Goal Driven Architecture Development using LEAP

Methods for goal driven system engineering exist and propose a number of categories of goals including behavioural, formal, informal and non-functional. This article goes further than existing goal driven approaches by linking goals directly to the semantics of an architectural modelling language called LEAP with an operational semantics. The behavioural goals are expressed using a Linear Temporal Logic and the non-functional goals are expressed as functions over meta-properties of the model. The meta-properties are supported using an encoding represented using Java reflection. The article describes the LEAP approach using a simple case study written in the LEAP language supported by the LEAP toolset

mPower: A component-based development framework for multi-agent systems to support business processes

One of the obstacles preventing the widespread adoption of multi-agent systems in industry is the difficulty of implementing heterogeneous interactions among participating agents via asynchronous messages. This difficulty arises from the need to understand how to combine elements of various content languages, ontologies, and interaction protocols in order to construct meaningful and appropriate messages. In this paper mPower, a component-based layered framework for easing the development of multi-agent systems, is described, and the facility for customising the components for reuse in similar domains is explained. The framework builds on the JADE-LEAP platform, which provides a homogeneous layer over diverse operating systems and hardware devices, and allows ubiquitous deployment of applications built on multi-agent systems both in wired and wireless environments. The use of the framework to develop mPowermobile , a multi-agent system to support mobile workforces, is reported

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

CAMMD: Context Aware Mobile Medical Devices

Telemedicine applications on a medical practitioners mobile device should be context-aware. This can vastly improve the effectiveness of mobile applications and is a step towards realising the vision of a ubiquitous telemedicine environment. The nomadic nature of a medical practitioner emphasises location, activity and time as key context-aware elements. An intelligent middleware is needed to effectively interpret and exploit these contextual elements. This paper proposes an agent-based architectural solution called Context-Aware Mobile Medical Devices (CAMMD). This framework can proactively communicate patient records to a portable device based upon the active context of its medical practitioner. An expert system is utilised to cross-reference the context-aware data of location and time against a practitioners work schedule. This proactive distribution of medical data enhances the usability and portability of mobile medical devices. The proposed methodology alleviates constraints on memory storage and enhances user interaction with the handheld device. The framework also improves utilisation of network bandwidth resources. An experimental prototype is presented highlighting the potential of this approach

Learning Generalized Reactive Policies using Deep Neural Networks

We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach significantly reduces the dependence of learning on handcrafted domain knowledge or feature selection. Instead, the GRP is trained from scratch using a set of successful execution traces. We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and show that our approach facilitates learning complex decision making policies and powerful heuristic functions with minimal human input. Videos of our results are available at goo.gl/Hpy4e3

Comparing consortial repositories: a model-driven analysis

This study aims to provide a comparative assessment of different repository consortia as a reference to inform future work in the area. A review of the literature was used to identify repository consortia, and their features were compared. Three models of consortial repositories were derived from this comparison, based on their structure and aims. The consortial models were based around either: creating a shared repository for the members, developing a repository software platform or creating a metadata harvesting service to aggregate content. Using case studies of each type of repository consortium, each model was assessed in terms of its particular strengths and weaknesses. These strengths were then compared across the models to enable those considering a consortial repository project to assess which model, or combination of models, would best address their needs and to aid in project planning