An Architecture for Identifying Emergent Behavior in Multi-Agent Systems

ABSTRACT Multi-agent systems exhibit unexpected, emergent behavior as a result of the complexity of agent behaviors and their interactions. Despite significant research interest in the past decades, computational methods to identify and analyze emergence as it happens are still needed. This paper proposes a software architecture for identifying emergent behavior in a multi-agent system as it happens, using interval-based snapshots and emergent behavior metrics. We propose various distance functions to compare between the multiagent system under analysis and systems that have been previously shown to exhibit emergent behavior

A Multi-Agent Control Approach for Optimization of Central Cooling Plants

This paper presents an application of a multi-agent control approach for supervisory control of large central cooling plants. The starting point for this work was a multi-agent control simulation framework developed by Cai (2015).  To adapt the framework to the problem at hand several tasks were accomplished: agents representing the performance of the different devices of the plant were developed and inserted in the framework and generalized heuristics were incorporated to make the approach less computationally intensive. A case study of an existing cooling plant with significant complexity was utilized to conduct an extensive evaluation of the approach in terms of optimality and computational resources. Simulations were carried out using one year of historical data to predict the performance of the plant under three different control strategies: 1) multi-agent control, 2) centralized optimization based on mathematical programming techniques and 3) a heuristic control strategy. The results showed that significant savings can be achieved through the implementation of multi-agent control. It is expected that, if each hardware component of the plant comes with an integrated agent that represents its behavior, then the proposed multi-agent framework could automatically generate the multi-agent structure and control algorithm after some relatively simple pre-configuration steps. This will reduce the site-specific engineering and will provide a more economic and easy to configure solution for central cooling systems

A Framework For Intelligent Multi Agent System Based Neural Network Classification Model

TIntelligent multi agent systems have great potentials to use in different purposes and research areas. One of the important issues to apply intelligent multi agent systems in real world and virtual environment is to develop a framework that support machine learning model to reflect the whole complexity of the real world. In this paper, we proposed a framework of intelligent agent based neural network classification model to solve the problem of gap between two applicable flows of intelligent multi agent technology and learning model from real environment. We consider the new Supervised Multilayers Feed Forward Neural Network (SMFFNN) model as an intelligent classification for learning model in the framework. The framework earns the information from the respective environment and its behavior can be recognized by the weights. Therefore, the SMFFNN model that lies in the framework will give more benefits in finding the suitable information and the real weights from the environment which result for better recognition. The framework is applicable to different domains successfully and for the potential case study, the clinical organization and its domain is considered for the proposed frameworkComment: 7 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423, http://sites.google.com/site/ijcsis

Using specification and description language to represent users’ profiles in OMNET++ simulations

Omnet++ is a powerful and open-source simulation tool which is basically intended to model discrete-event systems. In particular, Omnet++ is extensively used to model and simulate computer networks. Typically, when a Wide Area Network needs to be modeled, different assumptions are made in order to simplify the complexity associated with human behavior. Nevertheless, human behavior can also be modeled, at least to some extent, by using Multi Agent Systems (MAS). This paper presents a methodology that allows connecting a MAS model –which accounts for human behavior–, with a standard Omnet++ model –which represents the behavior of a computer network. The approach presented here can be useful to obtain a better representation of the human behavior through a MAS model when using Omnet++. Furthermore, our approach simplifies the modeling process by splitting the complexity of a real system into two different parts. Therefore, on the one hand computer scientists can focus on the Omnet++ model while, on the other hand, specialists in human behavior can focus on the MAS model. Finally, our approach also facilitates the distribution of the models among different computers.Postprint (published version