228,884 research outputs found
Self-organising agent communities for autonomic resource management
The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes
Creating agent platforms to host agent-mediated services that share resources
After a period where the Internet was exclusively filled with content,
the present
efforts are moving towards services, which handle the raw information to
create
value from it. Therefore labors to create a wide collection of
agent-based services
are being perfomed in several projects, such as Agentcities does.
In this work we present an architecture for agent platforms named
a-Buildings. The
aim of the proposed architecture is to ease the creation, installation,
search and
management of agent-mediated services and the share of resources among
services.
To do so the a-Buildings architecture creates a new level of abstraction
on top of
the standard FIPA agent platform specification.
Basically, an a-Building is a service-oriented platform which offers a
set of
low level services to the agents it hosts. We define low level services
as those
required services that are neccesary to create more complex high level
composed
services.Postprint (published version
Is a Semantic Web Agent a Knowledge-Savvy Agent?
The issue of knowledge sharing has permeated the field of distributed AI and in particular, its successor, multiagent systems. Through the years, many research and engineering efforts have tackled the problem of encoding and sharing knowledge without the need for a single, centralized knowledge base. However, the emergence of modern computing paradigms such as distributed, open systems have highlighted the importance of sharing distributed and heterogeneous knowledge at a larger scale—possibly at the scale of the Internet. The very characteristics that define the Semantic Web—that is, dynamic, distributed, incomplete, and uncertain knowledge—suggest the need for autonomy in distributed software systems. Semantic Web research promises more than mere management of ontologies and data through the definition of machine-understandable languages. The openness and decentralization introduced by multiagent systems and service-oriented architectures give rise to new knowledge management models, for which we can’t make a priori assumptions about the type of interaction an agent or a service may be engaged in, and likewise about the message protocols and vocabulary used. We therefore discuss the problem of knowledge management for open multi-agent systems, and highlight a number of challenges relating to the exchange and evolution of knowledge in open environments, which pertinent to both the Semantic Web and Multi Agent System communities alike
Towards trusted volunteer grid environments
Intensive experiences show and confirm that grid environments can be
considered as the most promising way to solve several kinds of problems
relating either to cooperative work especially where involved collaborators are
dispersed geographically or to some very greedy applications which require
enough power of computing or/and storage. Such environments can be classified
into two categories; first, dedicated grids where the federated computers are
solely devoted to a specific work through its end. Second, Volunteer grids
where federated computers are not completely devoted to a specific work but
instead they can be randomly and intermittently used, at the same time, for any
other purpose or they can be connected or disconnected at will by their owners
without any prior notification. Each category of grids includes surely several
advantages and disadvantages; nevertheless, we think that volunteer grids are
very promising and more convenient especially to build a general multipurpose
distributed scalable environment. Unfortunately, the big challenge of such
environments is, however, security and trust. Indeed, owing to the fact that
every federated computer in such an environment can randomly be used at the
same time by several users or can be disconnected suddenly, several security
problems will automatically arise. In this paper, we propose a novel solution
based on identity federation, agent technology and the dynamic enforcement of
access control policies that lead to the design and implementation of trusted
volunteer grid environments.Comment: 9 Pages, IJCNC Journal 201
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Deep Q-learning has achieved significant success in single-agent decision
making tasks. However, it is challenging to extend Q-learning to large-scale
multi-agent scenarios, due to the explosion of action space resulting from the
complex dynamics between the environment and the agents. In this paper, we
propose to make the computation of multi-agent Q-learning tractable by treating
the Q-function (w.r.t. state and joint-action) as a high-order high-dimensional
tensor and then approximate it with factorized pairwise interactions.
Furthermore, we utilize a composite deep neural network architecture for
computing the factorized Q-function, share the model parameters among all the
agents within the same group, and estimate the agents' optimal joint actions
through a coordinate descent type algorithm. All these simplifications greatly
reduce the model complexity and accelerate the learning process. Extensive
experiments on two different multi-agent problems demonstrate the performance
gain of our proposed approach in comparison with strong baselines, particularly
when there are a large number of agents.Comment: 7 pages, 5 figures, DAI 201
Coordination approaches and systems - part I : a strategic perspective
This is the first part of a two-part paper presenting a fundamental review and summary of research of design coordination and cooperation technologies. The theme of this review is aimed at the research conducted within the decision management aspect of design coordination. The focus is therefore on the strategies involved in making decisions and how these strategies are used to satisfy design requirements. The paper reviews research within collaborative and coordinated design, project and workflow management, and, task and organization models. The research reviewed has attempted to identify fundamental coordination mechanisms from different domains, however it is concluded that domain independent mechanisms need to be augmented with domain specific mechanisms to facilitate coordination. Part II is a review of design coordination from an operational perspective
Programming agent-based demographic models with cross-state and message-exchange dependencies: A study with speculative PDES and automatic load-sharing
Agent-based modeling and simulation is a versatile and promising methodology to capture complex interactions among entities and their surrounding environment. A great advantage is its ability to model phenomena at a macro scale by exploiting simpler descriptions at a micro level. It has been proven effective in many fields, and it is rapidly becoming a de-facto standard in the study of population dynamics. In this article we study programmability and performance aspects of the last-generation ROOT-Sim speculative PDES environment for multi/many-core shared-memory architectures. ROOT-Sim transparently offers a programming model where interactions can be based on both explicit message passing and in-place state accesses. We introduce programming guidelines for systematic exploitation of these facilities in agent-based simulations, and we study the effects on performance of an innovative load-sharing policy targeting these types of dependencies. An experimental assessment with synthetic and real-world applications is provided, to assess the validity of our proposal
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