Agent-based simulation of the learning dissemination on a Project-Based Learning context considering the human aspects

This work presents an agent-based simulation (ABS) of the active learning process in an Electrical Engineering course. In order to generate input data to the simulation, an active learning methodology developed especially for part-time degree courses, called Project-Based Learning Agile (PBLA), has been proposed and implemented at the Regional University of Blumenau (FURB), Brazil. Through the analysis of survey responses obtained over five consecutive semesters, using partial least squares path modeling (PLS-PM), it was possible to generate data parameters to use as an input in a hybrid kind of agent-based simulation known as PLS agent. The simulation of the scenario suggests that the learning occur faster when the student has higher levels of humanist's aspects as self-esteem, self-realization and cooperation.Comment: 8 pages, 6 figures, minor correction

Adapting Progress Feedback and Emotional Support to Learner Personality

Peer reviewedPostprin

Software agents in music and sound art research/creative work: Current state and a possible direction

Composers, musicians and computer scientists have begun to use software-based agents to create music and sound art in both linear and non-linear (non-predetermined form and/or content) idioms, with some robust approaches now drawing on various disciplines. This paper surveys recent work: agent technology is first introduced, a theoretical framework for its use in creating music/sound art works put forward, and an overview of common approaches then given. Identifying areas of neglect in recent research, a possible direction for further work is then briefly explored. Finally, a vision for a new hybrid model that integrates non-linear, generative, conversational and affective perspectives on interactivity is proposed

From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)

This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness

Patient Relationship Management (PRM) and AI: The role of Affective Computing

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceArtificial Intelligence (AI) has been praised as the next big thing in the computing revolution, and it's been touted as a game-changer in a variety of fields, including healthcare. The increasing popularity of this technology is driving early adoption and leading to a lack of consideration of the patient perspective in its use, bringing new sources of distrust that come from the absence of human attributes. This study aims to address this problem by presenting a strategy in the area of affective computing that will combat this absence of empathy experienced by the patient during a medical process. To reach this goal, a Design Science Research Methodology will be followed. A preliminary literature study had already been completed, and the research topic and objectives had been established. In addition, to apply the artifact developments, a bot will be built and evaluated by a set of users. The increased awareness of these AI systems will, expectedly, stimulate their use. By adding new research into the affective computing field, it is also expected to contribute to the digital healthcare evolution and to encourage further scientific progress in this area

Challenges for an Ontology of Artificial Intelligence

Of primary importance in formulating a response to the increasing prevalence and power of artificial intelligence (AI) applications in society are questions of ontology. Questions such as: What “are” these systems? How are they to be regarded? How does an algorithm come to be regarded as an agent? We discuss three factors which hinder discussion and obscure attempts to form a clear ontology of AI: (1) the various and evolving definitions of AI, (2) the tendency for pre-existing technologies to be assimilated and regarded as “normal,” and (3) the tendency of human beings to anthropomorphize. This list is not intended as exhaustive, nor is it seen to preclude entirely a clear ontology, however, these challenges are a necessary set of topics for consideration. Each of these factors is seen to present a 'moving target' for discussion, which poses a challenge for both technical specialists and non-practitioners of AI systems development (e.g., philosophers and theologians) to speak meaningfully given that the corpus of AI structures and capabilities evolves at a rapid pace. Finally, we present avenues for moving forward, including opportunities for collaborative synthesis for scholars in philosophy and science

Modeling Memes: A Memetic View of Affordance Learning

This research employed systems social science inquiry to build a synthesis model that would be useful for modeling meme evolution. First, a formal definition of memes was proposed that balanced both ontological adequacy and empirical observability. Based on this definition, a systems model for meme evolution was synthesized from Shannon Information Theory and elements of Bandura\u27s Social Cognitive Learning Theory. Research in perception, social psychology, learning, and communication were incorporated to explain the cognitive and environmental processes guiding meme evolution. By extending the PMFServ cognitive architecture, socio-cognitive agents were created who could simulate social learning of Gibson affordances. The PMFServ agent based model was used to examine two scenarios: a simulation to test for potential memes inside the Stanford Prison Experiment and a simulation of pro-US and anti-US meme competition within the fictional Hamariyah Iraqi village. The Stanford Prison Experiment simulation was designed, calibrated, and tested using the original Stanford Prison Experiment archival data. This scenario was used to study potential memes within a real-life context. The Stanford Prison Experiment simulation was complemented by internal and external validity testing. The Hamariyah Iraqi village was used to analyze meme competition in a fictional village based upon US Marine Corps human terrain data. This simulation demonstrated how the implemented system can infer the personality traits and contextual factors that cause certain agents to adopt pro-US or anti-US memes, using Gaussian mixture clustering analysis and cross-cluster analysis. Finally, this research identified significant gaps in empirical science with respect to studying memes. These roadblocks and their potential solutions are explored in the conclusions of this work