Multi-agent Monitoring Information Systems

The article proposes a multi-agent information system (MAIS) approach to model the order fulfilment process (OFP) in supply chain networks (SCNs). An order fulfilment process starts with receiving orders from customers and ends with delivery of the finished goods. As manufacturing practice is shifting toward the outsourcing paradigm, the OFP is more likely to be executed throughout SCNs. It becomes imperative to integrate the OFP into SCNs to improve the OFP. The proposed multi-agent information system (MAIS) approach is used for modeling the OFP in SCNs, and evaluating OFP performance by applying the proposed strategies. The objective of reengineering the OFP is to achieve agility of the process in terms of efficiency, flexibility, robustness and adaptability. A multi-agent simulation platform, called Swarm, is enhanced and applied for modeling the MAIS, and experiments are conducted to simulate the OFP in SCNs in a multi-agent environment. Based an the Swarm simulation platform, we model the OFP in SCNs, simulate the OFP, and then evaluate the potential OFP improvement strategies to identify useful strategies for improving the OFP. The results shed light on identifying the main effects of various strategies on OFP performance. The insights of utilizing various strategies in different SCNs help redesign the OFP in SCNs.

A fully abstract model for the exchange of information in multi-agent systems

This article describes using distributed coding of logical information in multiagent systems. First we identify failures of the agent platform in case of an unreliable underlying network. Next a simple, but inefficient way of distributing the agent state is given. Finally, we expose the way of solving failure problems by use of state snapshots and distributed coding. We develop a model of the network, based on the notion of erasure graph channel. We treat the properties of the erasure code encoding data efficiently with respect to a prescribed level of distortion. This work introduces information-theoretic ideas to multiagent middleware. It should enable applications of multiagent systems in environments where it is hard to establish a reliable communication infrastructure.

Speculative computation is an effective means for solving problems with incomplete information in multi-agent systems. It allows such a system to compute tentative solutions by using default knowledge about agents even if communications between agents are delayed or fail. Previously we have proposed a logical framework for speculative constraint processing for master-slave multi-agent systems. In this paper, we extend the framework to support more general multi-agent systems that are hierarchically structured. We provide an operational model for the framework and present a prototype implementation of the model.

In order to simulate the real world effectively, this paper proposes a multi-agent model that integrates a price negotiation support system based on the use of intelligent agents capable of processing information uncertainty. The certainty factor is integrated in expert systems to support the decision of agents. In the model, each agent is integrated with an expert system to deal with the uncertainty information. A real example of supply chains is chosen to show the validity of the proposed multi-agent model. Virtual companies can use the uncertainty information to support decisions. Multiple experiments are conducted to work on the coordination of the supply chain. The selling and purchasing prices in the supply chain are proposed in the experimental results. These prices are similar with the data in a real supply chain and are the optimal price strategy for the supply chain. The model was programmed using Jess and Swarm, which was run on Windows XP. The multi-agent model in the paper is beneficial to the reasoning of uncertainty information in multi-agent systems.

Institutional nature of using digital techniques in modern agrarian production was studied in this article. As a result, the approach illustrating the synergy of economic, ecological, and social effectiveness as a progressive direction of global economic system's development was worked out. The general model of a new informational paradigm of agricultural activities’ organization based on the agility of knowledge and analytical data transferring into value information was determined. The model of institutional interaction of information agents in agricultural production was constructed. The model's aim is based on the principle “lose less and feed more” and presupposes multiagent interaction of primary information sources (agricultural enterprises) with data bases of public unions (their activities are directed at solving the problems of agriculture and the environment) and government bodies, which make strategic managerial decisions based on macroeconomic indicators. At the empirical stage, the design of the process of solving individual accounting problems or their complexes can be used in multiagent information systems (artificial intelligence and the Internet of Things) to obtain relevant information about specific objects of agricultural production. In particular, the accounting tasks of GMO objects were decided on the basis of open data, design documents and standard design solutions, which can be the basis for further development of functional application packages.

With the recent advances in Internet and mobile technologies and infrastructures, there are increasing demands for ubiquitous access to tourist information systems for service coordination and integration. However, disparate tourist information and service resources such as airlines, hotels, tour operators, etc., make it difficult for tourist to use them effectively when planning their trips and/or during their trips. Motivated by the emerging technologies of multi-agent information system (MAIS) and its ability to aid Internet and mobile users, together with semantic Web that can effectively organize information and service resources. In this paper, we propose a virtual travel agent system (VTAS), which is built upon these technologies. In this paper, we formulate a scalable, flexible, and intelligent MAIS architecture for VTAS with agent clusters based on a case study of a large service-oriented travel agency. Agent clusters may comprise several types of agents to achieve the goals of the major processes of a tourist's trip. We show how agents can make use of ontology from the semantic web help tourists better plan, understand, and specify their requirements. We further illustrate how this can be successfully implemented with Web service technologies to integrate disparate Internet tourist resources.

According to the exponential growth of services available on large distributed networks, transport customers require relevant, interactive and instantaneous information during their travels. Thus, we designed and implemented a multi-agent information system (MIS) using the mobile agents. However, some network errors can occur during the moving of mobile. So, we define a negotiation ontology protocol where we incorporated architecture for negotiation process with an ontology-based knowledge management system (NOKMS) to facilitate, to automate the communications and to solve the problem of language interoperability between negotiation agents. Finally, we detailed the reassignment process by using dynamic reassigned tasks (DRT) algorithm. Our goal is to give users all needed information even if some information providers are no longer available. Thus we improve the quality of services (QoS) of the response time with the reasonable cost.

