Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
The AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) is intended to be the definitive point of interaction between entertainment software developers interested in AI and academic and industrial AI researchers. Sponsored by the Association for the Advancement of Artificial Intelligence (AAAI), the conference proceedings reflects the work of both the research and commercial communities, and promotes AI research and practice in the context of interactive digital entertainment systems with an emphasis on commercial computer and video games.
Association for the Advancement of Artificial Intelligence
The 21st AAAI conference on Artificial Intelligence and Interactive Digital Entertainment was held at the University of Alberta, in Edmonton, Alberta, Canada.
Copyright (c) 2025 Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
LLM Game Rule Understanding Through Out-of-Distribution Fine-Tuning
Large Language Models (LLMs) have shown that a model pre-trained on general knowledge can perform well on specific tasks. However, LLMs natively perform poorly when it comes to demonstrating an understanding of rules, such as applying them, interacting with them, generating or modifying them, or evaluating them. Fine-tuning LLMs on a specific set of rules can significantly improve this performance. Yet, doing so undermines one of the main advantages of using a pre-trained model, which is its ability to generalize to rulesets outside its training distribution. This ability is critical for using LLMs as a tool in the game development process to give feedback or suggest rule modifications. In this paper, we introduce a framework for generating datasets to benchmark and train LLMs on their understanding of rules. We use Solitaire card games as our testbed for generating these datasets, as they have simple rules but offer a large space of possible variants, each played completely differently. We define a set of these variants using our custom Game Description Language (GDL) and use the framework to generate game progression questions, along with a textual explanation for each answer. Using these datasets, we conduct experiments to evaluate multiple LLMs on their understanding of rules, both with and without fine-tuning. Furthermore, we perform out-of-distribution evaluations in which the model is tested on rulesets it has not been trained on. Our results show that fine-tuning can improve the model's performance on both in-distribution and out-of-distribution rulesets, suggesting that training on rule-based datasets can improve general rule understanding of LLMs.
Copyright (c) 2025 Association for the Advancement of Artificial Intelligence
GameTileNet: A Semantic Dataset for Low-Resolution Game Art in Procedural Content Generation
GameTileNet is a dataset designed to provide semantic labels for low-resolution digital game art, advancing procedural content generation (PCG) and related AI research as a vision-language alignment task. Large Language Models (LLMs) and image-generative AI models have enabled indie developers to create visual assets, such as sprites, for game interactions. However, generating visuals that align with game narratives remains challenging due to inconsistent AI outputs, requiring manual adjustments by human artists. The diversity of visual representations in automatically generated game content is also limited because of the imbalance in distributions across styles for training data. GameTileNet addresses this by collecting artist-created game tiles from OpenGameArt.org under Creative Commons licenses and providing semantic annotations to support narrative-driven content generation. The dataset introduces a pipeline for object detection in low-resolution tile-based game art (e.g., 32x32 pixels) and annotates semantics, connectivity, and object classifications. GameTileNet is a valuable resource for improving PCG methods, supporting narrative-rich game content, and establishing a baseline for object detection in low-resolution, non-photorealistic images.
Generative design - the use of randomness, simulation or complex algorithms as part of a creative work - remains an uncommon skillset in the games industry. Acquiring familiarity and confidence with generative concepts is difficult and intimidating, which hampers its uptake in games. In this paper we argue that game design is generative design, and that by embracing the two as one and the same we can find new ways to communicate generative thinking to those who are unfamiliar with generative systems. We discuss the history of procedural generation in research and design, as well as reporting on surveys of 261 players and 126 game designers; we introduce the term procedural gameplay system to define a subset of generative systems used in games; and we offer some insights into how drawing an equivalence between these two design practices can yield new approaches and perspectives.
Video Game Level Design as a Multi-Agent Reinforcement Learning Problem
Procedural Content Generation via Reinforcement Learning (PCGRL) offers a method for training controllable level designer agents without the need for human datasets, using metrics that serve as proxies for level quality as rewards. Existing PCGRL research focuses on single generator agents, but are bottlenecked by the need to frequently recalculate heuristics of level quality and the agent’s need to navigate around potentially large maps. By framing level generation as a multi-agent problem, we mitigate the efficiency bottleneck of single-agent PCGRL by reducing the number of reward calculations relative to the number of agent actions. We also find that multi-agent level generators are better able to generalize to out-of-distribution map shapes, which we argue is due to the generators’ learning more local, modular design policies. We conclude that treating content generation as a distributed, multi-agent task is beneficial for generating functional artifacts at scale.
Time-based Chart Partitioning: Improving Local Coherency in Rhythm Game Chart Generation
Creating rhythm game charts can be a time-intensive process involving manually synchronizing gameplay elements with audio. Procedural Content Generation (PCG) techniques have been applied to automate this task, but current methods are limited by imprecise onsets and locally incoherent note patterning. In this paper, we introduce Time-based Chart Partitioning (TCP), a chart generation framework that combines neural network-based onset detection, symbolic beat snapping, and a time partitioning pattern matching algorithm. We evaluate TCP on osu!mania, comparing it against AutoOsu, a state-of-the-art chart generation system, and find that TCP achieves higher onset precision and improved local pattern coherence.
Declarative Specification of Temporal Entities for Large-Scale Character Simulation
Large-scale character simulations for games such as Dwarf Fortress and Bad News involve both large numbers of interacting sub-simulations, and very large numbers of entities being simulated. This combination of software complexity and real-time performance constraints make them difficult to develop. One recurring issue in these systems is the simulation of temporal entities: entities that come into and out of existence. The system must typically track the set of temporal entities that exist in the current simulation step, their states, the events of their creation and destruction, and, when needed, the set of all temporal entities that have ever existed. In this paper, we present Simulog, a very high-level declarative language for large scale character simulation based on ideas from the formal ontology literature. Simulog allows developers to declaratively specify temporal entities, and their relationships. It compiles these specifications to logic programs that are then compiled to C# for multithreaded execution, providing very high performance.
