Multi-agent Architecture Research Articles

Reinforcement Learning for Building Energy Optimization Through Controlling of Central HVAC System

This paper presents a novel methodology to control HVAC system and minimize energy cost on the premise of satisfying power system constraints. A multi-agent architecture based on game theory and reinforcement learning is developed so as to reduce the cost and computational complexity of the microgrid. The multi-agent architecture comprising agents, state variables, action variables, reward function and cost game is formulated. The paper fills the gap between multi-agent HVAC systems control and power system optimization and planning. The results and analysis indicate that the proposed algorithm is beneficial to deal with the problem of “curse of dimensionality” for multi-agent microgrid HVAC system control and speed up learning of unknown power system conditions.

Data-driven Interactive Multiobjective Optimization: Challenges and a Generic Multi-agent Architecture

Abstract In many decision making problems, a decision maker needs computer support in finding a good compromise between multiple conflicting objectives that need to be optimized simultaneously. Interactive multiobjective optimization methods have a lot of potential for solving such problems. However, the growth of complexity in problem formulations and the abundance of data bring new challenges to be addressed by decision makers and method developers. On the other hand, advances in the field of artificial intelligence provide opportunities in this respect. We identify challenges and propose directions of addressing them in interactive multiobjective optimization methods with the help of multiple intelligent agents. We describe a generic architecture of enhancing interactive methods with specialized agents to enable more efficient and reliable solution processes and better support for decision makers.

Genetic algorithm-based intelligent multiagent architecture for extracting information from hidden web databases

Though there are enormous amount of information available in the web, only very small portion of the available information is visible to the users. Due to the non-visibility of huge information, the traditional search engines cannot index or access all information present in the web. The main challenge in the mining of the relevant information from a huge hidden web database is to identify the entry points to access the hidden web databases. The existing web crawlers cannot retrieve all information from the hidden web databases. To retrieve all the relevant information from the hidden web, this paper proposes an architecture that uses genetic algorithm and intelligent agents for accessing hidden web databases. The proposed architecture is termed as genetic algorithm-based intelligent multi-agent system (GABIAS). The experimental results show that the proposed architecture provides better precision and recall than the existing web crawlers.

A multi-agent architecture for mobile sensing systems

Mobile sensing systems based on smartphones, connected vehicles and integrated sensors on new mobile devices have become an important alternative for the development of intelligent services in large urban environments. Massive data collection and its real-time analysis are essential for big cities to move towards energy efficiency, sustainable mobility, protection of the environment and economic sustainability. Current research and applications are mainly focused on the use of individual devices and the analysis of information on a single domain (e.g. activity recognition). However, it is still necessary to provide solutions for social problems based on smart mobile devices connected to the city. In this paper, we present an architecture for mobile sensing systems in large cities based on the intelligent agent paradigm and multi-agent systems. The presented platform provides support for multi-purpose machine learning services, implementing expert learning agents in each domain where the system collects data. Furthermore, the main challenges in mobile sensing systems such as scalability in crowded environments, handling of a large amount of data and the increasing appearance of sensing devices are addressed by the architecture due to the agent paradigm and multi-agent systems suit these demands naturally.

Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems

Currently, enhancing sustainability, and in particular reducing energy consumption, is a huge challenge for manufacturing enterprises. The vision of the fourth industrial revolution (so-called “industry 4.0”) is not only to optimize production and minimize costs, but also to reduce energy consumption and enhance product life-cycle management. To address this challenge, a multi-agent architecture aimed at elaborating predictive and reactive energy-efficient scheduling through collaboration between cyber physical production and energy systems is proposed in this paper. Smart, sustainable decision tools for cyber physical production systems (CPPS) and cyber physical energy systems (CPES) are proposed. The decision tools are data-driven, agent-based models with dynamic interaction. The main aim of agent behaviours in the cyber part of CPPS is to find a predictive and reactive energy-efficient schedule. The role of agents in CPES is to control the energy consumption of connected factories and switch between the different renewable energy sources. Dynamic mechanisms in CPPS and CPES are proposed to adjust the energy consumption of production systems based on the availability of the renewable energy. The proposed approach was validated on a physically distributed architecture using networked embedded systems and real-time data sharing from connected sensors in each cyber physical systems. A series of instances inspired from the literature were tested to assess the performance of the proposed method. The results prove the efficiency of the proposed approach in adapting the energy consumption of connected factories based on a real-time energy threshold.

