In menstrual cycle tracking apps (MCTAs), AI-based predictions and insights have become increasingly popular. These features enable users to receive personalized information about their bodies and mental states. However, there is currently little research on how these predictive AI features and explanations affect users’ lived experiences. This paper examines human-AI entanglement in MCTAs through 14 semi-structured user interviews and a group autoethnography. These methods uncover the processes leading to this phenomenon. Our results reveal that: (1) users understand their lived experiences in light of AI predictions, although these predictions can be faulty due to imperfect logging practices, (2) the user interface features and AI explanations do not support awareness or critical engagement with this entanglement and meaning-making, and (3) non-normative MCTA users report a sense of isolation in this entangled interaction. Based on our findings, we propose design implications for predictive AI features and explanations.

How do product teams evaluate LLM-powered products? As organizations integrate large language models (LLMs) into digital products, their unpredictable nature makes traditional evaluation approaches inadequate, yet little is known about how practitioners navigate this challenge. Through interviews with nineteen practitioners across diverse sectors, we identify ten evaluation practices spanning informal ‘vibe checks’ to organizational meta-work. Beyond confirming four documented challenges, we introduce a novel fifth we call the results-actionability gap, in which practitioners gather evaluation data but cannot translate findings into concrete improvements. Drawing on patterns from successful teams, we contribute strategies to bridge this gap, supporting practitioners’ formalization journey from ad-hoc interpretive practices (e.g., vibe checks) toward systematic evaluation. Our analysis suggests these interpretive practices are necessary adaptations to LLM characteristics rather than methodological failures. For HCI researchers, this presents a research opportunity to support practitioners in systematizing emerging practices rather than developing new evaluation frameworks.

This paper critically examines the machine learning (ML) modeling of humans in three case studies of well-being technologies.Through a critical technical approach, it examines how these apps were experienced in daily life (technology in use) to surface breakdowns and to identify the assumptions about the "human" body entrenched in the ML models (technology design).To address these issues, this paper applies agential realism to decenter foundational assumptions, such as body regularity and health/illness binaries, and speculates more inclusive design and ML modeling paths that acknowledge irregularity, human-system entanglements, and uncertain transitions.This work is among the first to explore the implications of decentering theories in computational modeling of human bodies and well-being, offering insights for more inclusive technologies and speculations toward posthuman-centered ML modeling.

With the emerging Internet of Things (IoT) and Vehicle-Road-Cloud Integration System (VRCIS) technologies, coordinating Connected and Automated Vehicles (CAVs) and traffic signal is becoming a practical solution to further enhance traffic efficiency. However, current studies still have limitations. Firstly, there is a domain mismatch between CAV trajectory planning (temporal domain) and signal optimization (spatial domain). This mismatch requires separate modeling of trajectory planning and signal optimization, which greatly reduces global optimality. Secondly, previous studies are not applicable to actual mixed traffic environment, since they mostly simplify Human-driven Vehicle’s (HV) behavior without considering queuing and stop-and-go maneuvers. Therefore, we propose a novel Multi-Vehicles and Signal Cooperation (MVSC) planner to solve the limitations via following designs. (i) Joint optimization is achieved via formulating in the spatial domain, unifying CAV’s planning domain with traffic signal optimizing domain. (ii) A parallel algorithm is designed for the adaptation to numbers of CAVs. This algorithm is based on Alternating Direction Method of Multipliers (ADMM), making full use of IoT and VRCIS. (iii) HV queuing and stop-and-go behaviors are considered in our modeling. Simulation results show that the proposed MVSC planner can enhance efficiency and ecology by 23.60% and 15.63%. At CAV’s penetration rate of 40% and V/C ratio of 0.75, the proposed planner shows its full potential in performance enhancement. The average computation time of parallel computing approach is only within 10 milliseconds, which confirms the real-time implementation capability.

Educational games have emerged as potent tools for helping students understand complex concepts and are now ubiquitous in global classrooms, amassing vast data. However, there is a notable gap in research concerning the effective visualization of this data to serve two key functions: (a) guiding students in reflecting upon their game-based learning and (b) aiding them in analyzing peer strategies. In this paper, we engage educators, students, and researchers as essential stakeholders. Taking a Design-Based Research (DBR) approach, we incorporate UX design methods to develop an innovative visualization system that helps players learn through gaining insights from their own and peers’ gameplay and strategies.

