Mei Si

Modeling appraisal in theory of mind reasoning

Cognitive appraisal theories, which link human emotional experience to their interpretations of events happening in the environment, are leading approaches to model emotions. In this paper, we investigate the computational modeling of appraisal in a multi-agent decision-theoretic framework using POMDP based agents. We illustrate how five key appraisal dimensions (motivational relevance, motivation congruence, accountability, control and novelty) can be derived from the processes and information required for the agents decision-making and belief maintenance. Through this illustration, we not only provide a solution for computationally modeling emotion in POMDP based agents, but also demonstrate the tight relationship between emotion and cognition. Our model of appraisal is applied to three different scenarios to illustrate its usage. We also discuss how the modeling of theory of mind (recursive beliefs about self and others) is critical for simulating social emotions.

Thespian: modeling socially normative behavior in a decision-theoretic framework

To facilitate lifelike conversations with the human players in interactive dramas, virtual characters should follow similar conversational norms as those that govern human-human conversations. In this paper, we present a model of conversational norms in a decision-theoretic framework. This model is employed in the Thespian interactive drama system. In Thespian, characters have explicit goals of following norms, in addition to their other personal goals, and use a unified decision-theoretic framework to reason about conflicts among these goals. Different characters can weigh their goals in different ways and therefore have different behaviors. We discuss the model of conversational norms in Thespian. We also present preliminary experiments on modeling various kinds of characters using this model.

Foreign language learning in immersive virtual environments

Virtual reality has long been used for training simulations in fields from medicine to welding to vehicular operation, but simulations involving more complex cognitive skills present new design challenges. Foreign language learning, for example, is increasingly vital in the global economy, but computer-assisted education is still in its early stages. Immersive virtual reality is a promising avenue for language learning as a way of dynamically creating believable scenes for conversational training and role-play simulation. Visual immersion alone, however, only provides a starting point. We suggest that the addition of social interactions and motivated engagement through narrative gameplay can lead to truly effective language learning in virtual environments. In this paper, we describe the development of a novel application for teaching Mandarin using CAVE-like VR, physical props, human actors and intelligent virtual agents, all within a semester-long multiplayer mystery game. Students travel (virtually) to China on a class field trip, which soon becomes complicated with intrigue and mystery surrounding the lost manuscript of an early Chinese literary classic. Virtual reality environments such as the Forbidden City and a Beijing teahouse provide the setting for learning language, cultural traditions, and social customs, as well as the discovery of clues through conversation in Mandarin with characters in the game.

Tell a Story About Anything

With the fast development of internet technology, people can have easy access to a massive amount of information. The goal of this project is to provide a personal assistant for helping people explore large network of information by using narrative technologies. We propose an automated narration system that takes structured information and tailors the presentation to the user. It is aimed at presenting the information as an interesting and meaningful story by taking into consideration a combination of factors including topic consistency, novelty, user interests, and the user’s preferences in exploration style. We present preliminary results of using this system for presenting information about the 2008 Summer Olympics Games, followed by discussion and future work.

Encoding theory of mind in character design for pedagogical interactive narrative

Computer aided interactive narrative allows people to participate actively in a dynamically unfolding story, by playing a character or by exerting directorial control. Because of its potential for providing interesting stories as well as allowing user interaction, interactive narrative has been recognized as a promising tool for providing both education and entertainment. This paper discusses the challenges in creating interactive narratives for pedagogical applications and how the challenges can be addressed by using agent-based technologies. We argue that a rich model of characters and in particular a Theory of Mind capacity are needed. The character architect in the Thespian framework for interactive narrative is presented as an example of how decision-theoretic agents can be used for encoding Theory of Mind and for creating pedagogical interactive narratives.

Interactive stories for health interventions

In recent years, there has been an increasing interest in exploring virtual environments and computer aided interactive stories as tools in developing health promotion and disease prevention interventions. Applications have been developed to address a range of health related conditions, including stress [1], risky behaviors [2] and post-traumatic stress disorder(PTSD) [3].

Using Multiple Storylines for Presenting Large Information Networks

Storytelling has always been an effective and intuitive method of exchanging information. In today’s world of large, open, structured data, storytelling can benefit the ways in which people explore and consume such information. In this work, we investigate this potential. In particular, methods for creating multiple interweaving storylines are explored for tying together possibly disparate veins of exploration in such large networks of information and helping maintain audience interest. This paper presents the algorithms for automatically generating interweaving storylines, followed by examples and discussions for future work.

Intertwined Storylines with Anchor Points

Narrative and dialogue are effective ways for engaging people and helping people organize and memorize information. In this work, we present an automated narration system that takes structured information from the Internet and tailors the presentation to a user using storytelling techniques. It is aimed at presenting the information as an interesting and meaningful story by taking into consideration a combination of factors ranging from topic consistency and novelty to user interests. In particular, the designer of the agent can specify a number of anchor points for the presentation, and the agent will automatically decide its strategy for covering these anchor points and emphasizing them in its presentation using multiple intertwining storylines and contrasts.

Do I need a body to encourage you? The effect of embodiment on social facilitation

Social facilitation refers to the phenomenon that the presence of others can improve ones performance in simple tasks. This work investigates whether/how we can achieve the same effect using virtual characters. In particular, we want to study the difference among using a digital character, a cognitive robot, and a real person to achieve this effect. We also want to investigate the importance of feeling being evaluated for social facilitation to take place.

Should i stop thinking about it: a computational exploration of reappraisal based emotion regulation

Agent-based simulation of peoples behaviors and minds has become increasingly popular in recent years. It provides a research platformto simulate and compare alternative psychological and social theories, as well as to create virtual characters that can interact with people or among each other to provide pedagogical or entertainment effects. In this paper, we investigate computationally modeling peoples coping behaviors and in particular in relation to depression, in decision-theoretic agents. Recent studies have suggested that depression can result fromfailed emotion regulation under limited cognitive resources. In this work, we demonstrate how reappraisal can fail under high levels of stress and limited cognitive resources using an agent-based simulation. Further, we explored the effectiveness of reappraisal under different conditions. Our experiments suggest that for people who are more likely to recall positive memories, it is more beneficial to think about the recalled events from multiple perspectives. However, for people who are more likely to recall negative memories, the better strategy is to not evaluate the recalled events against multiple goals.