A multi-agent architecture for scheduling in platform-based smart manufacturing systems

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

Towards increasing reliability of Amazon EC2 spot instances with a fault-tolerant multi-agent architecture

Towards increasing reliability of Amazon EC2 spot instances with a fault-tolerant multi-agent architecture

Multi-agent architecture for information retrieval and intelligent monitoring by UAVs in known environments affected by catastrophes

The consequences of natural or man-made catastrophes can be devastating. To minimize its impact, it is crucial to carry out a rapid analysis of the affected environment in the moments after they occur, especially from the perspective of alert notification or crisis management. In this context, the use of UAVs, understood as the technological basis on which intelligent systems capable of providing support to rescue teams is built, has positively contributed to face this challenge. In this article the design of a multi-agent architecture which enables the deployment of systems made up of intelligent agents that can monitor environments affected by a catastrophe and provide support to human staff in the decision-making process is proposed. These environments, known in advance, are characterized through a set of points of interests that are critical from the point of view of aerial surveillance and monitoring. To conduct an intelligent information analysis, a formal model of normality analysis is employed, which makes possible the definition of surveillance components. These represent the knowledge bases of the agents responsible for monitoring environments. Likewise, the architecture envisages communication and cooperation mechanisms between the different agents, as the basis for fusing information to assess the overall level of risk of the monitored environment. A case study is presented in which the spread of toxic smoke in an industrial complex which has just suffered a hypothetical earthquake is monitored.

Multi-agent system architectures for collaborative prognostics

This paper provides a methodology to assess the optimal multi-agent architecture for collaborative prognostics in modern fleets of assets. The use of multi-agent systems has been shown to improve the ability to predict equipment failures by enabling machines with communication and collaborative learning capabilities. Different architectures have been postulated for industrial multi-agent systems in general. A rigorous analysis of the implications of their implementation for collaborative prognostics is essential to guide industrial deployment. In this paper, we investigate the cost and reliability implications of using different multi-agent systems architectures for collaborative failure prediction and maintenance optimization in large fleets of industrial assets. Results show that purely distributed architectures are optimal for high-value assets, while hierarchical architectures optimize communication costs for low-value assets. This enables asset managers to design and implement multi-agent systems for predictive maintenance that significantly decrease the whole-life cost of their assets.

A Multi-Agent Architecture for the Design of Hierarchical Interval Type-2 Beta Fuzzy System

This paper presents a new methodology for building and evolving hierarchical fuzzy systems. For the system design, a tree-based encoding method is adopted to hierarchically link low-dimensional fuzzy systems. Such tree structural representation has by nature a flexible design offering more adjustable and modifiable structures. The proposed hierarchical structure employs a type-2 beta fuzzy system to cope with the faced uncertainties, and the resulting system is called the hierarchical interval type-2 beta fuzzy system (HT2BFS). For the system optimization, two main tasks of structure learning and parameter tuning are applied. The structure learning phase aims to evolve and learn the structures of a population of the HT2BFS in a multi-objective context taking into account the optimization of both the accuracy and the interpretability metrics. The parameter tuning phase is applied to refine and adjust the parameters of the system. To accomplish these two tasks in the most optimal way, we further employ a multi-agent architecture to provide both a distributed and a cooperative management of the optimization tasks. Agents are divided into two different types based on their functions: a structure agent and a parameter agent. The main function of the structure agent is to perform a multi-objective evolutionary structure learning step by means of the multi-objective immune programming algorithm. The parameter agents have the function of managing different hierarchical structures simultaneously to refine their parameters by means of the hybrid harmony search algorithm. In this architecture, agents use cooperation and communication concepts to create high-performance HT2BFSs. The performance of the proposed system is evaluated by several comparisons with various state-of-the-art approaches on noise-free and noisy time series prediction datasets and regression problems. The results clearly demonstrate a great improvement in accuracy rate, convergence speed, and the number of used rules as compared to other existing approaches.

