Agent-based virtual organization architecture

The purpose of this paper is to present the applicability of THOMAS, an architecture specially designed to model agent-based virtual organizations, in the development of a multiagent system for managing and planning routes for clients in a mall. In order to build virtual organizations, THOMAS offers mechanisms to take into account their structure, behaviour, dynamic, norms and environment. Moreover, one of the primary characteristics of the THOMAS architecture is the use of agents with reasoning and planning capabilities. These agents can perform a dynamic reorganization when they detect changes in the environment. The proposed architecture is composed of a set of related modules that are appropriate for developing systems in highly volatile environments similar to the one presented in this study. This paper presents THOMAS as well as the results obtained after having applied the system to a case study

A genetic approach for long term virtual organization distribution

Electronic versíon of an article published as International Journal on Artificial Intelligent Tools, Volume 20, issue 2, 2011. 10.1142/S0218213011000152. © World Scientific Publishing Company[EN] An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios. © 2011 World Scientific Publishing Company.This work is supported by TIN2008-04446, TIN2009-13839-C03-01, CSD2007-00022 and FPU grant AP2008-00600 of the Spanish government, and PROMETEO 2008/051 of the Generalitat Valenciana.Sánchez Anguix, V.; Valero Cubas, S.; García Fornes, AM. (2011). A genetic approach for long term virtual organization distribution. International Journal on Artificial Intelligence Tools. 20(2):271-295. https://doi.org/10.1142/S0218213011000152S27129520

Flocking Behaviour: Agent-Based Simulation and Hierarchical Leadership

We have studied how leaders emerge in a group as a consequence of interactions among its members. We propose that leaders can emerge as a consequence of a self-organized process based on local rules of dyadic interactions among individuals. Flocks are an example of self-organized behaviour in a group and properties similar to those observed in flocks might also explain some of the dynamics and organization of human groups. We developed an agent-based model that generated flocks in a virtual world and implemented it in a multi-agent simulation computer program that computed indices at each time step of the simulation to quantify the degree to which a group moved in a coordinated way (index of flocking behaviour) and the degree to which specific individuals led the group (index of hierarchical leadership). We ran several series of simulations in order to test our model and determine how these indices behaved under specific agent and world conditions. We identified the agent, world property, and model parameters that made stable, compact flocks emerge, and explored possible environmental properties that predicted the probability of becoming a leader.Flocking Behaviour; Hierarchical Leadership; Agent-Based Simulation; Social Dynamics

The CMS Integration Grid Testbed

The CMS Integration Grid Testbed (IGT) comprises USCMS Tier-1 and Tier-2 hardware at the following sites: the California Institute of Technology, Fermi National Accelerator Laboratory, the University of California at San Diego, and the University of Florida at Gainesville. The IGT runs jobs using the Globus Toolkit with a DAGMan and Condor-G front end. The virtual organization (VO) is managed using VO management scripts from the European Data Grid (EDG). Gridwide monitoring is accomplished using local tools such as Ganglia interfaced into the Globus Metadata Directory Service (MDS) and the agent based Mona Lisa. Domain specific software is packaged and installed using the Distrib ution After Release (DAR) tool of CMS, while middleware under the auspices of the Virtual Data Toolkit (VDT) is distributed using Pacman. During a continuo us two month span in Fall of 2002, over 1 million official CMS GEANT based Monte Carlo events were generated and returned to CERN for analysis while being demonstrated at SC2002. In this paper, we describe the process that led to one of the world's first continuously available, functioning grids.Comment: CHEP 2003 MOCT01

Multi Site Coordination using a Multi-Agent System

A new approach of coordination of decisions in a multi site system is proposed. It is based this approach on a multi-agent concept and on the principle of distributed network of enterprises. For this purpose, each enterprise is defined as autonomous and performs simultaneously at the local and global levels. The basic component of our approach is a so-called Virtual Enterprise Node (VEN), where the enterprise network is represented as a set of tiers (like in a product breakdown structure). Within the network, each partner constitutes a VEN, which is in contact with several customers and suppliers. Exchanges between the VENs ensure the autonomy of decision, and guarantiee the consistency of information and material flows. Only two complementary VEN agents are necessary: one for external interactions, the Negotiator Agent (NA) and one for the planning of internal decisions, the Planner Agent (PA). If supply problems occur in the network, two other agents are defined: the Tier Negotiator Agent (TNA) working at the tier level only and the Supply Chain Mediator Agent (SCMA) working at the level of the enterprise network. These two agents are only active when the perturbation occurs. Otherwise, the VENs process the flow of information alone. With this new approach, managing enterprise network becomes much more transparent and looks like managing a simple enterprise in the network. The use of a Multi-Agent System (MAS) allows physical distribution of the decisional system, and procures a heterarchical organization structure with a decentralized control that guaranties the autonomy of each entity and the flexibility of the network

An approach to inter-organizational workflow management in an electronic institution

In a virtual organization, different business partners (individual organizations) cooperate in order to achieve a common goal. The coordination of the corresponding inter-organizational workflow is an important issue. In this paper an approach towards managing the operational embodiment of a manufacturing consortium is presented. This approach is conceptualized as a service within an Electronic Institution framework providing several agent-based services related with the formation and operation of virtual organizations. The behavior of the inter-organizational workflow management service is presented, modeled as an extension to a contract monitoring service. The paper also deals with the information exchange needs between these services and with the partners involved in a virtual organization contractual relationship.The work reported in this paper was supported by the FCT (Fundação para a Ciência e a Tecnologia) Project POSC/EIA/57672/2004

BOUNDARY ORGANIZATIONS: AN EFFICIENT STRUCTURE FOR MANAGING KNOWLEDGE IN DECISION-MAKING UNDER UNCERTAINTY

Modern environmental issues imply that decision-makers take into account opinions from experts of different spheres. Boundary organizations are institutions able to cross the gap between different areas of expertise and to act beyond the boundaries while remaining accountable to each side: by encouraging a flow of useful information, they permit an exchange to take place while maintaining the authority of each side, in order to provide a better knowledge and understanding of a situation characterized by uncertainty. Though never formally proved, this hypothesis is widely accepted based on the observation of existing boundary organizations. Through a multi-agent simulation, it is possible to assess their impact on the diffusion of opinions among experts. This virtual interaction of heterogeneous agents based on a model of continuous opinion dynamics over two dimensions, shows that boundary organizations have a significant quantitative impact on the diversity of opinions expressed and the number of experts agreeing to each emerging position.boundary organization, opinion, knowledge diffusion, multi-agent system, Agribusiness, Labor and Human Capital, Public Economics,

Smart Events and Primed Agents

We describe a new organization for virtual human responses to dynamically occurring events. In our approach behavioral responses are enumerated in the representation of the event itself. These Smart Events inform an agent of plausible actions to undertake. We additionally introduce the notion of agent priming, which is based on psychological concepts and further restricts and simplifies action choice. Priming facilitates multi-dimensional agents and in combination with Smart Events results in reasonable, contextual action selection without requiring complex reasoning engines or decision trees. This scheme burdens events with possible behavioral outcomes, reducing agent computation to evaluation of a case expression and (possibly) a probabilistic choice. We demonstrate this approach in a small group scenario of agents reacting to a fire emergency

Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems