In some areas of software engineering research, there are several metamodels claiming to capture the main issues. Though it is profitable to have variety at the beginning of a research field, after some time, the diversity of metamodels becomes an obstacle, for instance to the sharing of results between research groups. To reach consensus and unification of existing metamodels, metamodel-driven software language engineering can be applied. This paper illustrates an application of software language engineering in the agent-oriented software engineering research domain. Here, we introduce a relatively generic agent-oriented metamodel whose suitability for supporting modeling language development is demonstrated by evaluating it with respect to several existing methodology-specific metamodels. First, the metamodel is constructed by a combination of bottom-up and top-down analysis and best practice. The concepts thus obtained and their relationships are then evaluated by mapping to two agent-oriented metamodels: TAO and Islander. We then refine the metamodel by extending the comparisons with the metamodels implicit or explicit within five more extant agent-oriented approaches: Adelfe, PASSI, Gaia, INGENIAS, and Tropos. The resultant FAML metamodel is a potential candidate for future standardization as an important component for engineering an agent modeling language. Disciplines Physical Sciences and Mathematics Publication DetailsBeydoun, G., Low, G. Abstract-In some areas of software engineering research, there are several metamodels claiming to capture the main issues. Though it is profitable to have variety at the beginning of a research field, after some time, the diversity of metamodels becomes an obstacle, for instance to the sharing of results between research groups. To reach consensus and unification of existing metamodels, metamodel-driven software language engineering can be applied. This paper illustrates an application of software language engineering in the agent-oriented software engineering research domain. Here, we introduce a relatively generic agent-oriented metamodel whose suitability for supporting modeling language development is demonstrated by evaluating it with respect to several existing methodology-specific metamodels. First, the metamodel is constructed by a combination of bottom-up and top-down analysis and best practice. The concepts thus obtained and their relationships are then evaluated by mapping to two agent-oriented metamodels: TAO and Islander. We then refine the metamodel by extending the comparisons with the metamodels implicit or explicit within five more extant agent-oriented approaches: Adelfe, PASSI, Gaia, INGENIAS, and Tropos. The resultant FAML metamodel is a potential candidate for future standardization as an important component for engineering an agent modeling language.Index Terms-Modeling, metamodel, multiagent systems.

Disaster Management (DM) is a diffused area of knowledge. It has many complex features interconnecting the physical and the social views of the world. Many international and national bodies create knowledge models to allow knowledge sharing and effective DM activities. But these are often narrow in focus and deal with specified disaster types. We analyze thirty such models to uncover that many DM activities are actually common even when the events vary. We then create a unified view of DM in the form of a metamodel. We apply a metamodelling process to ensure that this metamodel is complete and consistent. We validate it and present a representational layer to unify and share knowledge as well as combine and match different DM activities according to different disaster situations.

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We capture student interactions in an e-learning community to construct a semantic web (SW) to create a collective meta-knowledge structure guiding students as they search the existing knowledge corpus. We use formal concept analysis (FCA) as a knowledge acquisition tool to process the students virtual surfing trails to express and exploit the dependencies between web-pages to yield subsequent and more effective focused search results. We mirror social navigation and bypass the cumbersome manual annotation of webpages and at the same time paralleling social navigation for knowledge. We present our system KAPUST2 (Keeper and Processor of User Surfing Trails) which constructs from captured students trails a conceptual lattice guiding student queries. We use KAPUST as an e-learning software for an undergraduate class over two semesters. We show how the lattice evolved over the two semesters, improving its performance by exploring the relationship between 'kinds' of research assignments and the e-learning semantic web development. Course instructors monitored the evolution of the lattice with interesting positive pedagogical consequences. These are reported as well in this paper.

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In Disaster Management (DM), a Decision Support System (DSS) first and foremost requires a sufficient amount of representative knowledge to avail it to authorities who need it. A challenge in providing DM knowledge is that relevant authorities are often varied with different requirements. The knowledge may be needed and presented differently according to the decision making process whether it be for planning/policy making or responding operationally to a real time situation. This reality is reflected in the fact that DSS in DM is often focussed on very specific set of tasks e.g. evacuation, logistic or coordination and/or DM phase specific e.g. Preparedness or Response only. However, the trigger for accessing such knowledge is often an external change be it environmental or organisational. Hence, a variety of views or stakeholders are often involved. DM is a complex domain and various concerns and phases are often intertwined. This work advocates a unified access based on a unified representation of the various phases and concerns. The paper contributes to development of the required hybrid mechanisms, some are top down and others are bottom up. The proposed DSS architecture and its development lifecycle accommodate interests of varying DM decision makers. A knowledge conversion process centred on the Meta Object Facility (MOF) is used to structure and disentangle the knowledge for those different interests and roles. A real case study of the recent Mt. Agung volcano eruption in Bali Indonesia is successfully used to demonstrate the efficacy of the development process and the resultant DSS.