5 research outputs found
Towards More Human-like AI Communication: A Review of Emergent Communication Research
In the recent shift towards human-centric AI, the need for machines to
accurately use natural language has become increasingly important. While a
common approach to achieve this is to train large language models, this method
presents a form of learning misalignment where the model may not capture the
underlying structure and reasoning humans employ in using natural language,
potentially leading to unexpected or unreliable behavior. Emergent
communication (Emecom) is a field of research that has seen a growing number of
publications in recent years, aiming to develop artificial agents capable of
using natural language in a way that goes beyond simple discriminative tasks
and can effectively communicate and learn new concepts. In this review, we
present Emecom under two aspects. Firstly, we delineate all the common
proprieties we find across the literature and how they relate to human
interactions. Secondly, we identify two subcategories and highlight their
characteristics and open challenges. We encourage researchers to work together
by demonstrating that different methods can be viewed as diverse solutions to a
common problem and emphasize the importance of including diverse perspectives
and expertise in the field. We believe a deeper understanding of human
communication is crucial to developing machines that can accurately use natural
language in human-machine interactions.Comment: 25 pages, 9 figures, 2 table
Opinion Mining for Software Development: A Systematic Literature Review
Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies.
SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in
code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take
considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils
these approaches entail.
We conducted a systematic literature review involving 185 papers. More specifically, we present 1) well-defined categories of opinion
mining-related software development activities, 2) available opinion mining approaches, whether they are evaluated when adopted in
other studies, and how their performance is compared, 3) available datasets for performance evaluation and tool customization, and 4)
concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques.
The results of our study serve as references to choose suitable opinion mining tools for software development activities, and provide
critical insights for the further development of opinion mining techniques in the SE domain
Model-Based Engineering of Collaborative Embedded Systems
This Open Access book presents the results of the "Collaborative Embedded Systems" (CrESt) project, aimed at adapting and complementing the methodology underlying modeling techniques developed to cope with the challenges of the dynamic structures of collaborative embedded systems (CESs) based on the SPES development methodology. In order to manage the high complexity of the individual systems and the dynamically formed interaction structures at runtime, advanced and powerful development methods are required that extend the current state of the art in the development of embedded systems and cyber-physical systems. The methodological contributions of the project support the effective and efficient development of CESs in dynamic and uncertain contexts, with special emphasis on the reliability and variability of individual systems and the creation of networks of such systems at runtime. The project was funded by the German Federal Ministry of Education and Research (BMBF), and the case studies are therefore selected from areas that are highly relevant for Germany’s economy (automotive, industrial production, power generation, and robotics). It also supports the digitalization of complex and transformable industrial plants in the context of the German government's "Industry 4.0" initiative, and the project results provide a solid foundation for implementing the German government's high-tech strategy "Innovations for Germany" in the coming years