Georgia Institute of Technology



Player Experience Extraction from Gameplay Video

AAAI Conferences

The ability to extract the sequence of game events for a given player's play-through has traditionally required access to the game's engine or source code. This serves as a barrier to researchers, developers, and hobbyists who might otherwise benefit from these game logs. In this paper we present two approaches to derive game logs from game video via convolutional neural networks and transfer learning. We evaluate the approaches in a Super Mario Bros. clone, Mega Man and Skyrim. Our results demonstrate our approach outperforms random forest and other transfer baselines.


Predicting Generated Story Quality with Quantitative Measures

AAAI Conferences

The ability of digital storytelling agents to evaluate their output is important for ensuring high-quality human-agent interactions. However, evaluating stories remains an open problem. Past evaluative techniques are either model-specific--- which measure features of the model but do not evaluate the generated stories ---or require direct human feedback, which is resource-intensive. We introduce a number of story features that correlate with human judgments of stories and present algorithms that can measure these features. We find this approach results in a proxy for human-subject studies for researchers evaluating story generation systems.


Automated Game Design via Conceptual Expansion

AAAI Conferences

Automated game design has remained a key challenge within the field of Game AI. In this paper, we introduce a method for recombining existing games to create new games through a process called conceptual expansion.Prior automated game design approaches have relied on hand-authored or crowd-sourced knowledge, which limits the scope and applications of such systems. Our approach instead relies on machine learning to learn approximate representations of games. Our approach recombines knowledge from these learned representations to create new games via conceptual expansion.We evaluate this approach by demonstrating the ability for the system to recreate existing games. To the best of our knowledge, this represents the first machine learning-based automated game design system.


Luo

AAAI Conferences

The ability to extract the sequence of game events for a given player's play-through has traditionally required access to the game's engine or source code. This serves as a barrier to researchers, developers, and hobbyists who might otherwise benefit from these game logs. In this paper we present two approaches to derive game logs from game video via convolutional neural networks and transfer learning. We evaluate the approaches in a Super Mario Bros. clone, Mega Man and Skyrim. Our results demonstrate our approach outperforms random forest and other transfer baselines.


Guzdial

AAAI Conferences

Automated game design has remained a key challenge within the field of Game AI. In this paper, we introduce a method for recombining existing games to create new games through a process called conceptual expansion.Prior automated game design approaches have relied on hand-authored or crowd-sourced knowledge, which limits the scope and applications of such systems. Our approach instead relies on machine learning to learn approximate representations of games. Our approach recombines knowledge from these learned representations to create new games via conceptual expansion.We evaluate this approach by demonstrating the ability for the system to recreate existing games. To the best of our knowledge, this represents the first machine learning-based automated game design system.


A Social Media Based Examination of the Effects of Counseling Recommendations after Student Deaths on College Campuses

AAAI Conferences

Student deaths on college campuses, whether brought about by a suicide or an uncontrollable incident, have serious repercussions for the mental wellbeing of students. Consequently, many campus administrators implement post-crisis intervention measures to promote student-centric mental health support. Information about these measures, which we refer to as "counseling recommendations", are often shared via electronic channels, including social media. However, the current ability to assess the effects of these recommendations on post-crisis psychological states is limited. We propose a causal analysis framework to examine the effects of these counseling recommendations after student deaths. We leverage a dataset from 174 Reddit campus communities and ~400M posts of ~350K users. Then we employ statistical modeling and natural language analysis to quantify the psychosocial shifts in behavioral, cognitive, and affective expression of grief in individuals who are "exposed" to (comment on) the counseling recommendations, compared to that in a matched control cohort. Drawing on crisis and psychology research, we find that the exposed individuals show greater grief, psycholinguistic, and social expressiveness, providing evidence of a healing response to crisis and thereby positive psychological effects of the counseling recommendations. We discuss the implications of our work in supporting post-crisis rehabilitation and intervention efforts on college campuses.


Measuring the Impact of Anxiety on Online Social Interactions

AAAI Conferences

For individuals with anxiety disorders, maladaptive feelings and negative beliefs can interfere with daily activities and importantly, social relationships. Literature has examined both direct and indirect influences of an individual's anxiety on their social interactions, however, how they co-vary temporally remains less explored. As individuals appropriate social media platforms more pervasively, can anxiety play an equally significant role in impacting one's \textit{online} social interactions? This paper seeks to answer this question. Employing a dataset of 200 Twitter users, their timeline, and social network data, we examine the relationship between manifested anxiety and various attributes of social interaction of a user by employing Granger causality and time series forecasting approaches. We observe that increases in anxiety levels of an individual result in increased future interaction with weak ties, indicating a tendency to seek support from the broader online community. We discuss how our findings provide novel insights and practical lessons around the impact of an individual's mental health state on their online social interactions.


Saha

AAAI Conferences

Student deaths on college campuses, whether brought about by a suicide or an uncontrollable incident, have serious repercussions for the mental wellbeing of students. Consequently, many campus administrators implement post-crisis intervention measures to promote student-centric mental health support. Information about these measures, which we refer to as "counseling recommendations", are often shared via electronic channels, including social media. However, the current ability to assess the effects of these recommendations on post-crisis psychological states is limited. We propose a causal analysis framework to examine the effects of these counseling recommendations after student deaths.


Ernala

AAAI Conferences

Self-disclosures of mental illnesses have been identified to yield coping and therapeutic benefits. An important construct in the self-disclosure process is the audience with whom the individual interacts and shares their experiences. Mental illness self-disclosures are increasingly happening online. However, unlike online support communities where the audience comprises sympathetic peers with similar experiences, what the discloser gains from an'invisible' audience on a general purpose, public social media platform is less understood. Focusing on a highly stigmatized mental illness, schizophrenia, this paper provides the first investigation characterizing the audience of disclosures of this condition on Twitter and how the audience's engagement impacts future disclosures. Our results are based on a rich year-long temporal analysis of the data of nearly 400 disclosers and their nearly 400 thousand audiences. First, characterizing and modeling the audience engagement temporally, we find evidence of reciprocity in the disclosure process between the discloser and their audience. Then, situating our work in the Social Penetration Theory and operationalizing the disclosure process via a measure of intimacy, an auto-regressive time series model indicates that the patterns of audience engagement and content can forecast changes in the intimacy of disclosures. We discuss the implications for building socially engaging, supportive online spaces for stigmatized mental illness disclosures.