Detecting Duplicate States Is Worth It in Narrative Planning with Belief
Narrative planning algorithms generate stories as a sequence of actions that align with an author-defined goal while ensuring characters act believably, often requiring reasoning over nested beliefs. When theory of mind is represented, states include not only the factual world but also each character’s beliefs about the world and other characters' beliefs, potentially to infinite depth. In such planners, detecting duplicate states can prune redundant paths in the search space, but it is unclear whether it is too computationally expensive to justify. This paper investigates the cost and benefit of duplicate state detection using the Sabre planner, which models infinitely nested beliefs deterministically. We compare two approaches: tree search, which does not check for duplicate states, and graph search, which uses a recursive equivalence algorithm to detect and avoid duplicates. We provide a polynomial-time algorithm for detecting duplicate states and empirically show that using it significantly reduces the number of nodes generated and the total planning time across several benchmark problems. These findings suggest that duplicate detection in epistemic narrative planning is both feasible and beneficial.
CoDi: A Director-Actor Framework for Goal-Driven Interactive Story Generation with LLMs
Interactive storytelling offers personalized and engaging narrative experiences but poses significant authoring challenges. Our proposed framework, CoDi, extends the existing director-actor paradigm by enhancing the director agent's control capabilities. Specifically, CoDi uses high-level narrative goals to facilitate adaptive storytelling, with the director agent able to introduce new events, select relevant non-player characters (NPCs), and explicitly describe narrative outcomes. Comparative evaluations have demonstrated CoDi's competitive narrative quality, highlighting its effectiveness in balancing structural control and flexible agent behaviors, thus underscoring its potential as a foundation for scalable interactive storytelling systems. The code is publicly available on Github.
Policies of Multiple Skill Levels for Better Strength Estimation in Games
In many online competitive games, matchmaking is typically based on ratings systems, such as Elo or Glicko-2. However, these rating systems generally require sufficient matches to achieve accurate estimates, resulting in mismatches with opponents of different skill levels, especially during the early stages. To address this issue, methods for estimating strength from a small number of matches are essential. We use "strength" to refer to a player's overall ability, which can be quantified as a skill level such as a rating tier. In a previous state-of-the-art study, researchers estimated player's skill levels using strength scores learned from human match data. In this paper, we further incorporate policies (i.e., probability distributions of moves) of different skill levels from neural networks trained to imitate human players' gameplay. Namely we combine features from policies and the strength scores to estimate skill levels. We targeted Go and chess, where abundant data is available. Experiments in Go show that our method achieved 80% accuracy in strength estimation when given 10 matches, increasing to 92% when given 20 matches. Compared to the previous state-of-the-art method's 71% and 84%, our approach yields 8% improvements. Similar improvements were observed in chess. Since our method requires no game-specific features, it can be applied to other games or real-world problems.
Expressive Range Characterization of Open Text-to-Audio Models
Text-to-audio models are a type of generative model that produces audio output in response to a given textual prompt. Although level generators and the properties of the functional content that they create (e.g., playability) dominate most discourse in procedurally generated content (PCG), games that emotionally resonate with players tend to weave together a range of creative and multimodal content (e.g., music, sounds, visuals, narrative tone), and multimodal models have begun seeing at least experimental use for this purpose. However, it remains unclear what exactly such models generate, and with what degree of variability and fidelity: audio is an extremely broad class of output for a generative system to target. Within the PCG community, expressive range analysis (ERA) has been used as a quantitative way to characterize generators' output space, especially for level generators. This paper adapts ERA to text-to-audio models, making the analysis tractable by looking at the expressive range of outputs for specific, fixed prompts. Experiments are conducted by prompting the models with several standardized prompts derived from the Environmental Sound Classification (ESC-50) dataset. The resulting audio is analyzed along key acoustic dimensions (e.g., pitch, loudness, and timbre). More broadly, this paper offers a framework for ERA-based exploratory evaluation of generative audio models.
Accounting for Incomplete and Partially-Ordered Observations Within Classical Model-Based Plan Recognition for Sensemaking of Information Storygames
We formalize the sensemaking needed to play information storygames as a task grounded in automated plan recognition, and outline extensions to a baseline model thereof needed to account for a key part of the information storygame-play loop: non-linear discovery of ambiguous information. This novel problem setting is non-trivial—mechanically simulating this narrative sensemaking requires piecing together incompletely-specified events to reason about the means through which particular ends were achieved in the virtual world. Our work readies plan recognition systems for the task by extending a foundational compilation of plan recognition as planning to cover partially-ordered and lifted observations of both actions and facts. While state-of-the-art plan recognizers approximate piecemeal aspects of these features, they do so in isolation of each other and by appealing to disparate and complex frameworks. In contrast, we achieve these functions within a classical planning-based framework. Our results confirm that, while slower, our approach never has more (and often has fewer) false positives than the baseline model in predicting the ground truth plan being executed. We discuss our findings in the context of future work toward better simulating human information storygame sensemaking.
Text-to-Level Diffusion Models with Various Text Encoders for Super Mario Bros
Recent research shows how diffusion models can unconditionally generate tile-based game levels, but use of diffusion models for text-to-level generation is underexplored. There are practical considerations for creating a usable model: caption/level pairs are needed, as is a text embedding model, and a way of generating entire playable levels, rather than individual scenes. We present strategies to automatically assign descriptive captions to an existing level dataset, and train diffusion models using both pretrained text encoders and simple transformer models trained from scratch. Captions are automatically assigned to generated levels so that the degree of overlap between input and output captions can be compared. We also assess the diversity and playability of the resulting levels. Results are compared with an unconditional diffusion model and a generative adversarial network, as well as the text-to-level approaches Five-Dollar Model and MarioGPT. Notably, the best diffusion model uses a simple transformer model for text embedding, and takes less time to train than diffusion models employing more complex text encoders, indicating that reliance on larger language models is not necessary. We also present a GUI allowing designers to construct long levels from model-generated scenes.
