats.2013040102 ◽

2013 ◽

Vol 5 (2) ◽

pp. 31-54

Author(s):

Nader Cheaib ◽

Samir Otmane ◽

Malik Mallem

Keyword(s):

Conceptual Model ◽

Multi Agent Systems ◽

Collaborative Process ◽

Agent Systems ◽

Collaborative Software ◽

Research Areas ◽

Multi Agent ◽

Collaboration Process ◽

3C Model ◽

Teleoperation Systems

This paper presents a conceptual model of an agent (called Collaborator Agent) intended to design collaborative software architectures based on multi-agent systems. The authors’ model combines astutely two research areas: Multi-Agent Systems (MAS) and Computer Supported Cooperative Work (CSCW). The particularity of their approach is the division of the collaborative process into three spaces according to Ellis' 3C model: communication, coordination and production. In their work, the authors extend the 3C model by adding a fourth space: collaboration. Hence, the authors present a model based on four types of agents (collaboration, communication, coordination and production) supporting the whole set of collaborative tasks. The model is used to create the conceptual software architecture of their MAS. The authors apply their conceptual model on the ARITI-C system for collaborative online robot teleoperation. Finally, the authors present a quantitative evaluation of the collaboration process in ARITI-C.

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Tendencies in Multi-Agent Systems: A Systematic Literature Review

CLEI electronic journal ◽

10.19153/cleiej.23.1.1 ◽

2020 ◽

Vol 23 (1) ◽

Author(s):

Mariana Falco ◽

Gabriela Robiolo

Keyword(s):

Assisted Living ◽

Ambient Assisted Living ◽

Multi Agent Systems ◽

Hazard Management ◽

Testing And Debugging ◽

Agent Systems ◽

Engineering Problems ◽

The Status ◽

Multi Agent ◽

Complex Engineering

The application of Artificial Intelligence mechanisms allows the development of systems capable to solve very complex engineering problems. Multi-agent systems (MAS) are one paradigm that allows an alternative way to design distributed control systems. While research in this area grew exponentially before 2009, there is a need to understand the status quo of the field from 2009 to June 2017. An extension of the results of a SLR related to Multi-Agent Systems, its applications and research gaps, following Kitchenham and Wholin guidelines are presented in this paper. From the analysis of 279 papers (out of 3522 candidates), our findings suggest that: a) there were 20 gaps related to agent-oriented methodologies; coordination, cooperation and negotiation; modelling, developing, testing and debugging; b) 24 gaps related to specific domains (recycling, dynamic evacuation, hazard management, health-care, industry, logistics and manufacturing, machine learning, ambient assisted living); and 14 gaps related to specific areas within MAS (A-Teams, dynamic MAS and mobile agents, ABMS, evolutionary MAS, and self-organizing MAS). These gaps specify lines of research where the MAS community must work to achieve the unification of the agent-oriented paradigm; as well as strengthen ties with the industry.

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