An Intelligent Security Defensive Model of SCADA Based on Multi-Agent in Oil and Gas Fields

Supervisory Control and Data Acquisition (SCADA) system in the modern industrial automation control network is facing an increasing number of serious security threats. In order to meet the security defense requirements of oil and gas SCADA system, an intelligent security defense model based on multi-agent was designed by analyzing the security risks in oil and gas SCADA system and combining the advantages of multi-agent technology in distributed intrusion detection system. First, the whole structure of this model was divided into three layers: monitoring layer, decision layer and control layer. Then, the defense model was verified by C4.5 decision tree algorithm, and obtained a good result. Finally, the security defense prototype system of large-scale oil and gas SCADA system based on this model was realized. Results demonstrate that the application of multi-agent technology in the security defense of oil and gas SCADA system can achieve more comprehensive defense, more accurate detection, which can handle large-scale distributed attacks and improve the robustness and stability of security defenses. This study makes full use of the multi-agent architecture and has the advantage of accurate detection, high detection efficiency and timely response.

A scalable multiagent architecture for monitoring IoT devices

In recent years, the use of wireless devices has experienced huge grown, mainly due to the decrease in cost, size and energy consumption. Research in the Internet of Things have concentrated their efforts on how to monitor these devices taking into account both scalability and efficiency while searching and updating continuously device information. In order to do that, current alternatives combine a widely recognized method, the data aggregation, and a widely adopted distributed structure, called Distributed Hash Table, which form groups of devices, saving energy by minimizing the number of transmissions. However, scalability is still a key challenge when the group comprises a large number of devices. In this paper, we propose a scalable architecture that creates agents to distribute the responsibility of managing groups and the interaction among them, such as merging and splitting, and shares the data aggregation with the devices of group frontier. Experimental results showed the viability of adopting this architecture if compared to the most widely used current approaches.

CLOE: a cross-lingual ontology enrichment using multi-agent architecture

ABSTRACT This article proposes across-lingual ontology enrichment (CLOE) approach for enriching ontologies from amultilingual text or other ontologies. Athree-level hierarchical multi-agent model has been built, where each node is modelled as an agent. This has several advantages: ontologies can be used to enrich other ontologies in adifferent language, several ontologies can be enriched at the same time, and domain-independent. A prototype for the proposed approach has been implemented. Evaluation results are promising, showing considerable precision and recall scores. We believe that this approach is the first such attempt to enrich multiple ontologies using adomain-independent approach.

Towards Recognising Individual Behaviours from Pervasive Mobile Datasets in Urban Spaces

Mobile phone network data, routinely collected by its providers, possess very valuable encoded information about human behaviors. Intensive tourist activities in urban spaces bring smartness via mobile phone fingerprints into the understanding of an urban ecosystem. Due to the diverse processes that govern mobile communication, mining the geolocations of individuals seems to be non-trivial, tedious, and even irregular, which can lead to an incomplete trajectory. Enriching trajectories with infrastructural facilities is another challenge. We provide a unified approach, comprised of both informal and formal elements, to obtain a common framework, which maps pervasive datasets into a collection of individual patterns in urban spaces, to obtain context-enhanced trajectory reconstructions. Through the algorithmization of the approach, we acquire a study that provides new insights on individual and anonymized tourist behaviors. In order to obtain individual behaviors, it is necessary to carry out an arduous extraction process. We propose a multi-agent system architecture and predefined message streams, which are transported on a message-broker platform. We also propose all of the basic algorithms that compose the prototype of the entire multi-agent system. All algorithms were formally analyzed due to termination and time complexity. System evaluation, together with a few basic experiments, was also carried out. The performance evaluation results authenticate system feasibility, credibility, and vitality. Those factors prove its effectiveness and the possibility to build the target system, whilst supporting every urban ecosystem. The system would also strongly influence municipal services to understand urban context and operate more effectively in order to support tourist activities to become safer and more comfortable.