Speeding Up Narrative Planning Using Fog of War Pruning
Narrative planning is the process of generating sequences of actions that form coherent and goal-oriented narratives. Classical implementations of narrative planning rely on heuristic search techniques to offer structured story generation, but often struggle with scalability because of large branching factors and deep search requirements. To improve the speed of narrative planning, we introduce Fog of War pruning, where Actions are only allowed if they involve people, places, and things that the protagonist character has discovered. This pruning technique restricts the planning to what is known from the perspective of the story's central character or characters, pruning branches of the search tree that involve actions beyond their current knowledge. This method is particularly useful in narratives where there is a strong protagonist focus and the story unfolds gradually as the character learns. This enables more efficient planning, while more closely aligning with how people would experience stories. Experiments across many narrative domains show that this technique not only speed up the search process, under identical search limits, also lets the planner solve more unique problems.
An Answer Set Encoding for Narrative Planning with Theory of Mind
There has been much research into making planning-based story generators more efficient; however, the question remains whether the same efficiency could be achieved by reducing the problem to a more widely-studied search problem and leveraging existing solvers. We investigate this question for the narrative planning formalism used by Sabre, which models character goals and beliefs with deeply-nested theory of mind. We use answer set programming to develop a declarative implementation of the same planning formalism. Benchmarking our implementation, we find that existing, specialized planners remain the state of the art for solving their target problems as quickly as possible. However, the compactness and modularity of our approach will make it easier for researchers to develop prototype generators for new solution spaces that build on existing models.
Spacetime Level Generation and Editing with Constraints from Examples
When generating levels for tile- and turn-based 2D games, generating a solution along with the level can ensure that the level is completable. One approach is to represent the level and solution as a 3D spacetime block with two spatial dimensions and one time dimension. Recent work in Space-Time WaveFunctionCollapse (STWFC) explored learning to generate such levels with solutions from example blocks by adapting 3D WaveFunctionCollapse for two spatial and one temporal dimension. While this generated solvable levels, the approach was found to be slow and could violate global constraints such as having a single player. Thus, in this work we explore learning to spacetime generate levels with solutions from examples by applying an existing 2D constraint-based level generation system. Rather than extending the 2D generator to 3D, we encode the time dimension in 2D by using a filmstrip representation where each frame represents a step in the time dimension. We compare STWFC and two filmstrip-based approaches: block constraints across frames that are analogous to those of STWFC, and constraints based on differences between frames. We demonstrate an application for interactive spacetime editing of levels and their solutions in the filmstrip representation, along with generating variations of levels sharing the same solution path, and variations of levels whose solution goes through the same arrangement of tiles at a point in time.
Generic Guard AI in Stealth Game with Composite Potential Fields
Guard patrol behavior is central to the immersion and strategic depth of stealth games, while most existing systems rely on hand-crafted routes or specialized logic that struggle to balance coverage efficiency and responsive pursuit with believable naturalness. We propose a generic, fully explainable, training-free framework that integrates global knowledge and local information via Composite Potential Fields, combining three interpretable maps—Information, Confidence, and Connectivity—into a single kernel-filtered decision criterion. Our parametric, designer-driven approach requires only a handful of decay and weight parameters—no retraining—to smoothly adapt across both occupancy-grid and NavMesh-partition abstractions. We evaluate on five representative game maps, two player-control policies, and five guard modes, confirming that our method outperforms classical baseline methods in both capture efficiency and patrol naturalness. Finally, we show how common stealth mechanics—distractions and environmental elements—integrate naturally into our framework as sub modules, enabling rapid prototyping of rich, dynamic, and responsive guard behaviors.
A Hybrid Co-Finetuning Approach for Visual Bug Detection in Video Games
Manual identification of visual bugs in video games is a resource-intensive and costly process, often demanding specialized domain knowledge. While supervised visual bug detection models offer a promising solution, their reliance on extensive labeled datasets presents a significant challenge due to the infrequent occurrence of such bugs. To overcome this limitation, we propose a hybrid Co-FineTuning (CFT) method that effectively integrates both labeled and unlabeled data. Our approach leverages labeled samples from the target game and diverse co-domain games, additionally incorporating unlabeled data to enhance feature representation learning. This strategy maximizes the utility of all available data, substantially reducing the dependency on labeled examples from the specific target game. The developed framework demonstrates enhanced scalability and adaptability, facilitating efficient visual bug detection across various game titles. Our experimental results show the robustness of the proposed method for game visual bug detection, exhibiting superior performance compared to conventional baselines across multiple gaming environments. Furthermore, CFT maintains competitive performance even when trained with only 50% of the labeled data from the target game.
Signals of Struggle: Detecting Player Difficulties Using Machine Learning
Struggle is an inevitable part of gameplay, and it’s often what makes games meaningful, rewarding, and fun. Still, some moments of difficulty spiral into frustration or confusion, and can cause players to quit entirely. Being able to detect and interpret struggle is thus essential for designing better player experiences, but identifying these moments remains challenging. We present a machine learning approach for detecting player struggle in real time using gameplay telemetry. Using three quests built in Terraria that each emphasize a different set of game mechanics – gathering, combat, or crafting – we collected data on how players interact with different systems and had them annotate where they encountered difficulty. Using this dataset, we trained Random Forest classifiers and evaluated model performance across different feature sets, window sizes, and step sizes. Our results show that such a model can successfully identify whether unseen players are experiencing struggle in the crafting quest, while the other quests proved more difficult. We also tested whether a model could classify the type of struggle as cognitive or performative, and found promising results for the crafting and combat quests. Our findings demonstrate the potential of using player telemetry to detect struggle, laying the groundwork for future adaptive systems that offer real-time, context-aware support tailored to individual player needs.
Real-time Prediction of Dota 2 Match Outcomes using In-game Chat Logs
This paper investigates the application of supervised learning for the purpose of match outcome prediction from Dota 2 in game chat logs. We analyze a dataset of 50,000 ranked matches, evaluating the predictive power of communication data alone and in combination with game events. Using LSTM and DistilBERT architectures, alongside a logistic regression baseline, we demonstrate that chat logs alone enable accurate prediction (up to 81.4% accuracy), while incorporating game events substantially improves performance (up to 98.4% accuracy). Our temporal analysis reveals that prediction reliability increases significantly during the mid-game phase (15-30 minutes), with models exhibiting different strengths - LSTM achieves higher accuracy while DistilBERT demonstrates greater prediction confidence. This study contributes to esports analytics by establishing chat logs as a viable predictive data source.
