The paper describes and discusses processes needed for human emotional behaviour simulation, in particular, emotion incorporation into rational thinking, as well as presents corresponding agent architecture. Such system would enable various application fields, perhaps one of the most important being enhancing smart devices with emotions. Decreasing frequency of social contact has become an urgent issue, particularly among young people. Emotional and social intelligence are however highly desired set of skills which is impossible to develop without interacting with others. Although this problem has been acknowledged, and there are some efforts to facilitate social contact, e.g., by augmented virtual reality games, that is still not enough. There is a need to develop environment that would allow learning exactly social and emotional skills. This on-going research aims at developing intelligent agents that are able to express and incorporate affects into rational processes.

Collaborative learning is a process that involves a group of peers collaborating with the aim to acquire new knowledge or skills. Collaborative learning environment enables such interactions by means of ICT. The paper focuses on affective collaborative learning environments, i.e., collaborative learning environments that are additionally aware of user’s emotions and moods. Based on the analysis of existing research, a general architecture of an affective collaborative learning environment has been proposed in the paper and the main challenges for developing such an environment have been identified, namely, nonintrusive and safe detection of user’s emotions, the adaptation of tutoring strategies, as well as modelling of artificial peers. This study can be considered the first step for the development of the collaborative learning environment that takes into account various affective aspects during the collaborative learning process.

To distinguish individuals with dangerous abnormal behaviours from the crowd, human characteristics (e.g., speed and direction of motion, interaction with other people), crowd characteristics (such as flow and density), space available to individuals, etc. must be considered. The paper proposes an approach that considers individual and crowd metrics to determine anomaly. An individual’s abnormal behaviour alone cannot indicate behaviour, which can be threatening toward other individuals, as this behaviour can also be triggered by positive emotions or events. To avoid individuals whose abnormal behaviour is potentially unrelated to aggression and is not environmentally dangerous, it is suggested to use emotional state of individuals. The aim of the proposed approach is to automate video surveillance systems by enabling them to automatically detect potentially dangerous situations.

Affective computing is a growing field of artificial intelligence. It focuses on models and strategies for detecting, obtaining, and expressing various affective states, including emotions, moods, and personality related attributes. The techniques and models developed in affective computing are applicable to various affective contexts, including Ambient Assisted Living. One of the hypotheses for the origin of emotion is that the primary purpose was to regulate social interactions. Since one of the crucial characteristics of Ambient Assisted Living systems is supporting social contact, it is unthinkable to build such systems without considering emotions. Moreover, the emotional capacity needed for Ambient Assisted Living systems exceeds simple user emotion detection and showing emotion expressions of the system. In addition, emotion generation and emotion mapping on rational thinking and behavior of a system should be considered. The chapter discusses the need and requirements for these processes in the context of various application domains of Ambient Assisted Living, i.e., healthcare, mobility, education, and social interaction.

Well-established knowledge and information flows are one of the main organisation success factors. It is possible to address different organisational issues by analysing knowledge and information flows, so the difference between these two types of flows has to be stated. This paper proposes a way how to differentiate between information and knowledge flows conceptually, in an organisation, and in information systems.

This paper proposes a long-term adaptive distributed intelligent systems model which combines an organization theory and multi-agent paradigm—ViaBots. Currently, the need for adaptivity in autonomous intelligent systems becomes crucial due to the increase in the complexity and diversity of the tasks that autonomous robots are employed for. To deal with the design complexity of such systems within the ViaBots model, each part of the modeled system is designed as an autonomous agent and the entire model, as a multi-agent system. Based on the viable system model, which is widely used to ensure viability, (i.e., long-term autonomy of organizations), the ViaBots model defines the necessary roles a system must fulfill to be capable to adapt both to changes in its environment (like changes in the task) and changes within the system itself (like availability of a particular robot). Along with static role assignments, ViaBots propose a mechanism for role transition from one agent to another as one of the key elements of long term adaptivity. The model has been validated in a simulated environment using an example of a conveyor system. The simulated model enabled the multi-robot system to adapt to the quantity and characteristics of the available robots, as well as to the changes in the parts to be processed by the system.