Ongoing efforts to turn Machine Learning (ML) into a design material have encountered limited success. This paper examines the burgeoning area of AI art to understand how artists incorporate ML in their creative work. Drawing upon related HCI theories, we investigate how artists create ambiguity by analyzing nine AI artworks that use computer vision and image synthesis. Our analysis shows that, in addition to the established types of ambiguity, artists worked closely with the ML process (dataset curation, model training, and application) and developed various techniques to evoke the ambiguity of processes. Our finding indicates that the current conceptualization of ML as a design material needs to reframe the ML process as design elements, instead of technical details. Finally, this paper offers reflections on commonly held assumptions in HCI about ML uncertainty, dependability, and explainability, and advocates to supplement the artifact-centered design perspective of ML with a process-centered one.

Text-to-image models such as DALL-E, Stable Diffusion, and Midjourney have seen a boom in development and adoption in both commercial and hobbyist spaces. This paper is a theoretical analysis aimed at informing the development of games that help improve critical literacy around text-to-image models. It asks: what assumptions and perspectives do text-to-image models have on visual creativity, and how do we bring that out through games? We propose a theory to differentiate between seeing an image through the expression of color, shapes and lines, and seeing an image through the recognition of concepts and ideas. These two ways of seeing are two different ways of orienting the player/user to their visual creativity. While traditional painting mechanics emphasize the former, text-to-image interfaces emphasize the latter. We deploy this perspective to study games with traditional painting interactions and games with text-to-image interactions. This paper hopes to contribute to design both broadly for games about visual creativity, and narrowly for gameplay with text-to-image models — specifically, how the latter fosters a different type of visual creativity than traditional painting interactions.

This research proposes a coordination method for multi-connected and automated vehicles (CAVs) and traffic signal. It aims at reducing stop-and-go maneuvers of CAVs and enhancing traffic efficiency. The proposed method has the following highlights: i) Adaptive to actual CAV and humandriven vehicle (HV) mixed traffic; ii) Jointly optimization of both vehicle trajectory and signal timing via formulating in the spatial domain; iii) Parallel distributed computing. Simulation test results demonstrate that the proposed coordinated control significantly outperforms the benchmark method. The proposed method reduces the average travel delay by 29.56%, enhances fuel efficiency by 18.87%, and reduces stop count by 87.10%. The proposed parallel distributed computing algorithm ensures a computation time basically within 10 milliseconds. It indicates that the proposed method is ready for real-time large-scale implementation.

Adaptation, or ability and willingness to consider an alternative approach, is a critical component of learning through reflection, especially in educational games, where there are often multiple avenues to success. As a domain, educational games have shown increased interest in using retrospective visualizations to promote and support reflection. Such visualizations, which can facilitate comparison with peer data, may also have an impact on adaptation in educational games. This has, however, not been empirically examined within the domain. In this work, we examine how comparison with other players’ data influenced adaptation, a part of reflection, in the context of a game that teaches parallel programming. Our results indicate that comparison with peers does significantly impact willingness to try a different approach, but suggest that there may also be other ways. We discuss what these results mean for future use of retrospective visualizations in educational games and present opportunities for future work.

Monte Carlo Tree Search (MCTS) employs Multi-Armed Bandit (MAB) techniques to direct the policy for child node selection during tree construction. Typical MCTS implementations have relied on the Upper Confidence Bounds for Trees (UCT) strategy, which leverages a specific variant of the general Upper Confidence Bounds (UCB) approach. The success of such strategies relies heavily on the proper tuning of the UCB C parameter to guide exploration effectively. This paper examines (1) the advantages of per-arm tuning of C, (2) the potential for a parameter-less UCB variant called UCBT to provide opportunities for automatic derivation of effective C values without prior tuning in a strategy called Poly-UCB1, and (3) the application of both of these concepts toward operational tuning of C during MCTS node expansion and tree construction in a strategy called UCB-Multi. We evaluate our approach in three turn-based, adversarial board games.

