Expressivity and axiomatizations are investigated for models without restrictions, and for models with equivalence relations for all agents (modeling knowledge) and awareness introspection (agents know what they are aware of).
The dawn of a new era in knowledge management due to information explosion is making old habits of modeling knowledge and decision-making inadequate.
Major benefits of our approach include: it recovers the overall shape from a single tree image, involves no tree modeling knowledge and minimal authoring effort, and the associated image-based representation is easy to compress and thus suitable for network streaming.
Further, to illustrate the application of the meta-modeling framework, we present the design and development of MetaSimModel – a knowledge-based decision support environment to organize, manage, use, and reuse modeling knowledge of simulation models in a flexible manufacturing environment.
We first present a classification of stakeholders from the perspective of model management and identify the layers of modeling knowledge required for managing models.
Process-based modeling focuses on explanatory models of dynamical systems; it constructs such models from measured time-course data and formalized modeling knowledge.
With the help of Linux-Apache-MySQL-Perl (LAMP) architecture, users can manage and retrieve modeling data and model parameterizations to reach a consensus on the simulation results and share modeling knowledge.
Despite current computational technologies and current modeling knowledge, the scale of micro-simulation is limited to a portion of a city and not the entire city.
The study proposes a generalized method for meta-modelbase management to use, store, and manage modeling knowledge.
The choice of W3C recommended technology was made on the premise that modeling knowledge in a computable, standardized, and community supported format will provide long term benefit for the synthetic biology community.
Instructional methods that involve dialogue with fellow students (Lemke, 1990), active learning (Ebert-May et al., 1997), problem solving (Mintzes et al., 1998), animation (Sanger et al., 2001), and/or modeling knowledge (Fisher et al., 2000; Odom and Kelly, 2001; Tekkaya, 2003) may lead to significant improvements in understanding (Freeman et al., 2007).
