Catálogo de publicaciones - libros

Adaptation is an essential requirement for self–organizing multi–agent systems functioning in unknown dynamic environments. Adaptation allows agents to change their actions in response to environmental changes or actions of other agents in order to improve overall system performance, and remain robust even while a sizeable fraction of agents fails. In this paper we present and study a simple model of adaptation for task allocation problem in a multi–robot system. In our model robots have to choose between two types of task, and the goal is to achieve desired task division without any explicit communication between robots. Robots estimate the state of the environment from repeated local observations and decide what task to choose based on these observations. We model robots and observations as stochastic processes and study the dynamics of individual robots and the collective behavior. We validate our analysis with numerical simulations.

Robotic soccer remains an area of active research owing to the difficulties of dynamic team formation and hard real time constraints regarding planning. Much of the existing research relies upon a central agency for coordination. Insect societies distribute work and allocate roles without a need for such a central agency and are robust with respect to changing environments and available agent resources. This paper explores the use of insect-inspired division of labour principles to robot soccer, highlighting the flexibility of the approach and ability to adapt to a wide range of soccer playing strategies.

In recent years, mobile ad-hoc networks (MANET’s) have been deployed in various scenarios, but their scalability is severely restricted by the human operators’ ability to configure and manage the network in the face of rapid change of the network structure and demand patterns. In this paper, we present a self-organizing approach to MANET management based on stigmergic agents and demonstrate how to analyze its performance under different deployment assumptions. Our results emphasize the importance of attention to notions from dynamical systems theory in designing and deploying multi-agent systems.

This paper presents the design of new manufacturing coordination and control systems based on multi-agent technology. This design aims to cope with a dynamic environment characteristic for manufacturing systems nowadays. One important feature to handle these dynamics is having the ability to plan ahead, thus avoiding problems before they occur. Therefore, one novel characteristic of the system is the ability to perform emergent forecasting. Regarding emergent forecasting, an important issue that rises from this design is how to ensure that the forecast is reliable, and on the other hand, that the system is still fast enough to react against disturbances. This paper elaborates on the agents that form the system, and proposes a way to engineer it. Moreover, this paper also describes emergent forecasting. In addition to that, the trade off between responsiveness and forecast reliability (system nervousness issue) is also discussed in this paper, altogether with an example on the design of social acceptable behaviour that aims to handle the nervousness issue. Finally, some implementation and prototyping results are presented.

Nowadays, lots of researches in and try to find new solutions to circumvent new intrusive behaviors. One of the principal weaknesses of these systems is the lack of robustness inherent in their centralized nature. Even though most of the existing (IDRSystems) use distributed data collection (host-based or network-based) many of them continue to perform data analysis centrally, thereby limiting scalability. Moreover, even if the IDRSystem is distributed in the network, its deployed elements generally remain static. With the means available to modern attackers, such as automated intrusion tools, these static and distributed elements are easily accessible. Often, this does not always contribute to improving the reliability and resistance to attacks of such static components.

This paper presents our approach for building an IDRSystem called or IDReAM for short. IDReAM combines (MAs) with self-organizing paradigms inspired by . This approach was already announced in a preceding paper [4], and the present paper describes in a more detailed way the conceptual model. All the research works relating to IDReAM are gathered in a PhD Thesis [3] which also contains the implementation results of the model and its evaluation. The present paper is limited only to the model.

In this work, we are interested in modeling a production chain following the perspective of complex adaptive system. We propose an approach, allowing a production chain to manage by itself, its own behavior, so as to satisfy the constraints imposed upon it by its environment and reach a set of predefined objectives. We propose self-organization as a mechanism, to achieve such a goal.

Textiles are omnipresent in everyday life. Their combination with microelectronics will lead to completely new applications, thus achieving elements of ambient intelligence. The integration of sensor or actuator networks, using fabrics with conductive fibres as a textile motherboard enable the fabrication of large active areas. In this paper we propose a “smart textile” based on a wired peer-to-peer network of simple information processing elements with integrated sensors or actuators. A self-organizing and fault-tolerant architecture is accomplished which detects the physical shape of the network. Routing paths are formed for data transmission, automatically circumventing defective or missing areas. The network architecture allows the smart textiles to be produced by reel-to-reel processes, cut into arbitrary shapes subsequently and implemented in systems at low installation costs. The possible applications are manifold, ranging from alarm systems to intelligent guidance systems, passenger recognition in car seats, air conditioning control in interior lining and smart wallpaper with software-defined light switches.

Companies in today’s automotive industry are under immense competitive pressure to reduce the length of their product development cycle from initial concept to begin of high-volume manufacturing. A very costly and immensely knowledge-intensive step in this process is the creation of tools and dies required to manufacture a car body of a specified design. This paper presents a novel architecture for a decision support system that streamlines the development process through the integration of a virtual assembly simulation, problem identification, and solution generation and evaluation. Following the virtual functional build process, our architecture deploys a number of multi-agent systems to provide system functionality, such as problem knowledge retrieval or solution generation and evaluation.

Motivated by trends in the industry towards transforming IT in large integrated service networks, this paper describes algorithms for the adaptive placement of “services” (as abstractions of collections of applications) in networks of “servers” (as abstractions for locations where services can be hosted). Networks comprised of interacting services as the foundation is also a vision pronounced by the Grid [9]. Manageability and “self-operation” of Grids is highly desirable. We analyze the requirements for algorithms one specific problem: the service placement problem. We discuss algorithms that neither require central control nor complete information about the system state. Algorithms are performed on a distributed overlay structure which summarizes load conditions in the underlying service network. The presented algorithms fulfill tasks of making initial placement decisions as well as initiating rearrangements when imbalance is detected. Presented algorithms have different characteristics regarding the tradeoff between accuracy (or quality) of a placement decision and its timeliness within which a decision can be made determining responsiveness.