Multiagent Systems Engineering: the Analysis Phase

Traditional software engineering approaches and metaphors fall short when applied to areas of growing relevance such as electronic commerce, enterprise resource planning, and mobile computing: such areas, in fact, generally call for open architectures that may evolve dynamically over time so as to accommodate new components and meet new requirements. This is probably one of the main reasons that the agent metaphor and the agent-oriented paradigm are gaining momentum in these areas. This thesis deals with the engineering of complex software systems in terms of the agent paradigm. This paradigm is based on the notions of agent and systems of interacting agents as fundamental abstractions for designing, developing and managing at runtime typically distributed software systems. However, today the engineer often works with technologies that do not support the abstractions used in the design of the systems. For this reason the research on methodologies becomes the basic point in the scientific activity. Currently most agent-oriented methodologies are supported by small teams of academic researchers, and as a result, most of them are in an early stage and still in the first context of mostly \academic approaches for agent-oriented systems development. Moreover, such methodologies are not well documented and very often defined and presented only by focusing on specific aspects of the methodology. The role played by meta- models becomes fundamental for comparing and evaluating the methodologies. In fact a meta-model specifies the concepts, rules and relationships used to define methodologies. Although it is possible to describe a methodology without an explicit meta-model, formalising the underpinning ideas of the methodology in question is valuable when checking its consistency or planning extensions or modifications. A good meta-model must address all the different aspects of a methodology, i.e. the process to be followed, the work products to be generated and those responsible for making all this happen. In turn, specifying the work products that must be developed implies dening the basic modelling building blocks from which they are built. As a building block, the agent abstraction alone is not enough to fully model all the aspects related to multi-agent systems in a natural way. In particular, different perspectives exist on the role that environment plays within agent systems: however, it is clear at least that all non-agent elements of a multi-agent system are typically considered to be part of the multi-agent system environment. The key role of environment as a first-class abstraction in the engineering of multi-agent system is today generally acknowledged in the multi-agent system community, so environment should be explicitly accounted for in the engineering of multi-agent system, working as a new design dimension for agent-oriented methodologies. At least two main ingredients shape the environment: environment abstractions - entities of the environment encapsulating some functions -, and topology abstractions - entities of environment that represent the (either logical or physical) spatial structure. In addition, the engineering of non-trivial multi-agent systems requires principles and mechanisms for supporting the management of the system representation complexity. These principles lead to the adoption of a multi-layered description, which could be used by designers to provide different levels of abstraction over multi-agent systems. The research in these fields has lead to the formulation of a new version of the SODA methodology where environment abstractions and layering principles are exploited for en- gineering multi-agent systems.

In Multi-Agent System (MAS), developers concentrate on creating design models and evolving them, from higher level models to lower level models, in several steps. Considerable part of MAS implementations is automatically produced from the design models. If a design model contains faults, they are passed to the generated implementations. Practical model validation techniques are required to discover and delete faults in abstract design models. In this paper, we introduce a formal approach for MAS design testing. It specifies a testing process that complements Multi-agent Systems Engineering (MaSE) methodology and strengthens the mutual relationship between UML and MAS. Besides, it defines a structured and comprehensive testing process for engineering software agents at the design level by providing a systematic way of converting the MAS design models to UML design diagram. Then a Petri Net (PN) diagram is generated from the UML models to simulate the behavior of the MAS system. Finally, because Petri Nets (PNs) are formal models, their analysis techniques can be applied to automatic MAS behavioral testing.

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.

Port power, as one of the important scenarios to promote energy substitution, has the characteristics of great substitution potential and strong interaction ability. In order to explore the technology of port power participating in power grid regulation, a strategy of port power participating in power grid regulation based on multi-agent system (MAS) technology is proposed in this paper. Firstly, the analysis and modeling of the controllable resources are carried out from the power side and the power grid side of the port, that is, the interactive resources on both sides of the supply and demand are analyzed and modeled, which lays the foundation for the key technology research. Secondly, in order to reflect the economic and environmental benefits of energy substitution, and to take into account the benefits of port ships, ports, power grids and the government, the optimization based on MAS is proposed. Finally, Q-learning algorithm is used to solve the model, and an example is used to verify the effectiveness of the model and the control strategy.