Speeding Up Narrative Planning with Causal Width Search and Pruning
Narrative planning can be used to create structured interactive experiences that dynamically respond to user input. Narrative planning works by generating a sequence of actions that achieves an author's desired goal while ensuring that there is an explanation for why each character takes each action in the sequence. An action in a sequence is considered necessary to that sequence if leaving the action out would prevent a later action in the sequence from being taken or prevent an author or character goal from being achieved. Using this definition, we define the causal width of a sequence to be the number of causally unnecessary actions, and we hypothesize sequences with a lower causal width are more likely to lead to a solution. We show that using causal width as a ranking mechanism can sometimes improve blind search, and ignoring stories with a high causal width can always improve the performance of heuristic search on a set of story benchmark problems.
Pixie: Code-Level Mechanic Generation for Game Designers
Procedural generation has been applied to many kinds of game content including levels, puzzles and narrative. Although PCG researchers have tackled the generation of rules and mechanics, these systems typically operate on bespoke codebases designed for mechanic invention tasks. In this paper we present Pixie, a system for generating and testing game mechanics that can be installed in any Unity project and configured using a simple annotation process to focus its scope and objectives. We describe the system's design and operation, demonstrate its ability to generate mechanics for several open-source Unity games, and provide an initial discussion of developer perspectives on the content it generates and its usefulness as a design companion.
Game Behaviour Trees Using Tile Rewrite Rules
Game creation tools that minimize required resources and knowledge to use them have transformed the practice of learning and prototyping game design, especially in hobbyist and indie development contexts. However, little is known about how the different programming models found underlying these tools affect their expressiveness and usability. A recently proposed programming model allows creators to author the logic of the entire game using a single "game behaviour tree" with tile-grid rewrite rules at the leaves. We contribute to this body of knowledge by studying this recently proposed programming model. We have used it to make clones of popular games as case studies, from which we extracted a number of design patterns. To gain formative information about usability, we also conducted a small user study with people who have varying levels of experience authoring games with other tools. We find game behaviour trees capable of expressing a wide variety of 2D turn-based games. Study participants are quick to grasp the underlying concepts, but further research is needed to understand discrepancies with user intuitions that may arise from their familiarity with different programming models.
From Unstable to Playable: Stabilizing Angry Birds Levels via Object Segmentation
Procedural Content Generation (PCG) techniques enable automatic creation of diverse and complex environments. While PCG facilitates more efficient content creation, ensuring consistently high-quality, industry-standard content remains a significant challenge. In this research, we propose a method to identify and repair unstable levels generated by existing PCG models. We use Angry Birds as a case study, demonstrating our method on game levels produced by established PCG approaches. Our method leverages object segmentation and visual analysis of level images to detect structural gaps and perform targeted repairs. We evaluate multiple object segmentation models and select the most effective one as the basis for our repair pipeline. Experimental results show that our method improves the stability and playability of AI-generated levels. Although our evaluation is specific to Angry Birds, our image-based approach is designed to be applicable to a wide range of 2D games with similar level structures.
Structure, Agency, and Improvisation in Human-Led Digital Interactive Narrative Exercises
Supporting high-agency player experiences without compromising narrative control is one of the major challenges in digital interactive narrative design. Humans, on the other hand, frequently meet this challenge when cooperating to improvise a narrative. We present a study examining how humans improvise narratives when paired together as the player and game master of a digital interactive narrative. We collected gameplay logs from these experiences, as well as participants’ reported perceptions of narrative structure, personal agency, and the reasons for both their choices and their partners’. We found a strong link between perceptions of structure and of agency. We also found a tendency for participants to better identify the goals of their partner’s actions following sessions where game masters expressed higher agency. Finally, we characterize the experiences using principles of improv theatre, drawing from the data to analyze negative experiences of agency as failures in the improv partnership.
Adversarial Strong Story Experience Management
In strong story experience management problems, an automated storytelling agent balances player autonomy with narrative structure in the context of an interactive story game world. However, it is possible for the game world to get softlocked in states outside narrative structures specified by the game designer. These states are called dead-ends. In this paper, we revisit adversarial strong story experience management, a framing of the experience management problem that models interactive storytelling as an adversarial game where dead-ends are losses. This framing is adversarial against narrative softlocks, not necessarily the player. We present a novel agent based on adversarial search and deep reinforcement learning, which is trained to avoid dead-ends while preserving player autonomy. We compare our approach to a reactive, narrative plan-based mediation system on a test set of games compatible with current narrative planning techniques. We show that our adversarial architecture outperforms narrative mediation on a suite of dead-end metrics during game trace and breadth-first tests of state transition system exploration, using classical and intentional planning domains.
Play-style Identification and Player Modelling for Generating Tailored Advice in Video Games
Learning new skills often leads to disengagement when individuals face obstacles arising from limited experience, poor instruction, or mismatched preferences. This is common in video games, where players frequently abandon challenging sections. While expert guidance can mitigate these issues, providing personalised advice at scale remains difficult. Automated systems typically offer generic feedback, lacking adaptability to individual playstyles. In this paper, we present an end-to-end system that generates personalised gameplay advice by learning from both pre-existing datasets and individual player behaviour. The system is evaluated in two domains: a simple GridWorld environment and the more complex MiniDungeons benchmark. Experimental results with simulated agents show that adherence to the generated advice leads to measurable improvements in performance. Our findings advance scalable, personalised guidance in games, with broader implications for learning and skill development.
Learning Combat Outcomes in Creative Assembly’s Total War
This paper outlines a supervised learning approach to model the combat outcomes of 1 vs 1 unit match-ups in strategy games. This is a core system in Creative Assembly's Total War. It is used to calculate combat outcomes in the auto-resolver and informs decisions of the battle AI whether units should engage or retreat from combat. We propose an alternative to the heuristic rules-based approach by using an automated framework to learn the combat outcomes. We describe our experiments, challenges and demonstrate that our model can accurately predict the unit's combat outcomes.
