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Distribution system service restoration is a process of restoring power outages through changing the on/off status of sectionalizing and tie switches within the whole power outage in a distribution system altered accordingly with its topological structure. Multi-agent Systems (MASs) can be applied for distribution system service restoration, an engineering optimization problem that can be addressed through metaheuristics, of a distribution system because service restoration planning can be made in parallel among intelligent software agents embedded inside sectionalizing and tie switches and feeders to form a distributed multi-agent environment such that the time of power restoration can be reduced. Service restoration planning can be built upon an MAS. This paper presents a three-tiered MAS-based Multi-Population Parallel Genetic Algorithm (MPPGA) and demonstrates its preliminary implementation to achieve service restoration planning for smart distribution system service restoration, which serves as a meta service restoration planner to perform efficient and fast switching operation for smart distribution system service restoration. The presented three-tiered MAS-based MPPGA is implemented, in Java programming language, on a Java Agent DEvelopment (JADE for short) platform, where in evolutionary computation (1) multiple populations are coevolved and (2) selection, crossover, and mutation operations for genetic search are parallelized. The effectiveness/feasibility of the presented three-tiered MAS-based MPPGA is demonstrated by a simulated distribution system and reported with simulation results. [ABSTRACT FROM AUTHOR]

A multi-agent system is a system of multiple interacting entities, known as intelligent agents, who possibly have different information and/or diverging interests. The agents could be robots, humans, or human teams. Opinion dynamics is a process of individual opinions in which a group of interacting agents continuously fuse their opinions on the same issue based on established rules to reach a consensus at the final stage. Historically, the idea of reaching consensus through repeated averaging was introduced by DeGroot for a structured time-invariant and synchronous environment. Since then, consensus, which is the most ubiquitous phenomenon of multi-agent systems, has become popular in various scientific fields such as biology, physics, control engineering, and social science. To some extent, a Krause mean process is a general model of opinion sharing dynamics in which the opinions are represented by vectors. In this paper, we represent opinion sharing dynamics by means of Krause mean processes generated by diagonally primitive cubic doubly stochastic matrices, and then we establish a consensus in the multi-agent system. [ABSTRACT FROM AUTHOR]

In this paper, a multi-agent system (MAS) based on the A-Team concept is proposed to solve the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). In the DRCMPSP, multiple distributed projects are considered. Hence, the local task schedule for each project and a coordination of the shared decisions are considered. The DRCMPSP belongs to the class of the strongly NP-hard optimization problems. Multi-agent system seems the natural way of solving such problems. The A-Team MAS, proposed in this paper, has been built using the JABAT environment where two types of the optimization agents are used: local and global. Local optimization agents are used to find solutions for the local projects, and global optimization agents are responsible for the coordination of the local projects and for finding the global solutions. The approach has been tested experimentally using 140 benchmark problem instances from MPSPLIB library with minimizing the Average Project Delay (APD) as global optimization criterion. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the pattern formation of objects that can be stacked and transported by distributed autonomous agents. Inspired by the social behavior of termite colonies, which often build elaborate three-dimensional structures (nest towers), this paper explores the mechanism of termite-like agents through a computational and minimalistic approach. We introduce a cellular automata model (i.e., spatially discretized) for the agents and the objects they can transport, where each agent follows a "rule" determined by the assignment of fundamental actions (move/ load/ unload) based on the state of its neighboring cells. To evaluate the resulting patterns from the viewpoint of structural complexity and agent effort, we classify the patterns using the Kolmogorov dimension and higher-order local autocorrelation, two well-known statistical techniques in image processing. We find that the Kolmogorov dimension provides a good metric for the structural complexity of a pattern, whereas the higher-order local autocorrelation is an effective means of identifying particular local patterns. [ABSTRACT FROM AUTHOR]

The world is witnessing a sudden shift in the paradigm of technology moving from centralized to decentralized approach. Centralized approach leads to single point of failure if any fault occurs and hence a whole system comes to rest. Hence, a decentralized approach like Multi-Agent System is trending now-a-days. A MAS is a collection of a number software entities (agents) working together in pursuit of specified tasks. This paper presents a comprehensive review on various aspects of multi-agent system. The paper explains the basic concepts of MAS with various ways it has been defined in literature. A comparison has been made on the standards to be followed for applying MAS. Classification of MAS architecture has been investigated and compared. Application of MAS in various areas of optimization technique, software platform and real-time simulation are listed in the paper. The paper draws attentions toward benefits and limitations of using MAS based on the survey done. Finally, after visualizing the wide scope of research in the field of MAS, an attempt has been made to identify future research avenues. [ABSTRACT FROM AUTHOR]

