Behaviour Driven Development for Multi-Agent Systems

This paper presents a testing methodology to apply Behaviour Driven Development (BDD) techniques while developing Multi-Agent Systems (MAS), so called BEhavioural Agent Simple Testing (BEAST) methodology. It is supported by the developed open source framework (BEAST Tool) which automatically generates test cases skeletons from BDD scenarios specifications. The developed framework allows testing MASs based on JADE or JADEX platforms and offers a set of configurable Mock Agents which allow the execution of tests while the system is under development. BEAST tool has been validated in the development of a MAS for fault diagnosis in FTTH (Fiber To The Home) networks

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Crowd Sourcing Rules in Agile Software Engineering to Improve Efficiency Using Ontological Framework

Business Rule Management System provides the necessary seeds for the planning, implementing, verifying and validating the Agile Requirements. The BRMS model needs to be modified in a way that organizational growth runs parallel with the intrinsic expansion in the number of User Requirements in Agile Development. This growth in Requirements or Rules in Agile Software Development is an obvious overhead that needs to be managed properly considering its sprint nature. A Semantic approach is followed by design and maintenance of an Ontology called RAgile. The ontology is developed in ‘Protégé 5 having inherent capability of Ontology Merging in case of disparate Rule files. User requirements that are drawn into the Rules or Policies depend upon the features users expect of the Agile System

MULTIAGENT SYSTEMS FOR SHOP FLOOR ARHITECTURE MANAGEMENT

The paper presents the problem of shop floor agility. In order to cope with the disturbances and uncertainties that characterise the current business scenarios faced by manufacturing companies, the capability of their shop floors needs to be improved quickly, such that these shop floors may be adapted, changed or become easily modifiable (shop floor reengineering). One of the critical elements in any shop floor reengineering process is the way the control/supervision architecture is changed or modified to accommodate for the new process and equipment. This paper, therefore, proposes an multi-agent architecture to support the fast adaptation or changes in the control/supervision architecture.multi-agent system, shop floor agility, control/supervision architecture, virtual organisation.

Human Factors in Agile Software Development

Through our four years experiments on students' Scrum based agile software development (ASD) process, we have gained deep understanding into the human factors of agile methodology. We designed an agile project management tool - the HASE collaboration development platform to support more than 400 students self-organized into 80 teams to practice ASD. In this thesis, Based on our experiments, simulations and analysis, we contributed a series of solutions and insights in this researches, including 1) a Goal Net based method to enhance goal and requirement management for ASD process, 2) a novel Simple Multi-Agent Real-Time (SMART) approach to enhance intelligent task allocation for ASD process, 3) a Fuzzy Cognitive Maps (FCMs) based method to enhance emotion and morale management for ASD process, 4) the first large scale in-depth empirical insights on human factors in ASD process which have not yet been well studied by existing research, and 5) the first to identify ASD process as a human-computation system that exploit human efforts to perform tasks that computers are not good at solving. On the other hand, computers can assist human decision making in the ASD process.Comment: Book Draf

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

A Review: Effort Estimation Model for Scrum Projects using Supervised Learning

Effort estimation practice in Agile is a critical component of the methodology to help cross-functional teams to plan and prioritize their work. Agile approaches have emerged in recent years as a more adaptable means of creating software projects because they consistently produce a workable end product that is developed progressively, preventing projects from failing entirely. Agile software development enables teams to collaborate directly with clients and swiftly adjust to changing requirements. This produces a result that is distinct, gradual, and targeted. It has been noted that the present Scrum estimate approach heavily relies on historical data from previous projects and expert opinion, while existing agile estimation methods like analogy and planning poker become unpredictable in the absence of historical data and experts. User Stories are used to estimate effort in the Agile approach, which has been adopted by 60–70% of the software businesses. This study's goal is to review a variety of strategies and techniques that will be used to gauge and forecast effort. Additionally, the supervised machine learning method most suited for predictive analysis is reviewed in this paper