Arnav Jhala

Towards affective camera control in games

Information about interactive virtual environments, such as games, is perceived by users through a virtual camera. While most interactive applications let users control the camera, in complex navigation tasks within 3D environments users often get frustrated with the interaction. In this paper, we propose inclusion of camera control as a vital component of affective adaptive interaction in games. We investigate the impact of camera viewpoints on psychophysiology of players through preference surveys collected from a test game. Data is collected from players of a 3D prey/predator game in which player experience is directly linked to camera settings. Computational models of discrete affective states of fun, challenge, boredom, frustration, excitement, anxiety and relaxation are built on biosignal (heart rate, blood volume pulse and skin conductance) features to predict the pairwise self-reported emotional preferences of the players. For this purpose, automatic feature selection and neuro-evolutionary preference learning are combined providing highly accurate affective models. The performance of the artificial neural network models on unseen data reveals accuracies of above 80% for the majority of discrete affective states examined. The generality of the obtained models is tested in different test-bed game environments and the use of the generated models for creating adaptive affect-driven camera control in games is discussed.

Cinematic Visual Discourse: Representation, Generation, and Evaluation

In this paper, we present the design, implementation, and evaluation of an end-to-end camera planning system called Darshak. Darshak automatically constructs cinematic narrative discourse of a given story in a 3-D virtual environment. It utilizes a hierarchical partial-order causal link (POCL) planning algorithm to generate narrative plans that contain story events and camera directives for filming them. Dramatic situation patterns, commonly used by writers of fictional narratives, are formalized as communicative plan operators that provide a basis for structuring the cinematic content of the storys visualization. The dramatic patterns are realized through abstract communicative operators that represent operations on a viewers beliefs about the story and its telling. Camera shot compositions and transitions are defined in this plan-based framework as execution primitives. Darshaks performance is evaluated through a novel user study based on techniques used to evaluate existing cognitive models of narrative comprehension. Initial study reveals significant effect of the choice of visualization strategies on measured viewer comprehension. It further shows significant effect of Darshaks choice of visualization strategy on comprehension.

Reactive planning idioms for multi-scale game AI

Many modern games provide environments in which agents perform decision making at several levels of granularity. In the domain of real-time strategy games, an effective agent must make high-level strategic decisions while simultaneously controlling individual units in battle. We advocate reactive planning as a powerful technique for building multi-scale game AI and demonstrate that it enables the specification of complex, real-time agents in a unified agent architecture. We present several idioms used to enable authoring of an agent that concurrently pursues strategic and tactical goals, and an agent for playing the real-time strategy game StarCraft that uses these design patterns.

Analyzing the impact of camera viewpoint on player psychophysiology

Information about interactive virtual environments, such as games, is perceived by users through a virtual camera. While most interactive applications let the users control the camera, in complex navigation tasks within 3D environments users often get frustrated with the interaction. In this paper, we motivate for the inclusion of camera control as a vital component of affective adaptive interaction in games and investigate the impact of camera viewpoints on psy-chophysiology of players through an evaluation game survey experiment. The statistical analysis presented demonstrates that emotional responses and physiological indexes are affected by camera settings.

Metrics for Character Believability in Interactive Narrative

The concept of character believability is often used in interactive narrative research hypothesis. In this paper we define believability metrics using perceived believability dimensions and discuss how they can be accessed. The proposed dimensions are: behavior coherence, change with experience, awareness, behavior understandability, personality, visual impact, predictability, social and emotional expressiveness.

Learning visual composition preferences from an annotated corpus generated through gameplay

This paper describes a game called Panorama, designed to facilitate data collection to study visual composition preferences. Design considerations for Panorama, implementation of composition rules, and data collection for an experiment to learn individual and collective preferences is described. Images taken through gameplay in Panorama are automatically scored for composition quality and contribute to a corpus of domain-specific virtual photographs annotated by visual features and scores. Scores in Panorama represent rules of good composition from photography textbooks. In the current version, Panorama scores photographs along balance, thirds alignment, symmetry, and spacing dimensions. Pairwise preference rankings are collected on images from this corpus through crowd-sourcing. Results are presented from data on relative pairwise rankings on the images to learn individual as well as general composition preferences over features annotated in Panorama images. This work seeks to extend the ability of AI systems to learn and reason about high-level aesthetic features of photographs that could be utilized for various procedural camera control and aesthetic layout algorithms in video games.

Rich computational model of conflict for virtual characters

Rich interactions with virtual characters in narrative-based environments can be enabled by providing characters with representation of parameters for reasoning about various types of conflict. This paper proposes a model of conflict that includes mechanics, context, and dynamics of conflict scenarios. This model extends and reconciles prior work on conflict management from various disciplines. This model complements task-oriented conflicts that are implemented in current agent architectures and seeks to motivate exploration to a new design space of possible conflict situations. This work is based on initial analysis of a corpus of conflict scenarios annotated with personality profiles and resolution strategies. This annotated corpus is made available to the community for further research on conflict.

From Mechanics to Meaning

While generative approaches to game design offer great promise, systems can only reliably generate what they can “understand,” which is often represented in a limited, implicit form in hand-crafted evaluation functions or constructive rules. Proceduralist readings, a semiformal approach for interpreting the meaning of a game based on its underlying processes and interactions in conjunction with aesthetic and cultural cues, offer a novel, systematic approach to game understanding. We formalize proceduralist argumentation as a logic program that performs static reasoning over game specifications to derive higher level meanings, as part of Gemini, a bidirectional game analysis and generation system.

Remember That Time? Telling Interesting Stories from Past Interactions

Sociability is a human trait that plays a central part in relationships over time. Today, humans are increasingly in long-term interactions with intelligent agents, which have proven most useful when they are sociable. Such sociability requires the agent to remember and appropriately refer to past interactions. A common way in which humans refer to their past interactions and collaborations is through storytelling. Such stories, often abbreviated, include a small set of interesting and extraordinary events. We propose the design, development and preliminary evaluation of a generic computational architecture for finding and retelling such interesting event sequences. Our system mines interesting interaction episodes in a corpus of prior interactions. Initial evaluation of interactions selected by the system for retelling are encouraging. A future goal of the research is to support collaborative composition of stories about prior interactions between humans and agents in a mixed-initiative framework to produce interesting retellings.

Revisiting Computational Models of Creative Storytelling Based on Imaginative Recall

Certain story generation systems consider the processes of imaginative recall and adaptation as central to human creativity in storytelling. Researchers have recently compared the output of these systems through the lens of Boden’s types of creativity [9]. This comparison highlights the contribution of predefined structures to story predictability, which influences perceived creativity. We revisit the connection between knowledge structures and story predictability, and compare Minstrel’s use of knowledge structures versus the use of Story Intention Graphs (SIGs) as the underlying case frames. Semantic information encoded in the SIG produces coherent stories and retains the imaginative recall and generalization aspect of Minstrel’s creative process. Mapping knowledge structures to SIGs enables the use of a common representation that is directly connected to surface realization. This opens up the performative aspect of creativity that does not come out in templated text outputs.