Believability has been a perennial goal for the intelligent virtual agent community. One important aspect of believability largely consists in demonstrating autonomous behavior, consistent with the agent’s personality and motivational state, as well as the world conditions. Autonomy, on behalf of the agent, implies the existence of an internal structure and mechanism that allows the agent to have its own needs and interests, based on which the agent will dynamically select and generate goals that will in turn lead to self-determined behavior. Intrinsic motivation allows the agent to function and demonstrate behavior, even when no external stimulus is present, due to the constant change of its internal emotional and physiological state. The concept of motivation has already been investigated by research works on intelligent agents, trying to achieve autonomy. The current work presents an architecture and model to represent and manage internal driving factors in intelligent virtual agents, using the concept of motivations. Based on Maslow and Alderfer’s bio-psychological needs theories, we present a motivational approach to represent human needs and produce emergent behavior through motivation synthesis. Particular attention is given to basic, physiological level needs, which are the basis of behavior and can produce tendency to action even when there is no other interaction with the environment.

The paper presents a generic framework for designing and implementing multiuser virtual exhibitions that adapt visitors’ preferences and goals and foster the emergence of communities with common interests. A user-oriented platform for designing and executing virtual exhibitions has been presented in which implicit generation and adjustment of user profile allows exhibition to dynamically adapt content presentation to users’ interest and preferences. http://dx.doi.org/10.14429/djlit.33.4604

Recently, the rapid growth of technology and the increased teleworking due to the COVID-19 outbreak have motivated cyber attackers to advance their skills and develop new sophisticated methods, e.g., Advanced Persistent Threat (APT) attacks, to leverage their cybercriminal capabilities. They compromise interconnected Critical Information Infrastructures (CIIs) (e.g., Supervisory Control and Data Acquisition (SCADA) systems) by exploiting a series of vulnerabilities and launching multiple attacks. In this context, industry players need to increase their knowledge on the security of the CIs they operate and further explore the technical aspects of cyber-attacks, e.g., attack’s course, vulnerabilities exploitability, attacker’s behavior, and location. Several research papers address vulnerability chain discovery techniques. Nevertheless, most of them do not focus on developing attack graphs based on incident analysis. This paper proposes an attack simulation and evidence chains generation model which computes all possible attack paths associated with specific, confirmed security events. The model considers various attack patterns through simulation experiments to estimate how an attacker has moved inside an organization to perform an intrusion. It analyzes artifacts, e.g., Indicators of Compomise (IoCs), and any other incident-related information from various sources, e.g., log files, which are evidence of cyber-attacks on a system or network.