From Frustration to Fun: An Adaptive Problem-Solving Puzzle Game Powered by Genetic Algorithm
This paper explores adaptive problem solving with a game designed to support the development of problem-solving skills. Using an adaptive, AI-powered puzzle game, our adaptive problem-solving system dynamically generates pathfinding-based puzzles using a genetic algorithm, tailoring the difficulty of each puzzle to individual players in an online real-time approach. A player-modeling system records user interactions and informs the generation of puzzles to approximate a target difficulty level based on various metrics of the player. By combining procedural content generation with online adaptive difficulty adjustment, the system aims to maintain engagement, mitigate frustration, and maintain an optimal level of challenge. A pilot user study investigates the effectiveness of this approach, comparing different types of adaptive difficulty systems and interpreting players' responses. This work lays the foundation for further research into emotionally informed player models, advanced AI techniques for adaptivity, and broader applications beyond gaming in educational settings.
A Method to the Machine: An Architecture for Argument-Driven, Dynamic Character Performance
The evolution of architectures for virtual agents reveals a history of principled trade-offs. While dominant paradigms have successfully optimized for scalability and robustness, other valuable agent characteristics—such as the legibility of intent and the capacity for expressive performance—were often de-prioritized. This paper investigates an alternative set of architectural choices aimed at reclaiming these qualities. We introduce a novel agent architecture inspired by Stanislavskian acting, which operationalizes performance techniques by having agents reason from a core "supertask" and "ask questions" about their circumstances. The computational framework is composed of three layers: a strategic layer for character-centric decision-making, a defeasible logic programming (DELP) reasoning layer for argumentation, and a tactical layer using A Behavior Language (ABL). This paper provides a detailed architectural analysis of the system, demonstrated through a case study in a Unity-based game scenario. We conclude by discussing the system's limitations and engineering challenges, outlining a path for future work.
Evolutionary Optimization of Deep Learning Agents for Sparrow Mahjong
We present Evo-Sparrow, a deep learning-based agent for AI decision-making in Sparrow Mahjong, trained by optimizing Long Short-Term Memory (LSTM) networks using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Our model evaluates board states and optimizes decision policies in a non-deterministic, partially observable game environment. Empirical analysis conducted over a significant number of simulations demonstrates that our model outperforms both random and rule-based agents, and achieves performance comparable to a Proximal Policy Optimization (PPO) baseline, indicating strong strategic play and robust policy quality. By combining deep learning with evolutionary optimization, our approach provides a computationally effective alternative to traditional reinforcement learning and gradient-based optimization methods. This research contributes to the broader field of AI game playing, demonstrating the viability of hybrid learning strategies for complex stochastic games. These findings also offer potential applications in adaptive decision-making and strategic AI development beyond Sparrow Mahjong.
Playing Atari Space Invaders with Sparse Cosine Optimized Policy Evolution
Evolutionary approaches have previously been shown to be effective learning methods for a diverse set of domains. However, the domain of game-playing poses a particular challenge for evolutionary methods due to the inherently large state space of video games. As the size of the input state expands, the size of the policy must also increase in order to effectively learn the temporal patterns in the game space. Consequently, a larger policy must contain more trainable parameters, exponentially increasing the size of the search space. Any increase in search space is highly problematic for evolutionary methods, as increasing the number of trainable parameters is inversely correlated with convergence speed. To reduce the size of the input space while maintaining a meaningful representation of the original space, we introduce Sparse Cosine Optimized Policy Evolution (SCOPE). SCOPE utilizes the Discrete Cosine Transform (DCT) as a pseudo attention mechanism, transforming an input state into a coefficient matrix. By truncating and applying sparsification to this matrix, we reduce the dimensionality of the input space while retaining the highest energy features of the original input. We demonstrate the effectiveness of SCOPE as the policy for the Atari game Space Invaders. In this task, SCOPE with CMA-ES outperforms evolutionary methods that consider an unmodified input state, such as OpenAI-ES and HyperNEAT. SCOPE also outperforms simple reinforcement learning methods, such as DQN and A3C. SCOPE achieves this result through reducing the input size by 53% from 33,600 to 15,625 then using a bilinear affine mapping of sparse DCT coefficients to policy actions learned by the CMA-ES algorithm. The results presented in this paper demonstrate that the use of SCOPE allow evolutionary computation to achieve results competitive with reinforcement learning methods and far beyond what previous evolutionary methods have achieved.
Designer Difficulties: Visualizing the Possibility Spaces of Dynamic Difficulty Adjustment Systems
Dynamic Difficulty Adjustment (DDA) systems procedurally tune video games during runtime to deliver a designer-specified, balanced gameplay experience for players. DDA systems have been well-studied and deployed in both academic and industry contexts. However, a relatively unexplored challenge of DDA systems is how to assess the diversity of experiences they can deliver to players and whether or not the range of possible DDA actions will satisfy the original game designer's goals. DDA systems are inherently unpredictable in their output due to being designed to react to game-states that are uncertain and ever-changing. The varying scope and unpredictability of DDA systems means human playtesting can be time- and cost-intensive, and automated playtesting may produce misleading results. In this work, we introduce an approach for using expressive range analysis and unsupervised clustering to explore and evaluate gameplay traces from an in-development DDA system (FighterDDA) for turn-based role-playing game encounters. We find that this is an effective method for assessing designer goals and re-tuning accordingly an in-development system by visualizing and understanding the character of different DDA approaches. While specific to this system, we believe that there is promise in extending this approach to other genres and DDA platforms in the future.