Identifying explainable player strategies and decision patterns that give insights into player behavior is one of the most difficult tasks for game analytics, yet yields great informative potential for various purposes. Industrial stakeholders can capture player experience and infer issues or feedback on design, content, and game balancing - while players themselves might want to leverage this technique to contrast their style of play to other players, fostering self-regulated learning. On top of that, in the case of educational games, the identification of learning strategies (as well as the discovery of popular erroneous strategies) could even elevate their potential to successfully communicate educational concepts. To advance this field, we investigate how the spatial map of a game contributes to identifying player strategies and emphasize the importance of the appropriate level of abstraction to capture strategical decisions. Using visualizations and expert domain knowledge about spatial abstraction, we illustrate how the partitioning into affordance zones can reveal patterns and strategies in gameplay. To showcase the generalizability of our methodology, we investigate two case studies for the distinct genres of educational games (Parallel) and MMORPGs (Guild Wars 2). The two case studies unveil insightful strategies between different player sets of interest – only possible by the apt level of spatial abstraction.

In this demo paper, we present Parallel OPM. Informed by research on player needs of AI in educational games [1], it is a new visualization system that uses play community data from other players to help players compare and reflect on their gameplay with their peers in the game Parallel [2]. In this demo paper/session: participants will (i) have the opportunity to play a level in the game Parallel [2] (ii) Use the guided reflection system to analyze and reflect on their gameplay compared to their peers.

We present re-trainable procedural level generation via machine learning (RT-PLGML), a game mechanic of providing in-game training examples for a PLGML system. We discuss opportunities and challenges, along with concept RT-PLGML games.

Many human-AI authoring tools are used in a playful way, while being primarily designed for task-achievement—not playfulness. We argue that playfulness is an important yet overlooked factor of user behaviour and experience when interacting with such tools. Motivating and rewarding playfulness as an exploratory, task-agnostic, open, and subversive attitude can support the satisfaction of more diverse user goals, and have a strong, positive effect on the user experience, the emerging human-AI interaction, and the resulting artefact. In this paper, we motivate the importance of playfulness as user experience in human-AI authoring tools, and propose concrete strategies to design for playfulness in the human user through UI design, in the AI through algorithms, or through interventions to their dialog. We conclude with an outlook of the research agenda.

The game design community has a long history of adapting different forms of AI techniques to produce new playable experiences. However, current AI-based game design literature focuses primarily on designers’ intent and expression. This paper argues that engaging with players’ perspectives on AI during development is an essential but often overlooked piece in existing AI-based game design processes. By integrating this perspective, game designers can better outline how players may experience AI in the context of games and tailor design decisions to the intended experience. This paper offers three case studies that incorporate the player perspective into the design process and discusses design implications.

Algorithmic fairness is an essential requirement as AI becomes integrated in society. In the case of social applications where AI distributes resources, algorithms often must make decisions that will benefit a subset of users, sometimes repeatedly or exclusively, while attempting to maximize specific outcomes. How should we design such systems to serve users more fairly? This paper explores this question in the case where a group of users works toward a shared goal in a social exergame called Step Heroes. We identify adverse outcomes in traditional multi-armed bandits (MABs) and formalize the Greedy Bandit Problem. We then propose a solution based on a new type of fairness-aware multi-armed bandit, Shapley Bandits. It uses the Shapley Value for increasing overall player participation and intervention adherence rather than the maximization of total group output, which is traditionally achieved by favoring only high-performing participants. We evaluate our approach via a user study (n=46). Our results indicate that our Shapley Bandits effectively mediates the Greedy Bandit Problem and achieves better user retention and motivation across the participants.

Understanding players' mental models are crucial for game designers who wish to successfully integrate player-AI interactions into their game. However, game designers face the difficult challenge of anticipating how players model these AI agents during gameplay and how they may change their mental models with experience. In this work, we conduct a qualitative study to examine how a pair of players develop mental models of an adversarial AI player during gameplay in the multiplayer drawing game iNNk. We conducted ten gameplay sessions in which two players (n = 20, 10 pairs) worked together to defeat an AI player. As a result of our analysis, we uncovered two dominant dimensions that describe players' mental model development (i.e., focus and style). The first dimension describes the focus of development which refers to what players pay attention to for the development of their mental model (i.e., top-down vs. bottom-up focus). The second dimension describes the differences in the style of development, which refers to how players integrate new information into their mental model (i.e., systematic vs. reactive style). In our preliminary framework, we further note how players process a change when a discrepancy occurs, which we observed occur through comparisons (i.e., compare to other systems, compare to gameplay, compare to self). We offer these results as a preliminary framework for player mental model development to help game designers anticipate how different players may model adversarial AI players during gameplay.