In today’s dynamically changing environment, we need to be able to respond in a timely manner to changes in supply chain processes. Software agents are successfully used in supply chain management tasks for a variety of purposes. The behaviour of agents is determined by the purpose of their development, and the effectiveness of the use of agents is considered in accordance with the purpose of their development. The paper presents research on the development of a multi-agent system for supply chain management, focusing on the steps of developing a multiagent system. The choice of each algorithm for agents is analysed and argued. The application of the developed multi-agent system for supply chain management is also described in the paper. The efficiency of application of the developed multi-agent system is presented as well. [ABSTRACT FROM AUTHOR]

Business processes, operational environment, variability of resources and user needs may change from time to time. An effective workflow management software system must be able to accommodate these changes. The ability to dynamically adapt to changes is a key success factor for workflow management systems. Holonic multi-agent systems (HMS) provide a flexible and reconfigurable architecture to accommodate changes based on dynamic organization and collaboration of autonomous agents. Although HMS provides a potential architecture to accommodate changes, the dynamic organization formed in HMS poses a challenge in the development of a new software development methodology to dynamically compose the services and adapt to changes as needed. This motivates us to study and propose a methodology to design self-adaptive software systems based on the HMS architecture. In this paper, we formulate a workflow adaptation problem (WAP) and propose an interaction mechanism based on contract net protocol (CNP) to find a solution to WAP to compose the services based on HMS. The interaction mechanism relies on a service publication and discovery scheme to find a set of task agents and a set of actor agents to compose the required services in HMS. We propose a viable self-adaptation scheme to reconfigure the agents and the composed services based on cooperation of agents in HMS to accommodate the changes in workflow and capabilities of actors. We propose architecture for our design methodology and present an application scenario to illustrate our idea. [ABSTRACT FROM AUTHOR]

In a smart grid, effective distributed control algorithms could be embedded in distributed controllers to properly allocate electrical power among connected buses autonomously. By selecting the incremental cost of each generation unit as the consensus variable, the incremental cost consensus (ICC) algorithm is able to solve the conventional centralized economic dispatch problem in a distributed manner. The mathematical formulation of the algorithm has been presented in this paper. The results of several case studies have also been presented to show that the difference between network topologies will influence the convergence rate of the ICC algorithm. [ABSTRACT FROM PUBLISHER]

Targeted drug delivery with the use of nanorobots, a yet mostly theoretical but very promising future concept, is anticipated to become a significant ally in cancer treatment. The way that nanorobot systems are currently envisaged by researchers is such that they exhibit autonomous and collaborative behaviour that can be uniquely captured by multi-agent systems. In this paper, we investigate this hypothesis by describing the process of formally modelling a simple agent-based system for a simulation of targeted drug delivery. We propose a system comprising different types of nanorobots, and evaluate the effects of various parameters on the final outcome. The data that were retrieved from the corresponding simulation runs, are in support of our hypothesis, demonstrating that nanorobotic drug delivery systems can be effectively simulated by utilising intelligent agent technology. [ABSTRACT FROM AUTHOR]

The paper presents a simulation tool for automated interactions between insurance companies and their clients during the travel insurance buying process. Insurance deal evaluation model using price and insured risks has been developed based on the study of the Latvian insurance market. The proposed model is used together with well-known agent auction protocols, thus providing a multi-agent negotiation protocol. It allows automating one-to-many negotiations between client and insurance companies simulating electronic insurance policy marketplace. The simulation tool has been developed using the MASITS methodology and tool, thus providing a case study for the methodology and tool for a new type of systems. [ABSTRACT FROM AUTHOR]

This paper presents an agent-based framework for configuration of agile supply chains. The presented framework, referred to as Digital Manufacturing Market (DMM), is implemented in Java Agent DEvelopment Framework (JADE). In the DMM, supply and demand entities are represented by intelligent software agents. The DMM agents communicate and interact dynamically for solving a given supply chain formation problem. Mediator agents use their internal search and discovery algorithms to connect buyers and sellers of manufacturing services based on their semantic similarities with respect to technological capabilities and needs. Manufacturing Service Description Language (MSDL) is the ontology of the DMM that based on the Web Ontology Language (OWL). The novel aspect of this research is to demonstrate the first application of agent technology for supply chain configuration based on a formal ontology that encodes the manufacturing capabilities of manufacturing suppliers. In this implementation, distributed ontology segmentation and sharing is investigated as well. Experimental results are provided based on a sample of suppliers and customers from the contract manufacturing domain. Although the implemented procedure does not necessarily result in an optimal supply chain, it guarantees the technological viability of the resulting supply chain. [ABSTRACT FROM AUTHOR]