ARIES: An Autonomous Controller For Multirobot Cooperation

This paper is focused in cooperative robotics research, particularly for space exploration missions. To do this, we proposed an autonomous controller developed to deploy cooperative heterogeneous robot systems which combine and coordinate their functionalities to achieve scientific targets in challenging scenarios. Contrary to other autonomous controllers, ARIES is a multiagent architecture which brings the T-REX framework and the hierarchical leader -follower approach together. This scheme allows ARIES to provide a distributed execution and a centralized deliberation into the leader, as well as scalability and flexibility to control multiple robotic platforms. Also, the hierarchical scheme provides an affordable way to implement and deploy fail recover mechanisms in ARIES to create fault-tolerant instances of the architecture.

A Multi-Agent Architecture for Data Analysis

ActoDatA (Actor Data Analysis) is an actor-based software library for the development of distributed data mining applications. It provides a multi-agent architecture with a set of predefined and configurable agents performing the typical tasks of data mining applications. In particular, its architecture can manage different users’ applications; it maintains a high level of execution quality by distributing the agents of the applications on a dynamic set of computational nodes. Moreover, it provides reports about the analysis results and the collected data, which can be accessed through either a web browser or a dedicated mobile APP. After an introduction about the actor model and the software framework used for implementing the software library, this article underlines the main features of ActoDatA and presents its experimentation in some well-known data analysis domains.

Learning process: Multi-Agent Tutoring System

A multi-agent architecture has been developed for tutorial assignation scheduling. It has two main types of agents: the students and the teachers. These two are coordinated by an algorithm which assigns the classes in order of arrival. The architecture will provide the necessary tools to the students, so they get the maximum profit from the tutorials. Students and Lecturers can coordinate their tutorial meeting in an efficient way with the help of the multi-agent system.

Multi-agent system for selecting images based on the gender and age

This paper presents a multi-agent system that is able to search people on a database of images recognizing patterns of facial features on each person, based on the main features of the face (eyes, nose and mouth). Using that multi-agent architecture, the system can do the work faster applying Fisherfaces algorithm for the face recognition and classification. This technology can be used for several purposes like specific ads in each user group to suit better their interests or search for the age and gender of people that usually go to different places like malls or shops.

Social-Aware Driver Assistance Systems for City Traffic in Shared Spaces

Shared spaces are gaining presence in cities, where a variety of players and mobility types (pedestrians, bicycles, motorcycles, and cars) move without specifically delimited areas. This makes the traffic they comprise challenging for automated systems. The information traditionally considered (e.g., streets, and obstacle positions and speeds) is not enough to build suitable models of the environment. The required explanatory and anticipation capabilities need additional information to improve them. Social aspects (e.g., goal of the displacement, companion, or available time) should be considered, as they have a strong influence on how people move and interact with the environment. This paper presents the Social-Aware Driver Assistance System (SADAS) approach to integrate this information into traffic systems. It relies on a domain-specific modelling language for social contexts and their changes. Specifications compliant with it describe social and system information, their links, and how to process them. Traffic social properties are the formalization within the language of relevant knowledge extracted from literature to interpret information. A multi-agent system architecture manages these specifications and additional processing resources. A SADAS can be connected to other parts of traffic systems by means of subscription-notification mechanisms. The case study to illustrate the approach applies social knowledge to predict people’s movements. It considers a distributed system for obstacle detection and tracking, and the intelligent management of traffic signals.

A multi-level generic multi-agent architecture for supervision of collective cyber-physical systems

A multi-level generic multi-agent architecture for supervision of collective cyber-physical systems