This article focuses ontracing player knowledgein educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the work is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from intelligent tutoring systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game calledParallelto teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error. We also provide results from deployment of our system in a classroom environment.

The papers in this special section focus on user experiences of artificial intelligence in games.

The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. Through a systematic survey of neural network games (n = 38), we identified the dominant interaction metaphors and AI interaction patterns in these games. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems. Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction.

This paper focuses on player modeling in multiplayer adaptive games. While player modeling has received a significant amount of attention, less is known about how to use player modeling in multiplayer games, especially when an experience management AI must make decisions on how to adapt the experience for the group as a whole. Specifically, we present a multi-armed bandit (MAB) approach for modeling groups of multiple players. Our main contributions are a new MAB framework for multiplayer modeling and techniques for addressing the new challenges introduced by the multiplayer context, extending previous work on MAB-based player modeling to account for new group-generated phenomena not present in single-user models. We evaluate our approach via simulation of virtual players in the context of multiplayer adaptive exergames.

Applying neural network (NN) methods in games can lead to various new and exciting game dynamics not previously possible. However, they also lead to new challenges such as the lack of large, clean datasets, varying player skill levels, and changing gameplay strategies. In this paper, we focus on the adversarial player strategy aspect in the game iNNk, in which players try to communicate secret code words through drawings with the goal of not being deciphered by a NN. Some strategies exploit weaknesses in the NN that consistently trick it into making incorrect classifications, leading to unbalanced gameplay. We present a method that combines transfer learning and ensemble methods to obtain a data-efficient adaptation to these strategies. This combination significantly outperforms the baseline NN across all adversarial player strategies despite only being trained on a limited set of adversarial examples. We expect the methods developed in this paper to be useful for the rapidly growing field of NN-based games, which will require new approaches to deal with unforeseen player creativity.

Social comparison-based features are widely used in social computing apps. However, most existing apps are not grounded in social comparison theories and do not consider individual differences in social comparison preferences and reactions. This paper is among the first to automatically personalize social comparison targets. In the context of an m-health app for physical activity, we use artificial intelligence (AI) techniques of multi-armed bandits. Results from our user study (n=53) indicate that there is some evidence that motivation can be increased using the AI-based personalization of social comparison. The detected effects achieved small-to-moderate effect sizes, illustrating the real-world implications of the intervention for enhancing motivation and physical activity. In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change. Additionally, we propose research directions to mitigate this Personalization Paradox.

This paper explores multi-armed bandit (MAB) strategies in very short horizon scenarios, i.e., when the bandit strategy is only allowed very few interactions with the environment. This is an understudied setting in the MAB literature with many applications in the context of games, such as player modeling. Specifically, we pursue three different ideas. First, we explore the use of regression oracles, which replace the simple average used in strategies such as ε-greedy with linear regression models. Second, we examine different exploration patterns such as forced exploration phases. Finally, we introduce a new variant of the UCB1 strategy called UCBT that has interesting properties and no tunable parameters. We present experimental results in a domain motivated by exergames, where the goal is to maximize a player's daily steps. Our results show that the combination of ε-greedy or ε-decreasing with regression oracles outperforms all other tested strategies in the short horizon setting.

This paper focuses on building personalized player models solely from player behavior in the context of adaptive games. We present two main contributions: The first is a novel approach to player modeling based on multi-armed bandits (MABs). This approach addresses, at the same time and in a principled way, both the problem of collecting data to model the characteristics of interest for the current player and the problem of adapting the interactive experience based on this model. Second, we present an approach to evaluating and fine-tuning these algorithms prior to generating data in a user study. This is an important problem, because conducting user studies is an expensive and labor-intensive process; therefore, an ability to evaluate the algorithms beforehand can save a significant amount of resources. We evaluate our approach in the context of modeling players’ social comparison orientation (SCO) and present empirical results from both simulations and real players.

Reflection is a critical aspect of the learning process. However, educational games tend to focus on supporting learning concepts rather than supporting reflection. While reflection occurs in educational games, the educational game design and research community can benefit from more knowledge of how to facilitate player reflection through game design. In this paper, we examine educational programming games and analyze how reflection is currently supported. We find that current approaches prioritize accuracy over the individual learning process and often only support reflection post-gameplay. Our analysis identifies common reflective features, and we develop a set of open areas for future work. We discuss these promising directions towards engaging the community in developing more mechanics for reflection in educational games.