This paper concerns the relationship between agents or multi-agent systems and distributed communities of practice. It presents a review of a number of agent and multi-agent applications with features that could contribute to supporting distributed communities of practice. The association is promising because of features like autonomy, pro-activity, flexibility or ability to integrate systems that characterize agents and multi-agent systems. Furthermore, such an association is a step towards building mixed communities of humans and artificial agents. To understand how agents and multi-agent systems could answer some of the needs of distributed communities of practice, we organize the analyzed applications into five different categories defined by considering the main activities of a community, namely: Individual Participation, Synchronous Interactions, Asynchronous Interactions, Publishing and Community Cultivation. Such a classification helps us identify the relevant features of the current technology and determine some that should be further developed, e.g. to support community coordination or gather information related to virtual communities. For each application we selected, we present its main approach and point out its potential interest. [ABSTRACT FROM AUTHOR]

Abstract: In recent years the widespread application of outsourcing to help improve finance and maintain competitiveness is growing rapidly, and has been accompanied by a growth of academic research in this field. Most of the outsourcing literature has focused primarily on the social and cultural impacts, the organisational and management perspectives of outsourcing and very few tools have been developed to assist managers in making outsourcing decisions. Incorrect decision-making in outsourcing can lead to devastating consequences for their business. To guide decision makers in managing the complexity of outsourcing a holistic approach to outsourcing has been developed by taking business, information and organisational perspectives (HABIO). The purpose of this paper is to demonstrate how HABIO can be applied to help managers evaluate the impact of their decision-making by providing them with a set of ‘what-if’ scenarios, which are enacted by intelligent agents. The case example, discussed in this paper, demonstrates that HABIO can be applied effectively using a multi-agent system to analyse the outsourcing decision for a call centre sector in the retail industry. A prototype of the multi-agent system is presented where intelligent agents are endowed with specific domain ontologies, appropriate to solve a specific problem, and with a reasoning mechanism to achieve specific goals and to collaborate together to produce a set of “what-if scenarios” to the outsourcing managers. [Copyright &y& Elsevier]

It is important to harmonize effectively the behaviors of the agents in the multi-agent system (MAS) to complete the solution process. The co-evolution computing techniques, inspired by natural selection and genetics, are usually used to solve these problems. Based on learning and evolution mechanisms of the biological systems, an adaptive co-evolution model was proposed in this paper. Inner-population, inter-population, and community learning operators were presented. The adaptive co-evolution algorithm (ACEA) was designed in detail. Some simulation experiments were done to evaluate the performance of the ACEA. The results show that the ACEA is more effective and feasible than the genetic algorithm to solve the optimization problems. [ABSTRACT FROM AUTHOR]

The advent of multi-agent systems has brought us opportunities for the development of complex software that will serve as the infrastructure for advanced distributed applications. During the past decade, there have been many agent architectures proposed for implementing agent-based systems, and also a few efforts to formally specify agent behaviors. However, research on narrowing the gap between agent formal models and agent implementation is rare. In this paper, we propose a model-based approach to designing and implementing intelligent agents for multi-agent systems (MAS). Instead of using formal methods for the purpose of specifying agent behavior, we bring formal methods into the design phase of the agent development life cycle. Specifically, we use the formalism called agent-oriented G-net model, which is based on the G-net formalism (a type of high-level Petri net), to serve as the high-level design for intelligent agents. Based on the high-level design, we further derived the agent architecture and the detailed design for agent implementation. To demonstrate the feasibility of our approach, we developed the toolkit called ADK (Agent Development Kit) that supports rapid development of intelligent agents for multi-agent systems and we discuss the role of inheritance in agent-oriented development. As a potential solution for automated software development, we summarize the procedure to generate a model-based design of application-specific agents. Finally, to illustrate an application built on ADK, we present an air-ticket trading example. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a first comparison of different agent-oriented programming languages, including the SARL agent-programming language. The study of those tools is based upon various criteria that are defining the essential aspects of multi-agent systems. For the sake of clarity, the comparative study is presented in a tabular form, including the languages and all the criteria. For each criterion, a definition is made to understand its meaning. At the end of the article, all the languages will be described in order to define their advantages and disadvantages. [ABSTRACT FROM AUTHOR]

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods. [ABSTRACT FROM AUTHOR]

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings. [ABSTRACT FROM AUTHOR]