In this paper we have done a survey over control and management challenges in modern power systems today in terms of emerging distributed power generation (DG), microgrid management, electricity marketing and power quality control toward smart grid. Intelligent agent and multiagent systems (MAS) technology are introduced, then properties and more potential benefits of MAS for power systems such as scalability, expertise and grid-computing are discussed to address MAS is a appropriate solution for power engineering challenges.

An adaptive user system can be helpful to improve the quality of human-computer interaction. Because of the one-size-fits-all user interface, most people in Web-based learning have to face lack of support on adaptive learning. Agents are autonomous and computational entities that can be viewed as perceiving and acting upon their environment. We make use of multi-agent system (MAS) technology in artificial intelligence to implement adaptivity to improve the users' study in quality dynamically. We start our work with a 4-key-component interaction model for adaptivity to construct special user models. On the basis of them an adaptive learning system is designed and implemented using MAS technology. Following that the architecture and working process of this system are presented. And then we introduce how to get learning resource adaptive to users; examples are also given to evaluate the adaptiveness.

: The goal of our research is to provide a framework for constructing complex, distributed systems that can autonomously adapt to their environment. There were three key elements to this framework. The first element was a metal-model for developing distributed, adaptive systems based on organization theory - the Organization Model for Adaptive Computational Systems (OMACS). OMACS defines the required components for an adaptive organization - agents, goals, roles, and capabilities - as well as an approach for computing the best organization of those components. The second framework element created was a the Goal Model for Adaptive Systems, which handles the runtime goals based on precedence between goals as well as runtime instantiation of new goals based on events that occur in the environment. The final part of the framework developed was a software engineering methodology for designing distributed, adaptive systems using a dynamic goal model. The Organization-based Multiagent Systems Engineering (O-MaSE) methodology is based on the Multiagent Systems Engineering methodology and incorporates all OMACS components.

Multi-agent systems have received much attention in recent years because of their many advantages in complex and distributed environments. There are a number of methodologies have been proposed for multi- agent engineering process such as Multi-agent System Engineering (MaSE). I have used the MaSE engineering process for the development of my ontology based multi- agent system for the Academic Institute. In the era of Semantic Web, the ontology has established as a powerful tool to enable knowledge sharing and it is an important means in Semantic Web to achieve the semantic interoperability among heterogeneous distributed systems. Both ontology and agent technologies are central to the semantic web, and their combined use will enable the sharing of heterogeneous, autonomous knowledge sources in a capable, adaptable and extensible manner. Ontology is used throughout the multi-agent system to assist the interactions among different agents as well as to improve the quality of the service provided by each agent. This paper focuses on the utilization of combining both Ontology and Multi-Agent System (MAS) structure towards system integration for University teaching environment. In this paper, I include system prototype for ontology based multi-agent system. I have used Web Ontology Language (OWL) for the development of domain ontology for the Academic Institute and KQML as an agent communication language. Finally I have developed ontology-based multi- agent system for the university teaching environment by taking the benefits of both renowned technologies.

One of the most critical issues in the engineering of multi-agent systems (MAS) is the inadequacy of the available tools for MAS development and deployment. As we assume interaction as a first-class issue in MAS, tools are particularly required to monitor and debug inter-agents aspects, such as interaction protocols, coordination policies, social norms, and environment constraints. Since we claim that the definition of such tools is a basic research issue, in this paper we aim to identify the main requirements for development and deployment tools within an effective agent infrastructure. Focusing on agent interaction aspects, we take tuple-based coordination infrastructures--in particular the TuCSoN technology and tools--as our reference, and discuss the role of tools in a simple case study: the development and deployment of a well-known agent interaction protocol, the Contract Net.