Agent Technologies for Polythematic Organizations Information-Analytical Support

A New Approach In Learning For Intelligent Multi Agent Systems

Artificial intelligence of large ensembles of intelligent agents in terms of computing power surpasses human intelligence. He is capable of artificial thinking and understanding. Giant ensembles of intellectual agents with artificial consciousness and intelligence are able, for the results set by the person necessary for him, to find solutions for their obtaining on the basis of professional competence and experience accumulation. The professional competence of artificial intelligence is the ability to use technologies, including computer vision, natural language processing, speech recognition and synthesis, intelligent decision support, as well as the use of synergistic methods, functional self-organizing methods and utility and preference criteria. For artificial intelligence, the functional organization of the system is important. The functionalism of artificial intelligence does not depend on its carrier, allows many methods of its functional implementation, as well as to form the completeness of its functions. Giant ensembles of intellectual agents with a full set of functions gradually and flexibly form events into solutions or rational behavior to obtain a given necessary result. Intelligent artificial intelligence has psychological, analytical, research, language, professional and behavioral hybrid competencies. Each competence is exercised by an intelligent agent with a competent functional professional manner. Intelligent agents form an ensemble with intelligent ethical artificial intelligence. Functional harmonious self-organization of the interaction of intellectual agents in different environments is carried out on the basis of data of a specific environment obtained by analytical competent intellectual agents. For each set of functions and smart hybrid competencies of an intelligent ensemble, there is a critical importance for the number of its intellectual agents capable of synergistic self-organization of interaction.

The intelligent agent paradigm has generated such a remarkable interest in many application domains over the last two decades. It is growing to be a continuously evolving and expanding area. Agents, Software Agents or Intelligent Agents are intelligent in the sense that they are adaptive, independent, and possess reasoning capability. They can plan and execute tasks in cooperation with other agents in order to satisfy their goals. A MultiAgent System (MAS) is defined as a loosely coupled network of problem solvers that work together to solve problems that are beyond the individual capabilities or knowledge of each problem solver (Agent). The increasing interest in MAS research is due to significant advantages inherent in such systems, including their ability to solve problems that may be too large for a centralized single agent, provide enhanced speed and reliability, and tolerate uncertain data and knowledge. Some of the key research issues related to problem-solving activities of agents in a multi-agent system MAS are in the areas of coordination, negotiation, and communication. With advances in Web technologies, collaborative applications are now server based and the user interface is typically a Web browser. Thus, a collaborative application can be a Webbased solution that runs on a local server that allows people communicate and work together, share information and documents, and talk in real-time over the Internet. Recently, much research has been conducted in distributed artificial intelligence and collaborative applications. Several interesting methodologies and systems have been developed in areas such as distributed multi-agent systems for decision support, web search and information retrieval, information systems modeling, and supply chain management. This chapter considers applying different data mining techniques for the decision making process in a Multi-Agent System for Collaborative E-learning (MASCE). The dynamism in elearning can be made more powerful with the help of intelligent agents. Intelligent agents – the so called e-assistants or helper programs can reside inside a computer and make the learning in e-learning occur dynamically to suit the need of the user. They can track the user’s likes and dislikes in different areas, the level of knowledge and the learning style and accordingly recommend the best matching helpers for collaboration. A previous research outlined the development and the implementation processes of a MultiAgent System for Collaborative E-learning (MASCE) which is designed to be used to assist

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

In this paper, we present a multi-agent information system model that can deal with complex multi-activity information-driven tasks. After defining a single information agent architecture in an abstract way, we describe the multi-agent model (called the parallel model), as well as a simple collaboration protocol for the multiple activity information agents. Experimental results are presented with the multi-agent system that demonstrate that a significantly larger number of relevant documents can be retrieved through collaboration than what is possible with decoupled agents. Hence, such multi-agent parallel models appear to hold considerable promise in providing the user with high quality information services in a variety of business and scientific information-driven tasks.

Intellectual agent ensembles allow you to create digital environment by professional images with language, behavioral and active communications, when images and communications are implemented by agents with smart artificial intelligence. Through language, behavioral and active communications, intellectual agents implement collective activities. The ethical standard through intelligent agents allows you to regulate the safe use of ensembles made of robots and digital doubles with creative communication artificial intelligence in the social sphere, industry and other professional fields. The use of intelligent agents with smart artificial intelligence requires responsibility from the developer and owner for harming others. If harm to others occurred due to the mistakes of the developer, then he bears responsibility and costs. If the damage to others occurred due to the fault of the owner due to non-compliance with the terms of use, then he bears responsibility and costs. Ethical standard and legal regulation help intellectual agents with intelligent artificial intelligence become professional members of society. Ensembles of intelligent agents with smart artificial intelligence will be able to safely work with society as professional images with skills, knowledge and competencies, implemented in the form of retrained digital twins and cognitive robots that interact through language, behavioral and active ethical communications. Cognitive robots and digital doubles through self-developing ensembles of intelligent agents with synergistic interaction and intelligent artificial intelligence can master various high-tech professions and competencies. Their use in the industry increases labor productivity and economic efficiency of production. Their application in the social sphere improves the quality of life of a person and society. Their widespread application requires compliance with an ethical standard so that their use does not cause harm. The introduction and use of an ethical standard for the use of cognitive robots and digital doubles with smart artificial intelligence increases the safety of their use. Ethical relationships between individuals and intellectual agents will also be governed by an ethical standard.