This paper will attempt to highlight the complexity of decision-making mechanisms as well as the interest of integrating the cognitive dimension into the simulation models, with a view to evaluate the impact of technological reality (flow, individual production feedback) over the results of enterprise decisions. Therefore, we will review a simulation model using discrete events simulation techniques, which integrates the agent-based decisional system modeling. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we aim to implement a highly distributed information infrastructure—MADIP by using Intelligent Agent paradigm, which is able to notify the responsible care-provider of abnormality automatically, offer distance medical advice, and perform continuous health monitoring for those who need it. To confront the issues of interoperability, scalability, and openness in heterogeneous e-health environments, a FIPA2000 standard compliant agent development platform—JADE (Java Agent DEvelopment Framework) was adopted for the design and implementation of the proposed intelligent multi-agent based MADIP system. [ABSTRACT FROM AUTHOR]

Recent years have defined the need to reduce gas emissions to fight climate change, and society's move to green energies is important to make responsible use of non-renewable energies. Therefore, it is now important to use technologies to optimize the use of actual energy sources. In this aspect, the Internet of Things (IoT) technology has had a great impact on society. Hence, this research work aims to use IoT technology and multi-agent systems to optimize the use of central heating installation in buildings. It is intended to improve the user's comfort, reduce the consumption of energy and reduce the financial costs. Therefore, a multi-agent system is proposed to collect data from sensors located in a smart-home and obtain the best action to perform in a central heating system. The decisions will be taken by an intelligent agent based on fuzzy logic. This technology will allow for generating the control action with a fuzzy controller. The results obtained show that the proposal improves the actual system in terms of users' comfort and financial and energy savings. [ABSTRACT FROM AUTHOR]

Continuous asynchronous trading activity is a key to understanding real-world market behavior. However, it is not easy to implement an agent-based computational market model because of the ambiguity between time and space. In this study, we use a model of asynchrony in a continuous double auction market in the form of noise and order restrictions to link inside- and outside- uncertainties in the economic system. Our model shows intermittent behavior with a small parameter value, which leads to the misapplication of the price-update rule, and consequently drives burst behavior. The statistical property of time development shows a similar tendency to that in previous empirical studies. Thus, it demonstrates the relationship between the asynchronous property and the complexity of economic systems. [ABSTRACT FROM AUTHOR]

Trust models are mechanisms that allow agents to build trust without relying on a trusted central authority. Our goal was to develop a trust model that would operate with values that humans easily understand and manipulate: qualitative and ordinal values. The result is a trust model that computes trust from experiences created in interactions and from opinions obtained from third-party agents. The trust model, termed qualitative trust model (QTM), uses qualitative and ordinal values for assessing experiences, expressing opinions and estimating trust. We treat such values appropriately; we never convert them to numbers, but merely use their relative order. To aggregate a collection of such values, we propose an aggregation method that is based on comparing distributions and show some of its properties; the method can be used in other domains and can be seen as an alternative to median and similar methods. To cope with lying agents, QTM estimates trustworthiness in opinion providers with a modified version of the weighted majority algorithm, and additionally combines trustworthiness with social links between agents; such links are obtained implicitly by observing how agents provide opinions about each other. Finally, we compare QTM against a set of well-known trust models and demonstrate that it consistently performs well and on par with other quantitative models, and in many cases even outperforms them, particularly when the number of direct experiences is low. [ABSTRACT FROM AUTHOR]

In recent years, most companies have resorted to multi-site or supply-chain organization in order to improve their competitiveness and adapt to existing real conditions. In this article, a model for adaptive scheduling in multi-site companies is proposed. To do this, a multi-agent approach is adopted in which intelligent agents have reactive learning capabilities based on reinforcement learning. This reactive learning technique allows the agents to make accurate short-term decisions and to adapt these decisions to environmental fluctuations. The proposed model is implemented on a 3-tier architecture that ensures the security of the data exchanged between the various company sites. The proposed approach is compared to a genetic algorithm and a mixed integer linear program algorithm to prove its feasibility and especially, its reactivity. Experimentations on a real case study demonstrate the applicability and the effectiveness of the model in terms of both optimality and reactivity. [ABSTRACT FROM AUTHOR]

Personalization is becoming a key issue in designing effective e-learning systems and, in this context, a promising solution is represented by software agents. Usually, these systems provide the student with a student agent that interacts with a site agent associated with each e-learning site. However, in presence of a large number of students and of e-learning sites, the tasks of the agents are often onerous, even more if the student agents run on devices with limited resources. To face this problem, we propose a new multiagent learning system, called ISABEL. Our system provides each student, that are using a specific device, with a device agent able to autonomously monitor the student's behavior when accessing e-learning Web sites. Each site is associated, in its turn, with a teacher agent. When a student visits an e-learning site, the teacher agent collaborates with some tutor agents associated with the student, to provide him with useful recommendations. We present both theoretical and experimental results to show that this distributed approach introduces significant advantages in quality and efficiency of the recommendation activity with respect to the performances of other past recommenders. [ABSTRACT FROM AUTHOR]