Computer games represent an ideal research domain for the next generation of personalized digital applications. This paper presents a player-centered framework of AI for game personalization, complementary to the commonly used system-centered approaches. Built on the Structure of Actions theory, the paper maps out the current landscape of game personalization research and identifies eight open problems that need further investigation. These problems require deep collaboration between technological advancement and player experience design.

Voice User Interfaces (VUIs) are increasing in popularity. However, their invisible nature with no or limited visuals makes it difficult for users to interact with unfamiliar VUIs. We analyze the impact of user characteristics and preferences on how users interact with a VUI-based calendar, DiscoverCal. While recent VUI studies analyze user behavior through self-reported data, we extend this research by analyzing both VUI usage data and self-reported data to observe correlations between both data types. Results from our user study (n=50) led to four key findings: 1) programming experience did not have a wide-spread impact on performance metrics while 2) assimilation bias did, 3) participants with more technical confidence exhibited a trial-and-error approach, and 4) desiring more guidance from our VUI correlated with performance metrics that indicate cautious users.

Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals. Although it has been explored for several decades, existing work in experience management has mostly focused on single-player experiences. This paper is a first attempt at identifying the main challenges to expand EM to multi-player/multi-user games or experiences. We also make connections to related areas where solutions for similar problems have been proposed (especially group recommender systems) and discusses the potential impact and applications of multi-player EM.

This paper recognizes idle games as a promising direction for exergames and other games designed for behavioral change. Based on a survey of 11 popular idle games, we extend existing literature by identifying the common core gameplay loop (active participation, inactive progress, and return reward) as well as the design patters used to support the loop. Furthermore, we propose an initial approach to extending idle game patterns to social exergames, focusing on improving player adherence.

Concurrent and parallel programming (CPP) skills are increasingly important in today's world of parallel hardware. However, the conceptual leap from deterministic sequential programming to CPP is notoriously challenging to make. Our educational game Parallel is designed to support the learning of CPP core concepts through a game-based learning approach, focusing on the connection between gameplay and CPP. Through a 10-week user study (n 25) in an undergraduate concurrent programming course, the first empirical study for a CPP educational game, our results show that Parallel offers both CPP knowledge and student engagement. Furthermore, we provide a new framework to describe the design space for programming games in general.

Voice User Interfaces (VUIs) are becoming increasingly popular. However, how VUIs can adapt to user differences remains insufficiently understood. We analyze usage data from a user study (n=50) where participants interacted with an unfamiliar VUI. Through automated clustering and statistical analysis, we present user models of their behavior patterns. We found user behavior can be grouped into three clusters: people who become proficient with the system and typically stay proficient while completing different tasks, people who exhibit an exploratory approach to completing tasks, and people who struggled to complete tasks. We discuss design implications based on these behavior clusters.

Voice User Interfaces (VUIs) are growing in popularity. However, even the most current VUIs regularly cause frustration for their users. Very few studies exist on what people do to overcome VUI problems they encounter, or how VUIs can be designed to aid people when these problems occur. In this paper, we analyze empirical data on how users (n=12) interact with our VUI calendar system, DiscoverCal, over three sessions. In particular, we identify the main obstacle categories and types of tactics our participants employ to overcome them. We analyzed the patterns of how different tactics are used in each obstacle category. We found that while NLP Error obstacles occurred the most, other obstacles are more likely to frustrate or confuse the user. We also found patterns that suggest participants were more likely to employ a "guessing" approach rather than rely on visual aids or knowledge recall.

In parallel programming, there is a shift away from the single execution path of sequential programming to situations where non-deterministic operation force consideration of multiple paths of execution. Compared to the substantial computer science education literature on helping students to learn sequential programming, there are fewer studies of the cognitive difficulties that students follow when learning parallel programming. To address this, we created a computer game, Parallel involving concurrent situations. The game is an abstract representation of concurrency problems where players are asked to solve a progression of puzzles involving arrows moving concurrently on tracks. Play does not require coding. The goals of our research were to 1) explore how students acquire skills in the design of solutions with parallelism, and 2) explore how interactive games can substitute or compliment conventional parallel programming courses. Through two user studies of the game (n=7) where students played the game and used a talk-aloud protocol alongside a researcher, three major themes emerged, that of non-determinism where students were able to make the connection of non-deterministic behavior in parallel programming to the game, self-efficacy where students were stating they felt their knowledge of parallel programming increased after playing the game, and expertise where researchers learned that expertise was important to successful connection of the game to parallel programming concepts These findings show that students are beginning to see the connection between the game/s presentation of concurrency to programming concepts such as non-determinism.