Growing a Puzzle Garden: Exploring Casual and Serious Features in a Mixed-Initiative Logic Puzzle Authoring Tool
In this project, we introduce Puzzle Garden, a mixed-initiative tool for authoring logic grid puzzles. We present Puzzle Garden as a test bed for exploring how the inclusion of casual components can impact the design and use of creative support tools. The ``casual'' components of Puzzle Garden are inspired by the design principles of casual creators, while ``serious'' components give users fine grain control over the end product. We investigate how this tool can be used to study how users interact with serious and casual elements in authoring tools, through a preliminary user study lasting three weeks. Through the outcomes of this study we compiled a list of suggestions for how researchers can approach similar study designs.
Pareto-Based Narrative Planning: Making NPCs More Rational
A narrative planner decides the actions for all characters in a story while justifying each action according to the acting characters' own individual goals. However, an action that contributes to a goal may still seem irrational when considered alongside other actions the character may take; for instance, it may sacrifice a more important goal, or expose the character to unnecessary risks. We redefine the narrative planning character model to use a multiobjective framework where character actions are chosen from a Pareto front of best and safest options. We discuss how this framework can be applied to generate a policy for how the non-player characters should behave in any given state of an interactive narrative, and how we applied such a policy in the Traffic Stop de-escalation training simulation.
Minding Motivation: The Effect of Intrinsic Motivation on Agent Behaviors
Games are challenging for Reinforcement Learning (RL) agents due to their reward sparsity, as rewards are only obtainable after long sequences of deliberate actions. Intrinsic Motivation (IM) methods—which introduce exploration rewards—are an effective solution to reward sparsity. However, IM also causes an issue known as ‘reward hacking’, where the agent optimizes for the new reward at the expense of properly playing the game. The larger problem is that reward hacking itself is largely unknown; there is no answer to whether, and to what extent, IM rewards change the behavior of RL agents. This study takes a first step by empirically evaluating the impact on behavior of three IM techniques on the MiniGrid game-like environment. We compare these IM models with Generalized Reward Matching (GRM), a method that can be used with any intrinsic reward function to guarantee optimality. Our results suggest that IM causes noticeable change by increasing the initial rewards, but also altering the way the agent plays, and that GRM mitigated reward hacking in some scenarios.
Adaptive Visual Navigation Assistant in 3D RPGs
In complex 3D game environments, players rely on visual affordances to spot map transition points. Efficient identification of such points is important to client-side auto-mapping, and provides an objective basis for evaluating map cue presentation. In this work, we formalize the task of detecting traversable Spatial Transition Points (STPs)-connectors between two sub regions-and selecting the singular Main STP (MSTP), the unique STP that lies on the designer-intended critical path toward the player’s current macro-objective, from a single game frame, proposing this as a new research focus. We introduce a two-stage deep-learning pipeline that first detects potential STPs using Faster R-CNN and then ranks them with a lightweight MSTP selector that fuses local and global visual features. Both stages benefit from parameter-efficient adapters, and we further introduce an optional retrieval-augmented fusion step. Our primary goal is to establish the feasibility of this problem and set baseline performance metrics. We validate our approach on a custom-built, diverse dataset collected from five Action RPG titles. Our experiments reveal a key trade-off: while full-network fine-tuning produces superior STP detection with sufficient data, adapter-only transfer is significantly more robust and effective in low-data scenarios and for the MSTP selection task. By defining this novel problem, providing a baseline pipeline and dataset, and offering initial insights into efficient model adaptation, we aim to contribute to future AI-driven navigation aids and data-informed level-design tools.
A Database-Driven Framework for 3D Level Generation with LLMs
Procedural Content Generation for 3D game levels faces challenges in balancing spatial coherence, navigational functionality, and adaptable gameplay progression across multi-floor environments. This paper introduces a novel framework for generating such levels, centered on the offline, LLM-assisted construction of reusable databases for architectural components (facilities and room templates) and gameplay mechanic elements. Our multi-phase pipeline assembles levels by: (1) selecting and arranging instances from the Room Database to form a multi-floor global structure with an inherent topological order; (2) optimizing the internal layout of facilities for each room based on predefined constraints from the Facility Database; and (3) integrating progression-based gameplay mechanics by placing components from a Mechanics Database according to their topological and spatial rules. A subsequent two-phase repair system ensures navigability. This approach combines modular, database-driven design with constraint-based optimization, allowing for systematic control over level structure and the adaptable pacing of gameplay elements. Initial experiments validate the framework's ability in generating diverse, navigable 3D environments and its capability to simulate distinct gameplay pacing strategies through simple parameterization. This research advances PCG by presenting a scalable, database-centric foundation for the automated generation of complex 3D levels with configurable gameplay progression.
Steering Narrative Agents Through a Dynamic Cognitive Framework for Guided Emergent Storytelling
Narrative generation frameworks often face a trade-off between character believability and storyline adherence. Planner-based approaches ensure adherence to authorial designs at the cost of character agency and believability. In contrast, emergent narratives excel at presenting believable characters but often lack meaningful plot progression. We present DiriGent, a novel cognitive framework for agent modeling and narrative generation that enables authentic behavior while maintaining storyline adherence. Our agents possess a dynamic belief system and role-based ideal worlds that encode their basic values and relationships. We leverage Large Language Models (LLMs) to analyze the tensions between an agent's ideal worlds and their perceived actual world, which motivates their belief-driven actions. The system then adjusts the narrative world to amplify these tensions, thereby steering agent behavior toward the desired storyline. This dynamic framework allows agents to evolve meaningfully as the story unfolds, overcoming limitations of static agent profiles such as OCEAN. We evaluate our approach by generating stories for five story prompts. Our evaluation, consisting of automated LLM judges and human assessments, demonstrated significant improvements in character development and character motivation compared to baselines, while preserving storyline adherence. Our work presents a path toward interactive narratives that deliver rich characters and enable unique user experiences while adhering to desired storylines.