Environmental impact assessment (EIA) is one of the principal indicators for human health evaluation as well as for further issues in occupational medicine, public health and planning. Certain difficulties to evaluate EIA appear due to, on the one hand, the nature of contaminants and health characteristics, and, on the other, the difficulties found in data processing, such as heterogeneity of the information sources, lack of data quality and need to use numerous data mining methods. The application of multi-agent systems (MAS) to EIA helps operating with distributed decentralized information and generating general decisions and solutions. In order to facilitate the workflow of MAS planning and implementation in decision support systems (DSS) applied to EIA, a generalized multi-agent system technology (MAST) is proposed. This workflow is general enough to enable the related MAS to be used in any application area requiring decision making. We demonstrate that by only changing the domain ontology the MAS is easily oriented towards another problem area. The functional organization and the roles executed in the MAS conform to the logical sequence of the data transformation flow, which include information retrieval and fusion, data pre-processing, data mining and modeling, and simulation and decision making.

The paper deals with the multiagent systems for the power system topology verification. Two of such multiagent systems are considered. The idea of topology verification utilized in each of them allows considering the topology verification of a whole power system as the set of many local verification processes. Each of the described multiagent systems performs the topology verification over a different protocol. Types of agent defined for these systems are different. For each of the considered multiagent systems, analysis and design are outlined using the Multiagent Systems Engineering. For their design models, the performance evaluation has been carried out. Then, a comparison of the investigated multiagent systems is made, paying special attention to their performance effectiveness.Keywordsmodelingmultiagent systeminteraction protocolsimulationpower system topology verificationperformance evaluation

A Multi-Agent System (MAS) is understood as a system consisting of interacting autonomous agents. Research on MAS is mainly concerned with functional properties such as coordination, rationality, and knowledge modeling. A very less attention is paid for the research activity on the nonfunctional properties performance, scalability and reliability of the MAS. However, as the MAS technologies have gradually matured to be exploited in building practical distributed applications, the non-functional properties have become increasingly important, and it is now vital to pay attention to the issues of nonfunctional characteristics of agents. We propose to access the performance of agents in MAS and the system is implemented using JADE. The experimental study provides the opportunity for cause and effect relationships. We considered a tiered architecture for the MAS. A tiered design can lessen the overall impact of changes to the application and allows modifying a component without disturbing the next Tier. So with the help of a good deployment plan, we can distribute the layers over multiple physical tiers in distributed computing to implement a better architecture of the system. The case study considered for illustration is Supply-Chain Management and the results obtained are validated with the tool SMTQA.

Today’s software industry is tasked with building evermore complex software applications, and multiagent system technology is a promising approach capable of meeting these new demands. Unfortunately, multiagent systems have not been widely adopted in industry for reasons that include lack of industrial strength methods and tools to support multiagent development. Method engineering, an approach to constructing processes from a set of existing method fragments, has been suggested as a solution to this problem. This chapter presents the Organization-based Multiagent Software Engineering (O-MaSE) methodology framework, which integrates a set of concrete technologies aimed at facilitating industrial acceptance. Specifically, O-MaSE is a customizable agent-oriented methodology based on consistent, well-defined concepts supported by plug-ins to an industrial strength development environment, agentTool III. O-MaSE is defined, and demonstrations of customizing O-MaSE for the CMS problem as well applying the customized process to the CMS design are presented.

In this paper we study tools for conviviality to develop user-friendly multiagent systems. First, we show how to use the social-cognitive concept of conviviality in multiagent system technology by relating it to agent power and social dependence networks. Second, we define conviviality masks as transformations of social dependencies by hiding power relations and social structures to facilitate social interactions. Third, we introduce dynamic dependence networks to model the creation of conviviality using conviviality masks. We illustrate the use of conviviality masks with a multiagent teleconferencing application for virtual worlds.

In this paper we study tools for conviviality to develop user-friendly multiagent systems. First, we show how to use the social-cognitive concept of conviviality in multiagent system technology by relating it to agent power and social dependence networks. Second, we define conviviality masks as transformations of social dependencies by hiding power relations and social structures to facilitate social interactions. Third, we introduce dynamic dependence networks to model the creation of conviviality using conviviality masks. We illustrate the use of conviviality masks with a multiagent teleconferencing application for virtual worlds.