Interaction Patterns in a Multi-Agent Organisation to Support Shared Tasks

Modeling and Using Context - Lecture Notes in Computer Science ◽

10.1007/978-3-642-40972-1_30 ◽

2013 ◽

pp. 364-370 ◽

Cited By ~ 1

Author(s):

Moser Silva Fagundes ◽

Felipe Meneguzzi ◽

Renata Vieira ◽

Rafael H. Bordini

Keyword(s):

Interaction Patterns ◽

Multi Agent

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Self-Organising Interaction Patterns of Homogeneous and Heterogeneous Multi-Agent Populations

2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems ◽

10.1109/saso.2009.15 ◽

2009 ◽

Cited By ~ 4

Author(s):

Emre Cakar ◽

Christian Muller-Schloer

Keyword(s):

Interaction Patterns ◽

Multi Agent

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Uncovering interaction patterns of multi-agent collective motion via complex network analysis

2014 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas.2014.6865609 ◽

2014 ◽

Author(s):

Xiao-Ke Xu ◽

Michael Small ◽

F. Javier Perez-Barberia

Keyword(s):

Network Analysis ◽

Complex Network ◽

Collective Motion ◽

Interaction Patterns ◽

Complex Network Analysis ◽

Multi Agent

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Interaction patterns and observable commitments in a multi-agent trading scenario

Proceedings of the fifth international conference on Autonomous agents - AGENTS '01 ◽

10.1145/375735.376422 ◽

2001 ◽

Cited By ~ 8

Author(s):

Jeremy Pitt ◽

Lloyd Kamara ◽

Alexander Artikis

Keyword(s):

Interaction Patterns ◽

Multi Agent

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Collective Learning in Multi-Agent Systems Based on Cultural Algorithms

CLEI electronic journal ◽

10.19153/cleiej.17.2.7 ◽

2014 ◽

Vol 17 (2) ◽

Cited By ~ 1

Author(s):

Juan Terán ◽

José L. Aguilar ◽

Mariela Cerrada

Keyword(s):

Learning Model ◽

Collective Learning ◽

Coordination Mechanisms ◽

Cultural Algorithms ◽

Interaction Patterns ◽

Multi Agent Systems ◽

Computational Tool ◽

Industrial Automation ◽

Agent Systems ◽

Multi Agent

This paper aims to present a learning model for coordination schemes in Multi-Agent Systems (MAS) based on Cultural Algorithms (CA). In this model, the individuals (one of the CA components) are the different conversations that may occur in any multi-agent systems, and the coordination scheme learned is at the level of the way to perform the communication protocols into the conversation. A conversation can has sub-conversations, and the sub-conversations and/or conversations are identified with a particular type of conversation associated with a certain interaction patterns. The interaction patterns use the coordination mechanisms existing in the literature. In order to simulate the proposed learning model, we develop a computational tool called CLEMAS, which has been used to apply the model to a case of study in industrial automation, related to a Faults Management System based on Agents.

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