The development of agent-based systems is negatively impacted by the lack of process standardization across the major development phases, such as the requirements analysis phase. This issue creates a key barrier for agent technology stockholders regarding comprehending and analyzing complexity associated with these systems specifications. Instead, such fundamental low-level infrastructure is loosely attended to in an ad-hoc fashion, and important aspects of requirements analysis are often neglected altogether. The IEEE Std 830 model is a recommended practice aimed at describing how to write better quality requirement specifications of conventional software. Knowing that agent-based computing is a natural and logical evolution of the conventional approaches to software development, we believe that the requirements phase in agent-based systems can benefit from applying the IEEE Std 830 model which results in high-quality and more accepted artifacts. This article provides a criteriabased evaluation that is derived from the software engineering body of knowledge guide to assessing the adoption degree of agent-oriented methodologies to software requirements standards. Then, it proposes a model-driven approach to restructuring and extending the IEEE Std 830-2009 standard model to specify requirements of agent-based systems. To evaluate the applicability and usefulness of the updated model, we design a research study that allows practicing the model with simple real-world problem scenarios and conducting a summative survey to solicit feedback on the model usages.
Social Media is used by many as a source of information for current world events, followed by publicly sharing their sentiment about these events. However, when the shared information is not trustworthy and receives a large number of interactions, it alters the public's perception of authentic and false information, particularly when the origin of these stories comes from malicious sources. Over the past decade, there has been an influx of users on the Twitter social network, many of them automated bot accounts with the objective of participating in misinformation campaigns that heavily influence user susceptibility to fake information. This can affect public opinion on real-life matters, as previously seen in the 2020 presidential elections and the current COVID-19 epidemic, both plagued with misinformation. In this paper, we propose an agent-based social simulation environment that utilizes the social network Twitter, with the objective of evaluating how the beliefs of agents representing regular Twitter users can be influenced by malicious users scattered throughout Twitter with the sole purpose of spreading misinformation. We applied two scenarios to compare how these regular agents behave in the Twitter network, with and without malicious agents, to study how much influence malicious agents have on the general susceptibility of the regular users. To achieve this, we implemented a belief value system to measure how impressionable an agent is when encountering misinformation and how its behavior gets affected. The results indicated similar outcomes in the two scenarios as the affected belief value changed for these regular agents, exhibiting belief in the misinformation. Although the change in belief value occurred slowly, it had a profound effect when the malicious agents were present, as many more regular agents started believing in misinformation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.