Facilitating mas complete life cycle through the protégé-prometheus approach

The approach of this paper aims to support the complete multi-agent systems life cycle, integrated by two existing and widely accepted tools, Protégé Ontology Editor and Knowledge-Base Framework, and Prometheus Development Kit. A general sequence of steps facilitating application creation is proposed in this paper. We propose that it seems reasonable to integrate all traditional software development stages into one single methodology. This view provides a general approach for MAS creation, starting with problem definition and resulting in program coding, deployment and maintenance. The proposal is successfully being applied to situation assessment issues, which has concluded in an agent-based decision-support system for environmental impact evaluation

Evaluation of environmental impact upon human health with decimas framework

The article is dedicated to the problem of decision making in complex systems. Application of a novel interdisciplinary approach, which widely use intelligent agents is offered. The principal ideas of the novel approach are embodied into the DeciMaS framework, that offers a logical set of stages oriented to creation of decision support systems for complex problem management. The components of the DeciMaS framework and the way in which they are organized are introduced. Design and implementation of the system are discussed. The article demonstrates how the initial information is transformed into knowledge. Impact assessment upon human health evaluation is the case study, which is resolved by DeciMas framework. It includes creation of the meta-ontology. In addition, a multi-agent architecture for a decision support system is introduced. The sequence of the steps for the DeciMaS framework design with Prometheus Development Kit and its implementation with JACK Development Environment are presented as well. Finally, data and experiment results of data modeling, simulation, impact assessment, and decision generation are discussed

Design methodology for ontology-based multi-agent applications (MOMA)

Software agents and multi-agent systems (MAS) have grown into a very active area of research and commercial development activity. There are many current emerging real-world applications spanning multitude of diverse domains. In the context of agents, ontology has been widely recognised for their significant benefits to interoperability, reusability, and both development and operational aspects of agent systems and applications. Ontology-based multi-agent systems (OBMAS) exploit these advantages in providing intelligent and semantically aware applications. In addressing the lack of support for ontology in existing methodologies for multi-agent development, this thesis proposes a design methodology for the building of such intelligent multi-agent applications called MOMA. This alternative approach focuses on the development of ontology as the driving force of the development process. By allowing the domain and characteristics of utilisation and experimentation to be dictated through ontology, researchers and domain experts can specify the agent application without any knowledge of agent design and lower level programming. Through the use of a structured ontology model and the use of integrated tools, this approach contributes towards the building of semantically aware intelligent applications for use by researchers and domain experts. MOMA is evaluated through case studies in two different domains: financial services and e-Health

Predicting Stock Trends: A Multi Agent Approach

This thesis aims to develop a scalable distributed system that provides reliable information about the trend that a certain security will follow in the Stock Market using Machine Learning techniques. The core of the research presented in this thesis is the creation of a Multi Agent based system capable of predicting whether the price of a stock will rise, drop or stay. In order to do so, the problem is divided into three parts: Information Retrieval, Data Analysis and Data Visualization. The first part is focused on retrieving the necessary information from Internet and on transforming this raw data into computer-understandable structures that will allow further analysis. In addition to this, new financial indicators are calculated to provide more meaningful data to the system. The second part is focused on analyzing this preprocessed data using several Machine Learning methods. The methods that have been selected to execute the analysis are: Artificial Neural Networks, Decision Trees, Support Vector Machines and Reinforcement Learning. The idea behind using different methods besides testing the performance of each in this scenario, is the creation of a team of “data analyzers” like the ones found at investment firms. Following the “Keep it Simple” principle, third party libraries have been used when possible to diminish implementation costs. Finally, the third part deals with the problem of how to present the data and results to the users in a clear but informative way. Just this part on this own could perfectly be a Final Project in a Media Degree, so here we will present a gentle introduction to the Data Visualization world. Since this thesis is also a Computer Science Engineering Final Project, emphasis will be made in describing the system architecture and the technologies used to create it. In part because of this, this Thesis aims to create a Proof of Concept for a possible future product instead of realizing just a evaluation of Machine Learning methods applied to predicting stock trend

Process control and configuration of a reconfigurable production system using a multi-agent software system

