Development of an autonomous distributed multiagent monitoring system for the automatic classification of end users

The purpose of this study is to investigate the feasibility of constructing a software Multi-Agent based monitoring and classification system and utilizing it to provide an automated and accurate classification for end users developing applications in the spreadsheet domain. Resulting in, is the creation of the Multi-Agent Classification System (MACS). The Microsoft‘s .NET Windows Service based agents were utilized to develop the Monitoring Agents of MACS. These agents function autonomously to provide continuous and periodic monitoring of spreadsheet workbooks by content. .NET Windows Communication Foundation (WCF) Services technology was used together with the Service Oriented Architecture (SOA) approach for the distribution of the agents over the World Wide Web in order to satisfy the monitoring and classification of the multiple developer aspect. The Prometheus agent oriented design methodology and its accompanying Prometheus Design Tool (PDT) was employed for specifying and designing the agents of MACS, and Visual Studio.NET 2008 for creating the agency using visual C# programming language. MACS was evaluated against classification criteria from the literature with the support of using real-time data collected from a target group of excel spreadsheet developers over a network. The Monitoring Agents were configured to execute automatically, without any user intervention as windows service processes in the .NET web server application of the system. These distributed agents listen to and read the contents of excel spreadsheets development activities in terms of file and author properties, function and formulas used, and Visual Basic for Application (VBA) macro code constructs. Data gathered by the Monitoring Agents from various resources over a period of time was collected and filtered by a Database Updater Agent residing in the .NET client application of the system. This agent then transfers and stores the data in Oracle server database via Oracle stored procedures for further processing that leads to the classification of the end user developers. Oracle data mining classification algorithms: Naive Bayes, Adaptive Naive Bayes, Decision Trees, and Support Vector Machine were utilized to analyse the results from the data gathering process in order to automate the classification of excel spreadsheet developers. The accuracy of the predictions achieved by the models was compared. The results of the comparison showed that Naive Bayes classifier achieved the best results with accuracy of 0.978. Therefore, the MACS can be utilized to provide a Multi-Agent based automated classification solution to spreadsheet developers with a high degree of accuracy

Web service control of component-based agile manufacturing systems

Current global business competition has resulted in significant challenges for manufacturing and production sectors focused on shorter product lifecyc1es, more diverse and customized products as well as cost pressures from competitors and customers. To remain competitive, manufacturers, particularly in automotive industry, require the next generation of manufacturing paradigms supporting flexible and reconfigurable production systems that allow quick system changeovers for various types of products. In addition, closer integration of shop floor and business systems is required as indicated by the research efforts in investigating "Agile and Collaborative Manufacturing Systems" in supporting the production unit throughout the manufacturing lifecycles. The integration of a business enterprise with its shop-floor and lifecycle supply partners is currently only achieved through complex proprietary solutions due to differences in technology, particularly between automation and business systems. The situation is further complicated by the diverse types of automation control devices employed. Recently, the emerging technology of Service Oriented Architecture's (SOA's) and Web Services (WS) has been demonstrated and proved successful in linking business applications. The adoption of this Web Services approach at the automation level, that would enable a seamless integration of business enterprise and a shop-floor system, is an active research topic within the automotive domain. If successful, reconfigurable automation systems formed by a network of collaborative autonomous and open control platform in distributed, loosely coupled manufacturing environment can be realized through a unifying platform of WS interfaces for devices communication. The adoption of SOA- Web Services on embedded automation devices can be achieved employing Device Profile for Web Services (DPWS) protocols which encapsulate device control functionality as provided services (e.g. device I/O operation, device state notification, device discovery) and business application interfaces into physical control components of machining automation. This novel approach supports the possibility of integrating pervasive enterprise applications through unifying Web Services interfaces and neutral Simple Object Access Protocol (SOAP) message communication between control systems and business applications over standard Ethernet-Local Area Networks (LAN's). In addition, the re-configurability of the automation system is enhanced via the utilisation of Web Services throughout an automated control, build, installation, test, maintenance and reuse system lifecycle via device self-discovery provided by the DPWS protocol...cont'd

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Middle-out domain-specific aspect languages and their application in agent-based modelling runtime inspection

Domain-Specific Aspect Languages (DSALs) are a valuable tool for separating cross-cutting concerns, particularly within fields with endemic cross-cutting practices. Agent-Based Modelling (ABM) runtime inspection, which cuts across the core concern of model development, serves as a prime example. Despite their usefulness, DSALs face multiple adoption issues: the literature regarding their development and use is incohesive, coupling to a weave target hinders re-use, and available tooling is immature compared to Domain-Specific Languages (DSLs). We believe these issues can be aided by furthering DSL middle-out techniques for DSALs.We first define the background of what a DSAL is and how they may be used, moving onto how we can use DSL techniques to further DSALs. We develop a middle-out semantic model approach for developing domain-level DSALs with transparent aspect orientation using adaptions of DSL techniques. We have implemented the approach for model-specific DSALs for the in-house framework Animaux, and as middleware-specific DSAL for agent messages in the JADE framework, which can be specialised to models using extension DSALs. We give illustrative result cases using our implementations to provide a base of the user development costs and performance of this approach.In conclusion, we believe the adoption of these technologies aids ABM applications and encourage future work in similar fields. This thesis has given a base philosophy toward DSLs, a novel approach for the development of middle-out DSALs and illustrative cases of this approach

