Когнитивни процеси, емоции и интелигентни интерфејси

Студијата презентира истражувања од повеќе научни дисциплини, како вештачка интелигенција, невронауки, психологија, лингвистика и филозофија, кои имаат потенцијал за креирање на интелигентни антропоморфни агенти и интерактивни технологии. Се разгледуваат системите од симболичка и конекционистичка вештачка интелигенција за моделирање на човековите когнитивни процеси, мислење, донесување одлуки, меморија и учење. Се анализираат моделите во вештачка интелигенција и роботика кои користат емоции како механизам за контрола на остварување на целите на роботот, како реакција на одредени ситуации, за одржување на процесот на социјална интеракција и за создавање на поуверливи антропормфни агенти. Презентираните интердисциплинарни методологии и концепти се мотивација за создавање на анимирани агенти кои користат говор, гестови, интонација и други невербални модалитети при конверзација со корисниците во интелигентните интерфејси

The IONWI algorithm: learning when and when not to interrupt

One of the key issues for an interface agent to succeed at assisting a user is learning when and when not to interrupt him to provide him assistance. Unwanted or irrelevant interruptions hinder the user’s work and make him dislike the agent because it is being intrusive and impolite. The IONWI algorithm enables interface agents to learn a user’s preferences and priorities regarding interruptions. The resulting user profile is then used by the agent to personalize the modality of the assistance, that is, assisting the user with an interruption or without an interruption depending on the user’s context. Experiments were conducted in the calendar management domain, obtaining promising results.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1Red de Universidades con Carreras en Informática (RedUNCI

ICA Based EEG Energy Spectrum for Detection of Negative Emotion by EEG

Master'sMASTER OF SCIENC

Real-Time Affective Support to Promote Learner’s Engagement

abstract: Research has shown that the learning processes can be enriched and enhanced with the presence of affective interventions. The goal of this dissertation was to design, implement, and evaluate an affective agent that provides affective support in real-time in order to enrich the student’s learning experience and performance by inducing and/or maintaining a productive learning path. This work combined research and best practices from affective computing, intelligent tutoring systems, and educational technology to address the design and implementation of an affective agent and corresponding pedagogical interventions. It included the incorporation of the affective agent into an Exploratory Learning Environment (ELE) adapted for this research. A gendered, three-dimensional, animated, human-like character accompanied by text- and speech-based dialogue visually represented the proposed affective agent. The agent’s pedagogical interventions considered inputs from the ELE (interface, model building, and performance events) and from the user (emotional and cognitive events). The user’s emotional events captured by biometric sensors and processed by a decision-level fusion algorithm for a multimodal system in combination with the events from the ELE informed the production-rule-based behavior engine to define and trigger pedagogical interventions. The pedagogical interventions were focused on affective dimensions and occurred in the form of affective dialogue prompts and animations. An experiment was conducted to assess the impact of the affective agent, Hope, on the student’s learning experience and performance. In terms of the student’s learning experience, the effect of the agent was analyzed in four components: perception of the instructional material, perception of the usefulness of the agent, ELE usability, and the affective responses from the agent triggered by the student’s affective states. Additionally, in terms of the student’s performance, the effect of the agent was analyzed in five components: tasks completed, time spent solving a task, planning time while solving a task, usage of the provided help, and attempts to successfully complete a task. The findings from the experiment did not provide the anticipated results related to the effect of the agent; however, the results provided insights to improve diverse components in the design of affective agents as well as for the design of the behavior engines and algorithms to detect, represent, and handle affective information.Dissertation/ThesisDoctoral Dissertation Computer Science 201

THE APPLICATION OF EMBODIED CONVERSATIONAL AGENTS FOR MENTORING AFRICAN AMERICAN STEM DOCTORAL STUDENTS

This dissertation presents the design, development and short-term evaluation of an embodied conversational agent designed to mentor human users. An embodied conversational agent (ECA) was created and programmed to mentor African American computer science majors on their decision to pursue graduate study in computing. Before constructing the ECA, previous research in the fields of embodied conversational agents, relational agents, mentorship, telementorship and successful mentoring programs and practices for African American graduate students were reviewed. A survey used to find areas of interest of the sample population. Experts were then interviewed to collect information on those areas of interest and a dialogue for the ECA was constructed based on the interview\u27s transcripts. A between-group, mixed method experiment was conducted with 37 African American male undergraduate computer science majors where one group used the ECA mentor while the other group pursued mentoring advice from a human mentor. Results showed no significant difference between the ECA and human mentor when dealing with career mentoring functions. However, the human mentor was significantly better than the ECA mentor when addressing psychosocial mentoring functions

