The uses of process modeling : a framework for understanding modeling formalisms

There is wide-spread recognition of the urgent need to improve software processes in order to improve the performance of software organizations. Process models are essential in achieving understanding and visibility of processes and are important for other uses including the analysis of processes for improvement. It has been increasingly difficult to compare and evaluate the variety of process modeling formalisms that have appeared in recent years without a clear understanding of precisely for what they will be used. The contribution of this paper is to provide an understanding and a fairly comprehensive catalog of the applications of process modeling for which formalisms may be used. The primary mechanism for doing this is a guided tour of the literature on process modeling supplemented by recent industrial experience. In the paper, basic definitions concerning processes, process descriptions and process modeling are reviewed and then uses of process modeling are surveyed under the following headings: communication among process participants, construction of new processes, control of processes, process· analysis, and process support by automation. Comments are offered on paradigms for process modeling formalisms and directions for future work to permit evolution of a discipline of process engineering are given

Behavior change interventions: the potential of ontologies for advancing science and practice

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Automated support for experience-based software management

To effectively manage a software development project, the software manager must have access to key information concerning a project's status. This information includes not only data relating to the project of interest, but also, the experience of past development efforts within the environment. This paper describes the concepts and functionality of a software management tool designed to provide this information. This tool, called the Software Management Environment (SME), enables the software manager to compare an ongoing development effort with previous efforts and with models of the 'typical' project within the environment, to predict future project status, to analyze a project's strengths and weaknesses, and to assess the project's quality. In order to provide these functions the tool utilizes a vast corporate memory that includes a data base of software metrics, a set of models and relationships that describe the software development environment, and a set of rules that capture other knowledge and experience of software managers within the environment. Integrating these major concepts into one software management tool, the SME is a model of the type of management tool needed for all software development organizations

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations