MECA: A Multi-agent Environment for Cognitive Agents

Many fully functional multi-agent systems have been developed and put to use over the past twenty years, but few of them have been developed to succesfully facilitate social research through the use of social agents. There are three important difficulties that must be dealt with to successfully create a social system for use in social research. First, the system must have an adaptable agent framework that can successfully make intuitive and deliberative decisions much like a human participant would. Secondly, the system must have a robust architecture that not only ensures its functioning no matter the simulation, but also provides an easily understood interface that researchers can interact with while running their simulations. Finally, the system must be effectively distributed to handle the necessary number of agents that social research requires to obtain meaningful results. This paper presents our work on creating a multi-agent simulation for social agents that overcomes these three difficulties

Development of Cluster Computing –A Review

This paper presents the review work of “Cluster Computing” in depth and detail.  Cluster Computing: A Mobile Code Approach by R.B.Patel and Manpreet Singh (2006); Performance Evaluation of Parallel Applications Using Message Passing Interface In Network of Workstations Of Different Computing Powers by Rajkumar Sharma, Priyesh Kanungo and Manohar Chandwani (2011); On the Performance of MPI-OpenMP on a 12 nodes Multi-core Cluster by Abdelgadir Tageldin, Al-Sakib Khan Pathan , Mohiuddin Ahmed (2011); Dynamic Load Balancing in Parallel Processing on Non-Homogeneous Clusters by Armando E. De Giusti, Marcelo R. Naiouf, Laura C. De Giusti, Franco Chichizola (2005); Performance Evaluation of Computation Intensive Tasks in Grid by P.Raghu, K. Sriram (2011); Automatic Distribution of Vision-Tasks on Computing Clusters by Thomas Muller, Binh An Tran and Alois Knoll (2011); Terminology And Taxonomy Parallel Computing Architecture by Amardeep Singh, Satinder Pal Singh, Vandana, Sukhnandan Kaur (2011); Research of Distributed Algorithm based on Parallel Computer Cluster System by Xu He-li, Liu Yan (2010); Cluster Computing Using Orders Based Transparent Parallelizing by Vitaliy D. Pavlenko, Victor V. Burdejnyj (2007) and VCE: A New Personated Virtual Cluster Engine for Cluster Computing by Mohsen Sharifi, Masoud Hassani, Ehsan Mousavi Khaneghah, Seyedeh Leili Mirtaheri (2008). Keywords:Cluster computing, Cluster Architectures, Dynamic and Static Load Balancing, Distributed Systems, Homogeneous and Non-Homogeneous Processors, Multicore clusters, Parallel computing, Parallel Computer Vision, Task parallelism, Terminology and taxonomy, Virtualization, Virtual Cluster

Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms

In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors