Multiagent Self-organization Research Articles

ТРАНСФОРМАЦИЯ ПОДХОДОВ К УПРАВЛЕНИЮ СОВРЕМЕННЫМИ СЕТЯМИ СВЯЗИ

Modern communication networks for special purposes —communication networks designed for the needs of state authorities, the needs of the country's defense, state security and law enforcement - these are complex decentralized network structures that use the resources of public communication networks and commercial communication protocols. The management of such structures requires a transition to new management models, since hierarchical and distributed static network management systems, such as TMN and OSS/BSS, no longer fully meet the requirements for managing state and military networks. These systems provide only partial automation of decision-making in network management. They are still not ready for constant changes and are not able to adapt to them because of their rigidly static nature. Also, over time, control systems will encounter such a problem as the heterogeneity and mobility of a huge number of network devices in the - mobile control and managed devices, drones, wearable networks, etc. As a result of all of the above, it becomes necessary to search for new models and methods for building telecommunication management systems. From the point of view of managing such infrastructures, the transition to distributed cognitive management models, as well as models that take into account the volume and context of data generated by control systems in the process of their functioning, is gaining importance. In this paper, the authors considered the evolution of networks control models: starting with traditional approaches and moving on to modern models and methods for managing telecommunications of the future. In the course of the study, in addition to studying traditional approaches to telecommunication management, a cognitive model based on multi-agent self-organization was identified and the relevance of this approach to managing post-NGN networks was examined.

Model for Automatic Speech Recognition Using Multi-Agent Recursive Cognitive Architecture

A concept of a fundamentally new approach to the development of speech recognition systems is proposed, as applications built based on existing approaches are not effective enough when used in noisy conditions and cocktail party situations. The architecture of the speech recognition system in an environment with several speakers based on multi-agent recursive cognitive models with imitation of the attention mechanism is constructed. The speech recognition system allows to model selectivity of perception in speech peculiarities for a speaker using multi-agent self-organization. Principles for selective signature processing inside sound modality are defined. They allow to tune on a speaker. Articulatory primitives were chosen as minimal functional pattern in the speech recognition problem. Due to multi-agent nature, use of space-time characteristics and self-learning this approach allow us to separate from each other and analyze sounds of different nature. Screenshots of the cognitive architecture of the speech recognition system based on multi-agent models of semantics are presented.

Modeling Multi-Agent Self-Organization through the Lens of Higher Order Attractor Dynamics.

Social interaction occurs across many time scales and varying numbers of agents; from one-on-one to large-scale coordination in organizations, crowds, cities, and colonies. These contexts, are characterized by emergent self-organization that implies higher order coordinated patterns occurring over time that are not due to the actions of any particular agents, but rather due to the collective ordering that occurs from the interactions of the agents. Extant research to understand these social coordination dynamics (SCD) has primarily examined dyadic contexts performing rhythmic tasks. To advance this area of study, we elaborate on attractor dynamics, our ability to depict them visually, and quantitatively model them. Primarily, we combine difference/differential equation modeling with mixture modeling as a way to infer the underlying topological features of the data, which can be described in terms of attractor dynamic patterns. The advantage of this approach is that we are able to quantify the self-organized dynamics that agents exhibit, link these dynamics back to activity from individual agents, and relate it to other variables central to understanding the coordinative functionality of a system's behavior. We present four examples that differ in the number of variables used to depict the attractor dynamics (1, 2, and 6) and range from simulated to non-simulated data sources. We demonstrate that this is a flexible method that advances scientific study of SCD in a variety of multi-agent systems.

Better manufacturing process organization using multi-agent self-organization and co-evolutionary classifier systems: The multibar problem

Manufacturing processes need their entities to coordinate and discoordinate at different times of the process, in order to achieve the adequate manufacturing timing some jobs need to be done in a sequence or can be done in parallel, using different machines. This paper introduces a complex discoordination problem: the multibar problem, based on the “El Farol” bar problem, devised to test enhanced complexity for multi-agent systems. A multi-agent system that learns based on the extended classifier system (MAXCS) is used for the simulation. Different classifier population sizes are used to help agent adaptation. MAXCS adapts to the all possible configurations of the different bars tested in 20 different experiments in different ways. The first set of experiments proved that MAXCS is able to adapt to the multibar problem with the emergence of several agents switching bars (vacillating agents). The preliminary experiments yielded the hypothesis of the irrelevance of the classifiers’ rule conditions, and their evolution to influence the result. MAXCS is then compared with multi-agent Q-learning (MAQL). These experiments demonstrate the need to use evolutionary computation for better adaptation, rather than just a reinforcement learning algorithm, proving wrong the previous hypothesis. The MAXCS–MAQL comparison showed that the use of rule conditions, combined with the genetic algorithm, determines whether there is only one or several vacillating agents at the same time throughout the experiment. The solution scales when 133 agents are used for the problem. After this study, it can be concluded that the multibar problem can become an interesting benchmark for multi-agent learning and provide manufacturing processes with suitable coordination solutions.

Self-organization and multiagent systems: II. Applications and the development technology

Major trends in the development of modern information technologies are to a large extend determined by important practical problems that arise in economics, ecology, safety of society and individuals, and in other fields. Even though these problems seem to be quite different and the requirements for their software implementation are also different, they have many common features, which imply the most stringent requirements for modern information technologies. These features were analyzed in the first part of the present paper. That analysis showed that the new requirements for the model and software implementation of such problems are best met by the multiagent self-organizing system model. In this paper, we consider examples of using this model in various applications and describe their architectures and software implementation; in particular, multiagent self-organization models as applied for flood forecasting and planning and operational enterprise management are described. New capabilities of multiagent self-organizing systems are demonstrated using a self-learning system for detecting intrusions into computer networks as an example. Here, the problem of self-configuration of an overlay network is actually solved. The capabilities of a multiagent self-organizing system in large-scale control in real time are demonstrated using adaptive traffic control in large cities. For the software implementation of multiagent self-organizing systems, special development tools that are different from the existing ones are needed because the conventional top-down development paradigm is inappropriate for self-organizing architectures. The cause is that the global behavior of a multiagent self-organizing system emerges due to local interactions; therefore, it cannot be predicted in advance. For that reason, the bottom-up development model is more appropriate for such systems. In this paper, we give a brief review of the models and approaches proposed for this purpose. One of the promising approaches based on the use of the so-called self-organization design patterns is described in more detail. Results of using the multiagent self-organization model are discussed and prospects of its practical application are estimated.

Self-organization and multiagent systems: I. Models of multiagent self-organization

Nowadays, there are many problems whose complexity is much higher than the capabilities of modern information technologies. Such problems arise in economics, ecology, managing state-level infrastructures and global computer and telecommunication systems, ensuring the safety of society, and in many other fields. Even though these problems seem to be quite different, they have many common features, which imply common difficulties in their solution. These features are as follows: they are large-scale problems, they are open, have unpredictable dynamics and complex structure, include mobile components, and some others. The management in such systems is a challenging task, which requires a revision of modern views, models, architectures, and development technologies. A response to this challenge is the increasing activity in the field of principles and mechanisms of self-organization and in the software tools for their development. Although the paradigm of self-organizing control systems is not new, it is now at a new step of development, which involves, in particular, its integration with the multiagent system paradigm. The purposes of this paper are to analyze the state-of-the art in the field of multiagent self-organizing systems, to provide a critical review of the available applications, analyze development techniques, and generalize the results obtained in this field. The paper consists of two parts. In the first part, we discuss the modern interpretation of the principles of self-organization is analyzed, and the reasons for which the integration of these principles with the achievements in the field of multiagent systems provides a new impetus to the development of information technologies in the context of most complex modern applications. A systematization and description of the self-organization models and mechanisms implemented in the framework of the multiagent architecture is given, and biological self-organization mechanisms are discussed. Applications of self-organizing multiagent systems in telecommunication, grid resource management, and routing in computer networks with dynamic topology, as well as applications in distributed learning and in detecting intrusions into computer networks are described.

Using multiagent self-organization techniques for seeking information in virtual social communities

This paper proposes an approach which aims at improving information access skills, services in large networks. It proposes a self-organized multiagent analysis of the problem allowing to reduce the number of messages transmitted for a skill search. The MWAC Multi-Wireless-Agent Communication model is extended to take some specificities of social networks into account, like the information held by the social network members and their connections with the others.The network of agents may represent a social network. Each agent holds information about members agents and is linked with other agents which represent its neighborhood. Each member builds a local network representation according to its needs. A global view of a social network is not dynamically available and furthermore not desirable. Our approach is thus based on a decentralized interrogation of the network and on organizational structures detection.With this method, a search does not need to flood all the network with the query because it uses the self-organized structure that emerges so, it can avoid a saturation in the network and it reduces the amount of transmitted messages. In order to show the interest of the approach, our proposition is validated by several simulations. The applications of this work are related to skill searching in a virtual social network.

A multiagent approach to manage communication in wireless instrumentation systems

This paper deals with the application of the emergence paradigm to communication management in wireless instrumentation systems. We exploit the emergence resulting from a multiagent self-organization process. We propose a model of autonomous adaptive multiagent organization to route the information in the best way, taking into consideration strong constraints on energy resources, dynamical topology, and asymmetric communication links. Simulation results are discussed, showing that the multiagent approach based on self-organization is an interesting alternative to traditional protocol approaches. The distributed artificial intelligence mechanism is implemented as a generic middleware. We apply our approach to the instrumentation of an underground hydrographic system.

Une approche multi-agent pour la gestion de la communication dans les réseaux de capteurs sans fil

This paper deals with multiagent self-organization giving adaptive features to a distributed system embodied in an agressive environment. It proposes an original approach of open multiagent systems in the context of wireless networks of intelligent sensors. Such networks are composed by sensors with independant energy sources. These networks need routing techniques which minimize communication energy cost with a strong fault tolerant feature. The paper presents finally the simulation platform, which we built, who allows us to compare our solution based on self-organization and common ad hoc routing protocols.