Systematic incremental development of agent systems using Prometheus

This paper presents a mechanism for dividing an agent oriented application into the three IEEE defined scoping levels of essential, conditional and optional. This mechanism is applied after the initial system specification, and is then used to direct incremental development with three separate releases. The scoping described can be applied at any stage of a project, in order to guide consistent scoping back if such is needed. The three levels of scoping that are used are consistent with the approach used in many companies. The approach to scoping requires that scenarios are prioritised manually on a five point scale. All other aspects are then prioritised automatically, based on this information. The approach used allows a developer to indicate what size partitions - based on number of scenarios - are required for each scoping level. The mechanisms are applied to the Prometheus development methodology and are integrated into the Prometheus design tool (PDT)

Adding debugging support to the Prometheus methodology

This paper describes a debugger which uses the design artifacts of the Prometheus agent-oriented software engineering methodology to alert the developer testing the system, that a specification has been violated. Detailed information is provided regarding the error which can help the developer in locating its source. Interaction protocols specified during design, are converted to executable Petri net representations. The system can then be monitored at run time to identify situations which do not conform to specified protocols. A process for monitoring aspects of plan selection is also described. The paper then describes the Prometheus Design Tool, developed to support the Prometheus methodology, and presents a vision of an integrated development environment providing full life cycle support for the development of agent systems. The initial part of the paper provides a detailed summary of the Prometheus methodology and the artifacts on which the debugger is based

Agent-oriented software engineering methodologies : analysis and future directions

The Internet of Things (IoT) facilitates in building cyber-physical systems, which are significant for Industry 4.0. Agent-based computing represents effective modeling, programming, and simulation paradigm to develop IoT systems. Agent concepts, techniques, methods, and tools are being used in evolving IoT systems. Over the last years, in particular, there has been an increasing number of agent approaches proposed along with an ever-growing interest in their various implementations. Yet a comprehensive and full-fledged agent approach for developing related projects is still lacking despite the presence of agent-oriented software engineering (AOSE) methodologies. One of the moves towards compensating for this issue is to compile various available methodologies, ones that are comparable to the evolution of the unified modeling language (UML) in the domain of object-oriented analysis and design. These have become de facto standards in software development. In line with this objective, the present research attempts to comprehend the relationship among seven main AOSE methodologies. More specifically, we intend to assess and compare these seven approaches by conducting a feature analysis through examining the advantages and limitations of each competing process, structural analysis, and a case study evaluation method. This effort is made to address the significant characteristics of AOSE approaches. The main objective of this study is to conduct a comprehensive analysis of selected AOSE methodologies and provide a proposal of a draft unified approach that drives strengths (best) of these methodologies towards advancement in this area.publishedVersio

Prioritisation mechanisms to support incremental development of agent systems

It is often necessary to partition a project into different priority levels and to develop incrementally. This paper presents a mechanism whereby a developer can prioritise scenarios on a five point scale, leading to automated, coherent partitioning of all required design entities, according to the three IEEE defined priority levels of essential, conditional and optional, which are used in many companies. This allows for automated support to guide the developer as to what design artefacts need to be developed at each phase. The developer can indicate the relative sizes desired for the three partitions and the algorithm described will attempt to get as close to this as possible. It is also possible to move items manually to achieve better sized partitions, as long as priority orderings are not violated. The approach is fast and easy to apply at various times during development, as needed

Design and Analysis of a Multi-Agent E-Learning System Using Prometheus Design Tool

Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of Prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.Comment: 17 figures, 3 table

Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems

Thesis (M. Tech.) -- Central University of Technology, Free State, 2010Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation

Software Traceability for Multi-Agent Systems Implemented Using BDI Architecture

The development of multi-agent software systems is considered a complex task due to (a) the large number and heterogeneity of documents generated during the development of these systems, (b) the lack of support for the whole development life-cycle by existing agent-oriented methodologies requiring the use of different methodologies, and (c) the possible incompleteness of the documents and models generated during the development of the systems. In order to alleviate the above problems, in this thesis, a traceability framework is described to support the development of multi-agent systems. The framework supports automatic generation of traceability relations and identification of missing elements (i.e., completeness checking) in the models created during the development life-cycle of multi-agent systems using the Belief-Desire-Intention (BDI) architecture. Traceability has been recognized as an important activity in the software development process. Traceability relations can guarantee and improve software quality and can help with several tasks such as the evolution of software systems, reuse of parts of the system, validation that a system meets its requirements, understanding of the rationale for certain design decisions, identification of common aspects of the system, and analysis of implications of changes in the system. The traceability framework presented in this thesis concentrates on multi-agent software systems developed using i* framework, Prometheus methodology, and JACK language. Here, a traceability reference model is presented for software artefacts generated when using i* framework, Prometheus methodology, and JACK language. Different types of relations between the artefacts are identified. The framework is based on a rule-based approach to support automatic identification of traceability relations and missing elements between the generated artefacts. Software models represented in XML were used to support the heterogeneity of models and tools used during the software development life-cycle. In the framework, the rules are specified in an extension of XQuery to support (i) representation of the consequence part of the rules, i.e. the actions to be taken when the conditions are satisfied, and (ii) extra functions to cover some of the traceability relations being proposed and completeness checking of the models. A prototype tool has been developed to illustrate and evaluate the work. The work has been evaluated in terms of recall and precision measurements in three different case studies. One small case study of an Automatic Teller Machine application, one medium case study of an Air Traffic Control Environment application, and one large case study of an Electronic Bookstore application