Xanadu: Generative Media Pipelines for Immersive Participatory Theater
We present Xanadu, a full-scale participatory theater production that integrated generative AI into a live musical. Over two weeks, 500 audience members contributed sketches, movements, and sounds that were transformed in real-time --via pipelines combining vision-language models, diffusion models, and moderation-- into images, 3D objects, poetry, and choreography rendered within an extended reality environment. Audience inputs were framed as ritual "offerings" to the Muses, with performers guiding participation and AI serving as interpretive intermediary. A hybrid hosting architecture combined controllable, research-driven models with fast foundation models, enabling 30–60 second generations while preserving stylistic and narrative coherence. We discuss design strategies and trade-offs that supported large-scale, group-level human-AI collaboration. This work contributes practical insights into deploying generative AI in live performance and highlights opportunities for designing AI systems that facilitate collective rather than individual creativity.
Learning Finite State Machines with Gameplay Video
There has been an ongoing trend of pitching neural networks as playable game engines. However, from a game developer’s perspective, world models are not very practical since they cannot make changes like in a traditional game engine. Our project learns a model of pacman from gameplay video that can be played in pygame. The learnt model captures the original behaviour of the entities in the majority of the instances.
A Demonstration of Pathfinding-Based Puzzle Generation with Adaptive Difficulty
In this demonstration paper, we showcase an adaptive puzzle-generation game designed to dynamically adjust puzzle difficulty in real-time for individual users. The game utilizes a genetic algorithm to procedurally generate pathfinding-based puzzles tailored specifically to each player's skill level and interaction patterns. A player-modeling mechanism continuously monitors user behaviors and interactions, enabling the game to match puzzle complexity to each player's abilities. By adaptively calibrating challenge levels, this system seeks to enhance player engagement, reduce frustration, and maintain an optimal difficulty balance.
State Space Visualization for Strong Story Experience Management Design
This paper presents a software library that enumerates the space of a state transition system specified by an action language, visualizes the states and action connections as a graph, and modifies the visualization based on underlying features determined through state and graph analysis. The library is intended as a tool for strong story interactive narrative design.
FighterDDA: A Simulation Testbed for Evaluating Director-Based Dynamic Balancing
While AI Directors have been widely studied, less attention has been paid to how different director styles can coexist in a single game environment with divergent design goals. This paper presents FighterDDA, a simulation testbed designed to evaluate dynamic balancing strategies aimed at different audiences using varied AI Directors in turn-based role-playing games. The testbed can rapidly simulate thousands of games, output formatted data, and provide visualizations of play traces for designers to evaluate whether a director strategy was successful. In an initial evaluation with the system, we found that a director made to even out win rates between differently-skilled players as well as a director to tune the overall difficulty for equally skilled players both showed success. The system demonstrates how a simulated testbed with data reporting supports prototyping and evaluating varied director systems.
GameGoogle: A Search Engine for Mechanics in Video Games
This paper introduces a new search engine for finding game mechanics internal to a game. The system utilizes a custom made database containing extracted gameplay information from openly available game walkthroughs, manuals, and guides along with general information about the game. This information is displayed as an interactive web application that displays relevant games to the user based on keyword queries. We conduct a preliminary user study to evaluate the relevancy of the results for the system and discuss future directions and feature implementations.
Towards a Model of Uncertainty in Narrative Planning
Most narrative planners currently lack the ability to model characters believing in multiple different possibilities or actions with different outcomes. By introducing a sense of uncertainty into narrative planning, we can model more realistic human behavior as well as generate new types of stories. In particular, stories surrounding a detective or a mystery require some form of uncertainty for the story to sound reasonable to a reader.
From Explainability to Interpretability: A Case Study on Game AI Using Program Synthesis
Many recent advances in artificial intelligence remain difficult to apply in real games due to their black-box nature. These systems often lack transparency and control, making them difficult to integrate into games where player experience and narrative coherence are important. Unpredictable or inexplicable agent behavior can confuse players and frustrate developers. My dissertation explores how program synthesis can address this issue by generating interpretable, controllable representations of agent behavior. Instead of relying on black-box neural network policies, symbolic programs are extracted or generated that capture agent logic in a readable and editable form. Several methods are explored: synthesizing functional programs to imitate and explain game agents, adapting logical program policies to multi-agent settings, and evaluating large language models for code generation across domains such as simplified Atari games, Baba is You, and also tabletop games. An open research question is whether the created conceptual program libraries are transferable across different game domains. My research aims to bridge the gap between AI capabilities and game development needs by making agent behavior transparent, explainable, and adaptable for developers.
Towards a Process of Simulating Narrative Comprehension
Simulating narrative comprehension has received little attention despite potentially aiding greatly in narrative generation or aiding in designing narratives. The work must be tied to theories of mind and understanding, and be based on algorithms that are described by psychology itself. For my thesis work, I propose building a cognition-like system based on the unified narrative understanding model: Multiversionality. In order to test the algorithmic model, I present the methods by which I plan to verify the solution: 1. by testing whether updates to prior knowledge that contradict prior assumptions actually affect, slow down, comprehension, 2. by testing how knowledgeable a player is in a given genre speeds up or slows down understanding by varying the input models, and 3. by testing how different refinement strategies could encapsulate human understanding.
Responsive Serious Games and Related Paths
Serious games are games whose primary purpose is something other than entertainment. A variety of domains have explored the use of serious games to achieve specific practical outcomes in players, including training and healthcare. In previous work, I have leveraged adversarial deep learning methods to create models that effectively and dynamically manage the experiences of users. I intend to develop this work further into serious games applications to adapt serious games during gameplay to support the desired outcome.
Developing game mechanics is challenging due to the need for intricate design and programming. Procedural Content Generation (PCG) is a prevalent aspect of modern video game development, enabling the generation of content via algorithms. Achieving the desired balance and player experience is a multifaceted challenge, with game mechanics playing a crucial role—requiring thorough testing, player feedback, and iterative refinement. This work explores automated approaches to mechanic generation and evaluation, drawing from Automated Game Design (AGD). I present methods for generating mechanics, reconstructing levels through level inpainting, and creating enemies that can only be defeated using newly generated mechanics. Comparative studies between reinforcement learning agents and traditional static agents such as A* show that RL facilitates more diverse and human-like mechanic discovery, while static methods remain more stable but less creative. Ongoing work integrates these techniques into environments where mechanics, levels, and enemies co-evolve, enabling richer evaluation of gameplay dynamics. To assess alignment between generated content and designer intent, I propose Design Impact Accuracy (DIA) as a metric to measure how effectively new mechanics are supported within AI-generated levels and enemies.
