Analysis and design of multiagent systems using MAS-CommonKADS

This article proposes an agent-oriented methodology called MAS-CommonKADS and develops a case study. This methodology extends the knowledge engineering methodology CommonKADSwith techniquesfrom objectoriented and protocol engineering methodologies. The methodology consists of the development of seven models: Agent Model, that describes the characteristics of each agent; Task Model, that describes the tasks that the agents carry out; Expertise Model, that describes the knowledge needed by the agents to achieve their goals; Organisation Model, that describes the structural relationships between agents (software agents and/or human agents); Coordination Model, that describes the dynamic relationships between software agents; Communication Model, that describes the dynamic relationships between human agents and their respective personal assistant software agents; and Design Model, that refines the previous models and determines the most suitable agent architecture for each agent, and the requirements of the agent network

A survey of agent-oriented methodologies

This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey

Construction of a taxonomy for requirements engineering commercial-off-the-shelf components

This article presents a procedure for constructing a taxonomy of COTS products in the field of Requirements Engineering (RE). The taxonomy and the obtained information reach transcendental benefits to the selection of systems and tools that aid to RE-related actors to simplify and facilitate their work. This taxonomy is performed by means of a goal-oriented methodology inspired in GBRAM (Goal-Based Requirements Analysis Method), called GBTCM (Goal-Based Taxonomy Construction Method), that provides a guide to analyze sources of information and modeling requirements and domains, as well as gathering and organizing the knowledge in any segment of the COTS market. GBTCM claims to promote the use of standards and the reuse of requirements in order to support different processes of selection and integration of components.Peer ReviewedPostprint (published version

The Semantic Grid: A future e-Science infrastructure

e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practice–aspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid

Some Ideas and Examples to Evaluate Ontologies

The lack of methods for evaluating ontologies in laboratories can be an obstacle to their use in companies. This paper presents a set of emerging ideas in evaluation of ontologies useful for: (1) ontologies developers in the lab, as a foundation from which to perform technical evaluations; (2) end users of ontologies in companies, as a point of departure in the search for the best ontology for their systems; and (3) future research, as a basis upon which to perform progressive and disciplined investigations in this area. After briefly exploring some general questions such as: why, what, when, how and where to evaluate; who evaluates; and, what to evaluate against, we focus on the definition of a set of criteria useful in the evaluation process. Finally, we use some of these criteria in the evaluation of the Bibliographic-Data [5] ontology

Understanding tissue morphology: model repurposing using the CoSMoS process

We present CoSMoS as a way of structuring thinking on how to reuse parts of an existing model and simulation in a new model and its implementation. CoSMoS provides a lens through which to consider, post-implementation, the assumptions made during the design and implementation of a software simulation of physical interactions in the formation of vascular structures from endothelial cells. We show how the abstract physical model and its software implementation can be adapted for a different problem: the growth of cancer cells under varying environmental perturbations. We identify the changes that must be made to adapt the model to its new context, along with the gaps in our knowledge of the domain that must be filled by wet-lab experimentation when recalibrating the model. Through parameter exploration, we identify the parameters that are critical to the dynamic physical structure of the modelled tissue, and we calibrate these parameters using a series of in vitro experiments. Drawing inspiration from the CoSMoS project structure, we maintain confidence in the repurposed model, and achieve a satisfactory degree of model reuse within our in silico experimental system