Designing Embodied Interactive Software Agents for E-Learning: Principles, Components, and Roles

Embodied interactive software agents are complex autonomous, adaptive, and social software systems with a digital embodiment that enables them to act on and react to other entities (users, objects, and other agents) in their environment through bodily actions, which include the use of verbal and non-verbal communicative behaviors in face-to-face interactions with the user. These agents have been developed for various roles in different application domains, in which they perform tasks that have been assigned to them by their developers or delegated to them by their users or by other agents. In computer-assisted learning, embodied interactive pedagogical software agents have the general task to promote human learning by working with students (and other agents) in computer-based learning environments, among them e-learning platforms based on Internet technologies, such as the Virtual Linguistics Campus (www.linguistics-online.com). In these environments, pedagogical agents provide contextualized, qualified, personalized, and timely assistance, cooperation, instruction, motivation, and services for both individual learners and groups of learners. This thesis develops a comprehensive, multidisciplinary, and user-oriented view of the design of embodied interactive pedagogical software agents, which integrates theoretical and practical insights from various academic and other fields. The research intends to contribute to the scientific understanding of issues, methods, theories, and technologies that are involved in the design, implementation, and evaluation of embodied interactive software agents for different roles in e-learning and other areas. For developers, the thesis provides sixteen basic principles (Added Value, Perceptible Qualities, Balanced Design, Coherence, Consistency, Completeness, Comprehensibility, Individuality, Variability, Communicative Ability, Modularity, Teamwork, Participatory Design, Role Awareness, Cultural Awareness, and Relationship Building) plus a large number of specific guidelines for the design of embodied interactive software agents and their components. Furthermore, it offers critical reviews of theories, concepts, approaches, and technologies from different areas and disciplines that are relevant to agent design. Finally, it discusses three pedagogical agent roles (virtual native speaker, coach, and peer) in the scenario of the linguistic fieldwork classes on the Virtual Linguistics Campus and presents detailed considerations for the design of an agent for one of these roles (the virtual native speaker)

Understanding the relationship between justice and team goal commitment in virtual project teams: The role of para-social presence as a moderating context

Master'sMASTER OF SCIENC

Answering questions about archived, annotated meetings

Retrieving information from archived meetings is a new domain of information retrieval that has received increasing attention in the past few years. Search in spontaneous spoken conversations has been recognized as more difficult than text-based document retrieval because meeting discussions contain two levels of information: the content itself, i.e. what topics are discussed, but also the argumentation process, i.e. what conflicts are resolved and what decisions are made. To capture the richness of information in meetings, current research focuses on recording meetings in Smart-Rooms, transcribing meeting discussion into text and annotating discussion with semantic higher-level structures to allow for efficient access to the data. However, it is not yet clear what type of user interface is best suited for searching and browsing such archived, annotated meetings. Content-based retrieval with keyword search is too naive and does not take into account the semantic annotations on the data. The objective of this thesis is to assess the feasibility and usefulness of a natural language interface to meeting archives that allows users to ask complex questions about meetings and retrieve episodes of meeting discussions based on semantic annotations. The particular issues that we address are: the need of argumentative annotation to answer questions about meetings; the linguistic and domain-specific natural language understanding techniques required to interpret such questions; and the use of visual overviews of meeting annotations to guide users in formulating questions. To meet the outlined objectives, we have annotated meetings with argumentative structure and built a prototype of a natural language understanding engine that interprets questions based on those annotations. Further, we have performed two sets of user experiments to study what questions users ask when faced with a natural language interface to annotated meeting archives. For this, we used a simulation method called Wizard of Oz, to enable users to express questions in their own terms without being influenced by limitations in speech recognition technology. Our experimental results show that technically it is feasible to annotate meetings and implement a deep-linguistic NLU engine for questions about meetings, but in practice users do not consistently take advantage of these features. Instead they often search for keywords in meetings. When visual overviews of the available annotations are provided, users refer to those annotations in their questions, but the complexity of questions remains simple. Users search with a breadth-first approach, asking questions in sequence instead of a single complex question. We conclude that natural language interfaces to meeting archives are useful, but that more experimental work is needed to find ways to incent users to take advantage of the expressive power of natural language when asking questions about meetings