Process-Centered Design in Co-Creative Support Tools for 2D Art
In 2D illustration an artist makes a series of decisions throughout the process, with those choices becoming more granular over time. These decisions vary in scope and impact. Given this formulation of a creative process, I observe that there exist portions of common workflows where an artist is largely following through on the logical conclusions of prior decisions. These are the specific areas of these creative workflows where I believe that co-creative tools can be implemented while retaining creative control over the completed artifact. My work so far has been in implementing such tools for steps in illustration and frame-by-frame animation. I intend to design further process-centered co-creative systems and study their usability and impact in the context of existing workflows.
Modeling the Internal Monologue: A Framework for Reconciling Authorial Intent with Agent Autonomy
The design of narrative agents is often caught in a trade-off between authorial control and character agency. This work proposes the existence of a middle-ground approach, arguing that a robust, authored narrative can emerge from the performative autonomy of deeply simulated, coherent agents. To explore this thesis, I have developed a novel, three-layer architecture that synthesizes Stanislavskian performance theory, defeasible logic for argumentation-based reasoning, and a dynamic behavior language for execution. My work to date includes implementing this architecture for goal attainment in the Oops! All Bards prototype, and exploring foundational personality models for goal formation in the Stay Thy Blade prototype. The remaining research directions presented for feedback include the synthesis of these two tracks, the development of a constrained knowledge authoring framework, and a multi-pronged evaluation strategy. The contribution of this dissertation will be a validated framework that attempts to bridge the gap between agent autonomy and authorial intent, enabling the creation of "strong stories through strong characters."
Game-Based Platforms for Studying Virtual Agent Believability
Believable virtual agents are central to many interactive systems, yet current methods for evaluating user trust and perception often rely on limited, decontextualized tools. My research investigates how narrative-driven, game-based environments can be used to study users’ perceptions of virtual agent believability, particularly in relation to gender presentation and role-based behavior. I am developing an interactive platform where users engage with male, female, and androgynous agents embedded in branching scenarios that simulate trust-based decision-making. The platform incorporates adaptive storytelling, real-time analytics, and machine learning to capture both behavioral and attitudinal responses. By gamifying the evaluation process, the research aims to produce richer, more ecologically valid data than traditional surveys. This work draws from human-computer interaction, affective computing, and AI ethics to explore how design choices, such as agent role, gender, and interactivity, affect user judgments of reliability and integrity. As an early-stage PhD student, I am currently refining the research questions, platform design, and experimental methods. Feedback from the AIIDE community will help sharpen the research focus, improve methodological choices, and ensure broader relevance across virtual agent applications.
Game Balancing via Procedural Content Generation and Simulations
Balancing games requires extensive manual work and human playtesting during development. Existing research proposes using search-based optimization combined with game simulations to estimate balance. However, these approaches are tailored to specific environments, making them difficult to transfer to other games. Additionally, simulating a game is computationally intensive. My research aims to develop procedural content generation (PCG) methods to automate the generation of balanced content for competitive games. Unlike related work, domain-independent methods enable transferability to other games. I focus on two key aspects that significantly impact a game's overall balance: game levels and economies. Game level balancing is approached by framing it as both, a PCG task and a Markov decision process in order to apply reinforcement learning (RL). Therefore, I adapt and extend the PCGRL (PCG via RL) framework. Based on an existing formal definition, my research considers game economies as graph structures. In this context, I present two frameworks: G-PCGRL (Graph-PCGRL) and GEEvo (Game Economy Evolution).
Setting the Stage: Using Knowledge Graphs for Environmental Context for Social Characters
In this paper, I present a preliminary approach for encoding location data for CiF-like social characters in a to alleviate some of the authorial burden associated with authoring these types of interactive characters. To ease some of the authorial burden, I discuss how decoupling character actions and storing them within the environment can lead to a lowering of authorial complexity as well as treating the environment as a first-class entity can lead to more nuanced character performances. To better understand what is being decoupled, I describe how the term environment may be too vague when regarding interactive characters and their performances, and instead extending concepts from theater and dramaturgy, such as stage and props. From there I describe their functionality and possible encoding for props and stages.
From Emergence to Planning: A Triangle Framework for Scalable, Controllable Interactive Storytelling
Interactive story systems today sit at three extremes. Emergent multi‑agent simulations give each character local intelligence but no global view, often losing plot structure. Reactive systems makes fast, state‑based decisions. They form plans using hand-authored rules without searching for action sequences, so these systems can respond quickly but can wander if long-term rules are not explicitly authored. Centralized narrative planners reason globally to craft coherent, goal‑directed plots, yet are computationally expensive. In my doctoral work I treat these not as isolated choices but as the three corners of a triangle spectrum of narrative generation. I propose hybrid, landmark‑guided approaches that can scale to larger domains. I am also exploring how large language models (LLMs) can be embedded within these hybrid approaches themselves. This paper outlines research questions, methodology, progress to date, evaluation plan, and requested feedback.
Design as Language Co-Construction: Toward Responsible, Transparent, and Adaptive AI Support for Educational Game Design
Despite proven benefits, educational games are rarely adopted in higher education due to the complexity of designing games that fit diverse teaching needs. Most instructors lack game design expertise or access to professional support. Advances in AI, particularly large language models (LLMs), now make it possible for instructors to design games with AI assistance. However, effective AI support must go beyond generating content; it should build trust, encourage instructor agency, and ensure the final game aligns with teaching goals. My dissertation explores an interactive framework where instructors and AI co-construct a shared design language, enabling collaborative, transparent, and context-aware game design. The in-development tool uses LLM-driven dialogue to transform course materials and pedagogical aims into game design artifacts such as mechanics outlines and design documents. Its text-based interface, supported by lightweight visual scaffolds, allows iterative refinement and explanation of AI suggestions, empowering instructors to create educational games suited to their classrooms.