A Petri Net Based Multi-Agent System Behavioral Testing

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.

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.

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

The development of theoretical-based methods for the assessment of multi-agent systems properties is of critical importance. This work is a preliminary investigation on methodologies for modeling, analysis and design of multi-agent systems. Multi-agent systems are regarded as discrete-event dynamic systems and Petri nets are used as a modeling tool to assess the structural properties of the multi-agent system. Our methodology consists of defining a simple multi-agent system based on the abstract architecture for intelligent agents. The abstract architecture is modeled as a discrete-event system using Petri nets and structural analysis of the net provides an assessment of the communication and coordination properties of the multi-agent system. Deadlock avoidance in the multi-agent system is considered as an initial key property, and it is evaluated using liveness and boundedness properties of the Petri net model.

This report describes the Analysis phase of the Multiagent Systems Engineering (MaSE) methodology. MaSE is a general purpose, methodology for developing heterogeneous multiagent systems. The goal of MaSE is to guide a system developer from an initial system specification to a multiagent system implementation. This is done by directing the designer through this set of inter-related system models. Although the majority of the MaSE models are graphical, the underlying semantics clearly and unambiguously defines specific relationships between the graphical models.

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.

Design models are valuable deliverables of the software development process but have the potential to be large and unwieldy. Sound software engineering practice guides us towards the break-up of design models into manageable components. These smaller design models, however, need to be considered together in order to get the “big picture” of the full design. In order to integrate a design model, it must be possible to merge that model with the remainder of the design. There is considerable potential for conflict between the design models being composed. We introduce a tool that supports the resolution of conflicts when composing UML class models, and the definition of composition rules to make the process as automated as possible.

We propose a new modularity vision to integrate design modeling with programming. A system consists of not only program code but also design models. However, design models such as UML diagrams have been treated as not modules but complementary documents. This paper provides a novel idea that treats a design model as a first-class software module termed design module. A system cannot be complied without design modules. A developer has to create and modify not only program modules but also design modules. To realize design modules, we introduce Archface, an architectural interface mechanism. Archface exposes architectural points shared between design and code. Archface plays a role as a design interface for a design module and as a program interface for a program module. Arch-face type system automatically checks the consistency between design and code. Our approach can integrate research on design modeling with research on programming in terms of modularity. Additional costs are not needed to introduce design modules, because Archface descriptions can be generated from a model editor and Archface is just an extension of the notion of program interfaces. That is, we can substitute Archface for traditional program interfaces.

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.

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.

: 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.

With the growing in size and complexity of modern computer systems, the need for improving the quality at all stages of software development has become a critical issue. The current software production has been largely dependent on manual code development. Despite the slow development process, the errors introduced by the programmers contribute to a substantial portion of defects in the final software product. Model-driven engineering (MDE), despite having many advantages, is often overlooked by programmers due to lack of proper understanding and training in the matter. This paper investigates the advantages and disadvantages of MDE and looks at research results showing the adoption rates of design models. It analyzes different tools used for automated code generation and displays the reasons that led to technical decisions such as the programming language or design model used. In light of the findings, an educational tool, namely Lorini, was developed to provide automated code generation from the design models. The implemented tool consists of a plug-in for the Astah framework aimed at teaching Java programming to students through UML diagrams. It features instantaneous code generation from three types of UML diagrams, code-diagram matching, a feedback panel for error displays and on-the-fly compilation and execution of the resulting program. We also explore the possibility of generating assertion constraints from the design model and use them to verify the implementation. Evaluation of the tool indicated it to be successful with unique educational features and intuitive to use.

A key difficulty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system configurations is essential. To this end, we present a taxonomy that classifies multi-agent systems according to communication, computational and other capabilities. We survey existing efforts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.

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.