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

Conceptual Spaces in Object-Oriented Framework

The aim of this paper is to show that the middle level of mental representations in a conceptual spaces framework is consistent with the OOP paradigm. We argue that conceptual spaces framework together with vague prototype theory of categorization appears to be the most suitable solution for modeling the cognitive apparatus of humans, and that the OOP paradigm can be easily and intuitively reconciled with this framework. First, we show that the prototypebased OOP approach is consistent with Gärdenfors’ model in terms of structural coherence. Second, we argue that the product of cloning process in a prototype-based model is in line with the structure of categories in Gärdenfors’ proposal. Finally, in order to make the fuzzy object-oriented model consistent with conceptual space, we demonstrate how to define membership function in a more cognitive manner, i.e. in terms of similarity to prototype

An architecture and methodology for the design and development of Technical Information Systems

In order to meet demands in the context of Technical Information Systems (TIS) pertaining to reliability, extensibility, maintainability, etc., we have developed an architectural framework with accompanying methodological guidelines for designing such systems. With the framework, we aim at complex multiapplication information systems using a repository to share data among applications. The framework proposes to keep a strict separation between Man-Machine-Interface and Model data, and provides design and implementation support to do this effectively.\ud The framework and methodological guidelines have been developed in the context of the ESPRIT project IMPRESS. The project also provided for ldquotesting groundsrdquo in the form of a TIS for the Spanish Electricity company Iberdrola.\ud This work has been conducted within the ESPRIT project IMPRESS (Integrated, Multi-Paradigm, Reliable and Extensible Storage System), ESPRIT No. 635

Towards a systemic research methodology in agriculture: Rethinking the role of values in science

The recent drastic development of agriculture, together with the growing societal interest in agricultural practices and their consequences, pose a challenge to agricultural science. There is a need for rethinking the general methodology of agricultural research. This paper takes some steps towards developing a systemic research methodology that can meet this challenge – a general self-reflexive methodology that forms a basis for doing holistic or (with a better term) wholeness-oriented research and provides appropriate criteria of scientific quality. From a philosophy of research perspective, science is seen as an interactive learning process with both a cognitive and a social communicative aspect. This means, first of all, that science plays a role in the world that it studies. A science that influences its own subject area, such as agricultural science, is named a systemic science. From this perspective, there is a need to reconsider the role of values in science. Science is not objective in the sense of being value-free. Values play, and ought to play, an important role in science – not only in form of constitutive values such as the norms of good science, but also in the form of contextual values that enter into the very process of science. This goes against the traditional criterion of objectivity. Therefore, reflexive objectivity is suggested as a new criterion for doing good science, along with the criterion of relevance. Reflexive objectivity implies that the communication of science must include the cognitive context, which comprises the societal, intentional, and observational context. In accordance with this, the learning process of systemic research is shown as a self-reflexive cycle that incorporates both an involved actor stance and a detached observer stance. The observer stance forms the basis for scientific communication. To this point, a unitary view of science as a learning process is employed. A second important perspective for a systemic research methodology is the relation between the actual, different, and often quite separate kinds of science. Cross-disciplinary research is hampered by the idea that reductive science is more objective, and hence more scientific, than the less reductive sciences of complex subject areas – and by the opposite idea that reductive science is necessarily reductionistic. Taking reflexive objectivity as a demarcator of good science, an inclusive framework of science can be established. The framework does not take the established division between natural, social and human science as a primary distinction of science. The major distinction is made between the empirical and normative aspects of science, corresponding to two key cognitive interests. Two general methodological dimensions, the degree of reduction of the research world and the degree of involvement in the research world, are shown to span this framework. The framework can form a basis for transdisciplinary work by way of showing the relation between more and less reductive kinds of science and between more detached and more involved kinds of science and exposing the abilities and limitations attendant on these methodological differences

Model Based Development of Quality-Aware Software Services

Modelling languages and development frameworks give support for functional and structural description of software architectures. But quality-aware applications require languages which allow expressing QoS as a first-class concept during architecture design and service composition, and to extend existing tools and infrastructures adding support for modelling, evaluating, managing and monitoring QoS aspects. In addition to its functional behaviour and internal structure, the developer of each service must consider the fulfilment of its quality requirements. If the service is flexible, the output quality depends both on input quality and available resources (e.g., amounts of CPU execution time and memory). From the software engineering point of view, modelling of quality-aware requirements and architectures require modelling support for the description of quality concepts, support for the analysis of quality properties (e.g. model checking and consistencies of quality constraints, assembly of quality), tool support for the transition from quality requirements to quality-aware architectures, and from quality-aware architecture to service run-time infrastructures. Quality management in run-time service infrastructures must give support for handling quality concepts dynamically. QoS-aware modeling frameworks and QoS-aware runtime management infrastructures require a common evolution to get their integration

On Agent-Based Software Engineering

Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. Moreover, even less effort has been devoted to discussing the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures

Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction

The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation