Affect and believability in game characters:a review of the use of affective computing in games

Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions

Emergent Story Generation: Lessons from Improvisational Theater

An emergent approach to story generation by computer is characterized by a lack of predetermined plot and a focus on character interaction forming the material for stories. A potential problem is that no interesting story emerges. However, improvisational theater shows that – at least for human actors – a predetermined plot is not necessary for creating a compelling story. There are some principles that make a successful piece of improvisational theater more than a random interaction, and these principles may inform the type of computational processes that an emergent narrative architecture draws from. We therefore discuss some of these principles, and show how these are explicitly or implicitly used in story generation and interactive storytelling research. Finally we draw lessons from these principles and ask attention for two techniques that have been little investigated: believably incorporating directives, and late commitment

Adapting Progress Feedback and Emotional Support to Learner Personality

Peer reviewedPostprin

Simulating emotional reactions in medical dramas

Presenting information on emotionally charged topics is a delicate task: if bare facts alone are conveyed, there is a risk of boring the audience, or coming across as cold and unfeeling; on the other hand, emotional presentation can be appropriate when carefully handled, but when overdone or mishandled risks being perceived as patronising or in poor taste. When Natural Language Generation (NLG) systems present emotionally charged information linguistically, by generating scripts for embodied agents, emotional/affective aspects cannot be ignored. It is important to ensure that viewers consider the presentation appropriate and sympathetic. We are investigating the role of affect in communicating medical information in the context of an NLG system that generates short medical dramas enacted by embodied agents. The dramas have both an informational and an educational purpose in that they help patients review their medical histories whilst receiving explanations of less familiar medical terms and demonstrations of their usage. The dramas are also personalised since they are generated from the patients' own medical records. We view generation of natural/appropriate emotional language as a way to engage and maintain the viewers' attention. For our medical setting, we hypothesize that viewers will consider dialogues more natural when they have an enthusiastic and sympathetic emotional tone. Our second hypothesis proposes that such dialogues are also better for engaging the viewers' attention. As well as describing our NLG system for generating natural emotional language in medical dialogue, we present a pilot study with which we investigate our two hypotheses. Our results were not quite as unequivocal as we had hoped. Firstly, our participants did notice whether a character sympathised with the patient and was enthusiastic. This did not, however, lead them to judge such a character as behaving more naturally or the dialogue as being more engaging. However, when pooling data from our two conditions, dialogues with versus dialogues without emotionally appropriate language use, we discovered, somewhat surprisingly, that participants did consider a dialogue more engaging if they believed that the characters showed sympathy towards the patient, were not cold and unfeeling, and were natural (true for the female agent only)

Leveraging Generative Agents: Autonomous AI with Simulated Personas for Interactive Simulacra and Collaborative Research

The advent of large language models (LLMs) and AI learning have fundamentally reshaped the research landscape, paving the way for novel problem-solving approaches. This paper introduces a unique framework that leverages the capabilities of autonomous AI agents with simulated personas to drive collaborative research in groundbreaking ways. Inspired by a recent study of autonomous agents mirroring human behavior, this concept encourages the use of a cadre of AI agents, each possessing specialized expertise for collective endeavors. By replicating human diversity in teamwork, this approach targets complex and hitherto unsolvable issues. The key to this strategy is persona and emotional simulation, enabling these AI agents to facilitate cross- disciplinary and interdisciplinary research within a decentered author model, and providing innovative solutions to wicked problems. Expertise can be drawn upon from disparate fields, including STEM, business, education, arts and humanities, and more. Enhanced by the advancements in AI research, specifically with LLMs like OpenAI\u27s ChatGPT 3.5 and 4, this model offers profound potential to nurture research culture within universities by identifying barriers and proposing strategies to surmount them, drawing from international models for inspiration. This proposed decentered collaborative research model, despite constraints, holds immense promise in reinventing the research paradigm

Overcoming foreign language anxiety in an emotionally intelligent tutoring system

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning. Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct and less regulated by the learner, but it is uncomfortable and requires the learner to be in the lab. Employing self-reports is scalable because it is easy to administer in the lab and online. However, it interrupts the learning flow, and people sometimes respond inaccurately. Using sensor-free human behavioral metrics is a scalable and practical measurement because it is feasible online or in class with minimum adjustments. To overcome FLA, researchers have studied the use of robots, games, or intelligent tutoring systems (ITS). Within these technologies, they applied soothing music, difficulty reduction, or storytelling. These methods lessened FLA but had limitations such as distracting the learner, not improving performance, and producing cognitive overload. Using an animated agent that provides motivational supportive feedback could reduce FLA and increase learning. It is necessary to measure FLA effectively with minimal interruption and then successfully reduce it. In the context of an e-learning system, I investigated ways to detect FLA using sensor-free human behavioral metrics. This scalable and practical method allows us to recognize FLA without being obtrusive. To reduce FLA, I studied applying emotionally adaptive feedback that offers motivational supportive feedback by an animated agent

Project PRAIA: Pedagogical Rational and Affective Intelligent Agents

ISBN: 978-85-7669-245-4International audienceno abstrac