Within interactive narrative research, agency is largely considered in terms of a player's autonomy in a game, defined as theoretical agency. Rather than in terms of whether or not the player feels they have agency, their perceived agency. An effective interactive narrative needs to provide a player a level of agency that satisfies their desires and must do that without compromising its own structure. Researchers frequently turn to techniques for increasing theoretical agency to accomplish this. This paper proposes an approach to categorize and explore techniques in which a player's level of perceived agency is affected without requiring more or less theoretical agency.

The silent player character (SPC) is a reoccurring but not well-understood type of player character in narrative-driven games. In this paper, we present our findings from an analysis of SPC development in popular narrative games. We identify two main types of SPCs: projective and expressive characters. Then, we synthesized a list of methods designers can use to effectively communicate a SPC's story.

In the context of a learning game to teach parallel programming, we describe a procedural content generation (PCG) approach that can be controlled to generate programming puzzles involving a desired set of concepts, and of desired size and "difficulty". Our approach is based on grammars to control the generation of the puzzle structure, and orthographic graph embedding techniques to render it into a two-dimensional grid for our game. The proposed PCG system is designed to work with a player model in order to provide personalized learning experiences. We present an evaluation of the variability of the generated puzzles using several metrics including challenge and solvability as evaluated by a custom-build model checker. Our evaluation shows that this PCG system can generate a large number of varied puzzles but it is still not able to generate puzzles with certain aesthetic and functional qualities found in puzzles generated by human authors.

In this paper we present a survey of two of the main areas of research within the field of computational narrative, namely narrative analysis and generation. We argue that there is a gap between these two lines of work and propose a taxonomy of the computational models of narrative used within each. We outline potential mappings between computational narrative models with the goal of bridging this gap and alleviating the authorial bottleneck problem occurring when authoring content for computational narrative generation systems. Finally we discuss related work in this direction and report on our work-in-progress towards an end-to-end computational narrative system to bridge the gap.

In this paper, we present our method for automatically extracting narrative information of characters and their narrative roles from natural language stories. In our corpus of 15 unannotated folk tales, our Voz system identifies 87% of the characters in the stories and correctly assigns 68% of the character roles. To better understand the sources of error in our system, we present an analytical methodology to study how the error is introduced by different modules and how it propagates through the pipeline. This methodology allows us to identify the bottleneck with the largest impact on the final error, which might be different from the module with the largest individual error in isolation. Our methodology can be applied to a wide variety of similar information extraction pipelines.

Despite their use in traditional storytelling, analogy-based narrative devices have not been sufficiently explored in computational narrative. In this paper, we present our analogy-based story generation (ASG) approach in the Riu system, focusing on analogical retrieval and projection. We report on an empirical user evaluation about Riu's capability to retrieve and generate short noninteractive stories using the story analogies through mapping (SAM) algorithm. This work provides the foundation for exploration of ASG in more complex and interactive computational narrative works.

In computer-based interactive narrative, a key challenge is the conflict between user agency and authorial control of the story quality. In this paper, we use the constructs of character status and status shifts from improvisational and interactive theatre to further engage users in the creative process of co-creating the story. Based on the cognitive semantics theory of force dynamics, we develop a computational model of status shifts.

When compared to the depiction of external actions, modern computer games have developed very limited means of conveying game characters' inner activities. In this paper, we focus on different narrative perspectives and the ways in which they enable us to express a wider range of characters' inner worlds. We also present our on-going project Remembrance which uses a system of shifting external environments to reflect the character's inner world.

Computational narrative is a complex and interesting domain for exploring AI techniques that algorithmically analyze, understand, and most importantly, generate stories. This paper studies the importance of domain knowledge in story generation, and particularly in analogy-based story generation (ASG). Based on the construct of knowledge container in case-based reasoning, we present a theoretical framework for incorporating domain knowledge in ASG. We complement the framework with empirical results in our existing system Riu.