Thesis (M. Tech. (Information Technology)) -- Central University of technology, Free State, 2011Traditional designs for component-handling platforms are rigidly linked to the product being produced. Control and monitoring methods for these platforms consist of various proprietary hardware controllers containing the control logic for the production process. Should the configuration of the component handling platform change, the controllers need to be taken offline and reprogrammed to take the changes into account. The current thinking in component-handling system design is the notion of re-configurability. Reconfigurability means that with minimum or no downtime the system can be adapted to produce another product type or overcome a device failure. The re-configurable component handling platform is built-up from groups of independent devices. These groups or cells are each responsible for some aspect of the overall production process. By moving or swopping different versions of these cells within the component-handling platform, re-configurability is achieved. Such a dynamic system requires a flexible communications platform and high-level software control architecture to accommodate the reconfigurable nature of the system. This work represents the design and testing of the core of a re-configurable production control software platform. Multiple software components work together to control and monitor a re-configurable component handling platform. The design and implementation of a production database, production ontology, communications architecture and the core multi-agent control application linking all these components together is presented

Supporting multi-agent systems life cycle by integrating protégé and prometheus

In this paper, an approach aiming to support the complete multi-agent systems (MAS) life cycle is proposed. Two existing and widely accepted tools, Protege Ontology Editor and Knowledge-Base Framework and Prometheus Development Kit, are integrated, offering a general sequence of steps facilitating application creation. It seems reasonable to integrate all traditional software development stages into one single methodology, which can provide a general approach for MAS creation, starting with problem definition and resulting in program coding, deployment and maintenance. The approach is successfully being applied to situation assessment issues, which has concluded in an agent-based decision-support system for environmental impact evaluation. An example is offered to evaluate the impact of environmental parameters upon human health in Spanish region Castilla-La Mancha, in general, and in the city of Albacete, in particular

Applying agent technology to constructing flexible monitoring systems in process automation

The dissertation studies the application of agent technology to process automation monitoring and other domain specific functions. Motivation for the research work derives from the development of industrial production and process automation, and thereby the work load of operating personnel in charge of these large-scale processes has become more complex and difficult to handle. At the same time, the information technology infrastructure in process automation domain has developed ready to accept and utilise novel software engineering solutions. Agent technology is a new programming paradigm which has attractive properties like autonomy, flexibility and a possibility to distribute functions. In addition, agent technology offers a systematic methodology for designing goal based operations. This enables parts of the monitoring tasks to be delegated to the system. In this research, new agent system architecture is introduced. The architecture specifies a structure that enables the use of agents in the process monitoring domain. In addition, an introductory internal layered design of an agent aiming to combine Semantic Web and agent technologies is presented. The developed agent architecture is used in conjunction with the systematic agent design methodology to construct and implement four test cases. Each case has industrially motivated interest and illustrates various aspects of monitoring functionalities. These tests provide evidence that by utilising agent technology it is possible to develop new monitoring features for process operators, otherwise infeasible as such within current process automation systems. As a result of the research work, it can be stated that agent technology is a suited methodology to realise monitoring functionalities in process automation. It is also shown, that by applying solutions gained from the agent technology research, it is possible to define an architecture that enables to utilise the properties offered by agents in process automation environment. The proposed agent architecture supports features that are of generic interest in monitoring tasks. The developed architecture and research findings provide ground to import novel software engineering solutions to process automation monitoring

A multi-agent approach to adaptive learning using a structured ontology classification system

Diagnostic assessment is an important part of human learning. Tutors in face-to-face classroom environment evaluate students’ prior knowledge before the start of a relatively new learning. In that perspective, this thesis investigates the development of an-agent based Pre-assessment System in the identification of knowledge gaps in students’ learning between a student’s desired concept and some prerequisites concepts. The aim is to test a student's prior skill before the start of the student’s higher and desired concept of learning. This thesis thus presents the use of Prometheus agent based software engineering methodology for the Pre-assessment System requirement specification and design. Knowledge representation using a description logic TBox and ABox for defining a domain of learning. As well as the formal modelling of classification rules using rule-based approach as a reasoning process for accurate categorisation of students’ skills and appropriate recommendation of learning materials. On implementation, an agent oriented programming language whose facts and rule structure are prolog-like was employed in the development of agents’ actions and behaviour. Evaluation results showed that students have skill gaps in their learning while they desire to study a higher-level concept at a given time