Mara Pudane

Affective states influence how humans make decisions, what actions they choose to perform, and how group structure and relationships among individuals develop over time. The studies on emotion modelling in computer systems have developed in number and quality over the past 30 years. However, there are no models that allow to fully simulate human affective interaction. The study in the Thesis integrates existing models and interaction mechanisms found in psychology research, using multi-agent systems to implement affective interaction mechanisms.

Promocijas darba pētījumā integrēti cilvēku grupas imitācijas modeļi un psiholoģijā identificētie komunikācijas mehānismi, komunikācijas realizēšanai izmantojot daudzaģentu sistēmas.

The paper aims to model and simulate emotions' rational and social effects for a fully affective human group behaviour simulation model. The approach used in the paper intertwines the concepts used in agent-based modelling and multi-agent systems to generate and observe emerging macro patterns in the behaviour of a simulated human group. The paper demonstrates how such an approach can be used to create macro-level patterns characteristic of small human groups in the scenario of the UNO board game.

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.

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.

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.

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.