Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.Comment: PhD thesi

Building a Positive Teacher and Student Identity in the Chinese DLI Context

This portfolio contains a selection of the author’s research interests and learning achievements while in the Master Second Language Teaching (MSLT) program at Utah State University (USU). It represents the author’s investigation, observation, and reflection as an MSLT student and as a Chinese teacher and coordinator for the Chinese Dual Language Immersion (DLI) program in Utah. The first section of the portfolio contains the author’s teaching perspectives including professional environment, teaching philosophy statement, and professional development through teaching observations. These perspectives represent her professional growth over the years in the field of Chinese teaching in the DLI setting. The second section consists of two research perspectives. They demonstrate the author’s research interests that aligned with her teaching perspectives as a Chinese DLI practitioner. Lastly, an annotated bibliography is included with further discussion of pedagogical implications for the Chinese DLI classroom

Using Multimodal Analysis to Investigate the Role of the Interpreter

Recent research in Interpreting Studies has favoured the argument that, in practice, the interpreter plays an active role, rather than the prescribed role stipulated in professional codes of conduct. Cutting-edge studies utilising multimodal research methods have taken a more comprehensive approach to investigating this argument, searching for evidence of the interpreter’s active involvement not only through textual analysis, but also by examining a range of non-verbal communicative means. Studies using multimodal analysis, such as those by Pasquandrea (2011) and Davitti (2012), have succeeded in offering new insights into the interpreter’s role in interaction. This research presents further investigation into the interpreter’s role through multimodal analysis by focusing on the use of gesture movements, gaze and body orientation in interpreter-mediated communication; it also looks at the impact of the state of knowledge asymmetry on the interpreter’s role. This thesis presents findings from six simulated face-to-face dialogue interpreting cases featuring three different groups of participants and interpreters representing different interpreting settings (e.g. parent-teacher meeting, business meeting, doctor-patient meeting, etc.). By adapting a multimodal approach, findings of this study (a) contribute to our understanding of the active role of the interpreter in Interpreting Studies by exploring new insights from a multimodal approach, and (b) offer new empirical findings from interpreter-mediated interactions to the technical analysis of multimodal communication

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Deep Emotion Recognition in Textual Conversations: A Survey

While Emotion Recognition in Conversations (ERC) has seen a tremendous advancement in the last few years, new applications and implementation scenarios present novel challenges and opportunities. These range from leveraging the conversational context, speaker and emotion dynamics modelling, to interpreting common sense expressions, informal language and sarcasm, addressing challenges of real time ERC, recognizing emotion causes, different taxonomies across datasets, multilingual ERC to interpretability. This survey starts by introducing ERC, elaborating on the challenges and opportunities pertaining to this task. It proceeds with a description of the emotion taxonomies and a variety of ERC benchmark datasets employing such taxonomies. This is followed by descriptions of the most prominent works in ERC with explanations of the Deep Learning architectures employed. Then, it provides advisable ERC practices towards better frameworks, elaborating on methods to deal with subjectivity in annotations and modelling and methods to deal with the typically unbalanced ERC datasets. Finally, it presents systematic review tables comparing several works regarding the methods used and their performance. The survey highlights the advantage of leveraging techniques to address unbalanced data, the exploration of mixed emotions and the benefits of incorporating annotation subjectivity in the learning phase