Mathematical Models of Coordination Mechanisms in Multi-Agent Systems

2014 IEEE Congress on Evolutionary Computation (CEC)

2014

Self Cite

It is usually agreed that a system capable of learning deserves to be called intelligent; and conversely, a system being considered as intelligent is, among other things, usually expected to be able to learn. Learning always has to do with the self-improvement of future behavior based on past experience. In this paper we present a learning model for Multi-Agent System, which aims to the optimization of coordination schemes through a collective learning process based on Cultural Algorithms.

Section: Learning Model Of Coordination Schemes In Masmentioning

confidence: 99%

Section: A the Populationmentioning

confidence: 99%

Section: B Design Of the Experimentsmentioning

confidence: 99%

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Cultural learning for Multi-Agent System and its application to fault management

2014 IEEE Congress on Evolutionary Computation (CEC)

2014

Self Cite

“…The formal model of learning proposed in this work involves the formal definition of the CM and the components of a CA [8]. Specifically, the components of the learning model are: the population, the CM specification, the belief space, the objective function, and the acceptance and influence functions.…”

Section: Learning Model Of Coordination Schemes In Masmentioning

confidence: 99%

Collective Learning in Multi-Agent Systems Based on Cultural Algorithms

2014

CLEIej

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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.

“…Los mecanismos de coordinación a utilizar en este trabajo serán la subasta (inglesa y holandesa) y la licitación. Estos mecanismos ya fueron previamente formalizados por [13], sus variables son descritas en la tabla I:…”

Section: Aprendizaje Colectivo En Smaunclassified

Cultural Algorithms-based learning model for multi-agent systems

2013 XXXIX Latin American Computing Conference (CLEI)

2013

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This paper aims to evaluate the learning model for coordination schemes in multiagent systems (MAS) based on Cultural Algorithms. The model is applied to a case of study in industrial automation, related to the Agents-based System for Fault Management System. The instantiation occurs on the conversations that are defining in the MAS's coordination model, which are characterized by type of conversation that have been previously defined. A conversation can have sub-conversations, and in this case the sub-conversations are characterized by a particular type of conversation. Additionally in these conversations can occur some type of conflict, that can be solved by using different coordination mechanisms existing in the literature. For this, it is developed a model based on cultural algorithms, which is used by the MAS as a learning way in the process to determine which coordination mechanism is more suitable for a given conversation and a given scenario. The results show that the obtained model through this learning guides the MAS to determine which mechanism is better suited for a given conversation. Keywords-cultural algorithms; coordination; collective learning; multi-agent systems I. INTRODUCCIÓNUn sistema multiagente (SMA) está formado por un grupo (comunidad) de agentes que interactúan entre sí utilizando protocolos y lenguajes de comunicación de alto nivel, para resolver problemas que están más allá de las capacidades o del conocimiento de cada uno [1]. Estas interacciones entre agentes pueden ser vistas como conversaciones, que a su vez pueden tener sub-conversaciones. Para caracterizar estas subconversaciones se utilizan tipos de conversación (TCs), los cuales han sido definidos previamente [2], éstos permiten generalizar las interacciones o conversaciones entre agentes de cualquier comunidad. Ahora bien, estas sociedades de agentes pueden enfrentar conflictos a la hora no sólo de comunicarse, sino también a la hora de administrar recursos entre los individuos o a la hora de asignar tareas, etc. Para manejar dichos conflictos, existen los esquemas de coordinación (mecanismos de coordinación, MC) que permiten la resolución de los mismos. En este trabajo se propone un modelo de aprendizaje y optimización de esquemas de coordinación para SMA basado en Algoritmos Culturales (AC). Estos algoritmos permiten compartir experiencias entre los individuos, ya que uno de los componentes principales de estos algoritmos es un espacio común de experiencias, proveyendo así la capacidad de un aprendizaje colectivo basado en el intercambio de conocimientos.Dentro del marco de la coordinación en SMA existe una gran cantidad de trabajos orientados a su estudio, e. g., en el trabajo de [3] se enfocan en diseñar agentes que logren una óptima, eficiente y flexible coordinación. Para lograr esto basan su modelo en la teoría de juegos y derivan una solución llamada Harsanyi-Bellman Ad hoc coordination (HBA), la cual utiliza el equilibrio de Nash Bayesiano para planear procedimientos que lleven a encontrar acciones óptimas en el sent...