Automatic Transformation-Based Model Checking of Multi-agent Systems

Multi-Agent Systems (MASs) are highly useful constructs in the context of real-world software applications. Built upon communication and interaction between autonomous agents, these systems are suitable to model and implement intelligent applications. Yet these desirable features are precisely what makes these systems very challenging to design, and their compliance with requirements extremely difficult to verify. This explains the need for the development of techniques and tools to model, understand, and implement interacting MASs. Among the different methods developed, the design-time verification techniques for MASs based on model checking offer the advantage of being formal and fully automated. We can distinguish between two different approaches used in model checking MASs, the direct verification approach, and the transformation-based approach. This thesis focuses on the later that relies on formal reduction techniques to transform the problem of model checking a source logic into that of an equivalent problem of model checking a target logic. In this thesis, we propose a new transformation framework leveraging the model checking of the computation tree logic (CTL) and its NuSMV model checker to design and implement the process of transformation-based model checking for CTL-extension logics to MASs. The approach provides an integrated system with a rich set of features, designed to support the transformation process while simplifying the most challenging and error-prone tasks. The thesis presents and describes the tool built upon this framework and its different applications. A performance comparison with MCMAS, the model checker of MASs, is also discussed

From Business Understanding to Deployment: An application of Machine Learning Algorithms to Forecast Customer Visits per Hour to a Fast-Casual Restaurant in Dublin

This research project identifies the significant factors that affects the number of customer visits to a fast-casual restaurant every hour and proceeds to develop several machine learning models to forecast customer visits. The core value proposition of fast-casual restaurants is quality food delivered at speed which means they have to prepare meals in advance of customers visit but the problem with this approach is in forecasting future demand, under estimating demand could lead to inadequate meal preparation which would leave customers unsatisfied while over estimation of demand could lead to wastage especially with restaurants having to comply with food safety regulations whereby heated food not consumed within 90 minutes has to be discarded. Hourly forecasting of demand as opposed to monthly or even daily forecasting is important to help the manager of the fast-casual restaurant optimize resources and reduce wastage. Approaches to forecasting demand can be broadly categorized into qualitative and quantitative methods. Quantitative methods can be further divided into time series and regression-based methods. The regression-based approach which is used for this study enabled the researcher to gather data on several factors hypothesized to have an impact on the number of customer visits to the fast-casual restaurant every hour, carry out an experiment to test for the significance of these factors and to develop several predictive machine learning models capable of predicting the number of customer visits every hour. The results of the experiments carried out shows that hour, day, public holidays, temperature, humidity, rain and windspeed are significant factors in predicting the number of hourly customer visits. Multiple linear regression, regression tree, random forest and gradient boosting machine learning algorithms were also trained to predict the number of customer visits with the Gradient boosting algorithm achieving the lowest Mean Absolute Percentage Error(MAPE) of 18.82%

An Automated Negotiation System for eCommerce Store Owners to Enable Flexible Product Pricing

If a store owner wishes to sell a product online, they traditionally have two options for deciding on a price. They can sell the product at a fixesd price like the products sold on sites like Amazon, or they can put the product in an auction and let demand from customers drive the final sales price like the products sold on sites like eBay. Both options have their pros and cons. An alternative option for deciding on a final sales price for the product is to enable negotiation on the product. With this, there is a dynamic nature to the price; each customer can negotiate with the store owner on the price which allows the final sales price to both change over time and on a customer by customer basis. The issue with enabling negotiation in the context of eCommerce is the time investment needed from the store owner. A store owner cannot negotiate every time an offer comes in from a potential customer, the potential time investment would not be acceptable. Using software agents to automate the process of negotiation for the seller is a potential solution to enabling negotiation in eCommerce for store owners. In this research, a system such as the one just described is developed in a way that mirrors real life negotiations more closely and after evaluation, is found to be a potential solution for the enabling of negotiation in eCommerce

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

Designing and trusting multi-agent systems for B2B applications

This thesis includes two main contributions. The first one is designing and implementing B usiness-to-B usiness (B2B ) applications using multi-agent systems and computational argumentation theory. The second one is trust management in such multi-agent systems using agents' credibility. Our first contribution presents a framework for modeling and deploying B2B applications, with autonomous agents exposing the individual components that implement these applications. This framework consists of three levels identified by strategic, application, and resource, with focus here on the first two levels. The strategic level is about the common vision that independent businesses define as part of their decision of partnership. The application level is about the business processes, which are virtually integrated as result of this common vision. Since conflicts are bound to arise among the independent applications/agents, the framework uses a formal model based upon computational argumentation theory through a persuasion protocol to detect and resolve these conflicts. Termination, soundness, and completeness properties of this protocol are presented. Distributed and centralized coordination strategies are also supported in this framework, which is illustrated with an online purchasing case study followed by its implementation in Jadex, a java-based platform for multi-agent systems. An important issue in such open multi-agent systems is how much agents trust each other. Considering the size of these systems, agents that are service providers or customers in a B2B setting cannot avoid interacting with others that are unknown or partially known regarding to some past experience. Due to the fact that agents are self-interested, they may jeopardize the mutual trust by not performing the actions as they are supposed to. To this end, our second contribution is proposing a trust model allowing agents to evaluate the credibility of other peers in the environment. Our multi-factor model applies a number of measurements in trust evaluation of other party's likely behavior. After a period of time, the actual performance of the testimony agent is compared against the information provided by interfering agents. This comparison process leads to both adjusting the credibility of the contributing agents in trust evaluation and improving the system trust evaluation by minimizing the estimation error