Supporting Dimensional Analysis in SystemC-AMS

This paper will introduce new modeling capabilities for SystemC-AMS to describe energy conserving multi-domain systems in a formal and consistent way at a high level of abstraction. To this end, all variables and parameters of the system model need to be annotated with their measurement units in such a way that they become intrinsic part of the data type. This enforces correct model assembly through strict interfaces and coherent formulas describing the analog behavior by means of dimensional analysis. A library of generic block diagram components has been developed to demonstrate how both requirements can be met using the Boost libraries together with SystemC- AMS. The demonstrated implementation techniques are the key to integrate new Models of Computation (MoCs) into SystemC-AMS to facilitate further the description of multi-domain systems

Spud 1.0: generalising and automating the user interfaces of scientific computer models

The interfaces by which users specify the scenarios to be simulated by scientific computer models are frequently primitive, under-documented and ad-hoc text files which make using the model in question difficult and error-prone and significantly increase the development cost of the model. In this paper, we present a model-independent system, Spud, which formalises the specification of model input formats in terms of formal grammars. This is combined with an automated graphical user interface which guides users to create valid model inputs based on the grammar provided, and a generic options reading module, libspud, which minimises the development cost of adding model options. <br><br> Together, this provides a user friendly, well documented, self validating user interface which is applicable to a wide range of scientific models and which minimises the developer input required to maintain and extend the model interface

From SMART to agent systems development

In order for agent-oriented software engineering to prove effective it must use principled notions of agents and enabling specification and reasoning, while still considering routes to practical implementation. This paper deals with the issue of individual agent specification and construction, departing from the conceptual basis provided by the SMART agent framework. SMART offers a descriptive specification of an agent architecture but omits consideration of issues relating to construction and control. In response, we introduce two new views to complement SMART: a behavioural specification and a structural specification which, together, determine the components that make up an agent, and how they operate. In this way, we move from abstract agent system specification to practical implementation. These three aspects are combined to create an agent construction model, actSMART, which is then used to define the AgentSpeak(L) architecture in order to illustrate the application of actSMART

What use are formal design and analysis methods to telecommunications services?

Have formal methods failed, or will they fail, to help us solve problems of detecting and resolving of feature interactions in telecommunications software? This paper contains SWOT(Strengths, Weaknesses, Opportunities and Threats) analysis of the use of formula design and analysis methods in feature interaction analysis and makes some suggestions for future research

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

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