bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning to Reason in Round-Based Games: Multi-Task Sequence Generation for Purchasing Decision Making in First-Person Shooters
Authors: Zeng Yilei , Lei Deren , Li Beichen , Jiang Gangrong , Ferrara Emilio , Zyda Michael
Published: October 2022

Sequential reasoning is a complex human ability, with extensive previous research focusing on gaming AI in a single continuous game, round-based decision makings extending to a sequence of games remain less explored. Counter-Strike: Global Offensive (CS:GO), as a round-based game with abundant expert demonstrations, provides an excellent environment for multi-player round-based sequential reasoning. In this work, we propose a Sequence Reasoner with Round Attribute Encoder and Multi-Task Decoder to interpret the strategies behind the round-based purchasing decisions. We adopt few-shot learning to sample multiple rounds in a match, and modified model agnostic meta-learning algorithm Reptile for the meta-learning loop. We formulate each round as a multi-task sequence generation problem. Our state representations combine action encoder, team encoder, player features, round attribute encoder, and economy encoders to help our agent learn to reason under this specific multi-player round-based scenario. A complete ablation study and comparison with the greedy approach certify the effectiveness of our model. Our research will open doors for interpretable AI for understanding episodic and long-term purchasing strategies beyond the gaming community.
Reinforcement Learning Methods to Evaluate the Impact of AI Changes in Game Design
Authors: Gutiérrez-Sánchez Pablo , Gómez-Martín Marco A. , González-Calero Pedro A. , Gómez-Martín Pedro P.
Published: October 2022

Game development has become a long process that requires many professionals working on a project during several months or years. With this scenario the re-utilization of resources is crucial not only to alleviate the process but also to bring coherence into the final product. In this paper we focus on the reuse of NPCs and the problems it brings about. In particular it is common to have different breeds (or personalities) of NPCs that are placed on different levels on the game. The problem arises when their behaviors are fine-tuned to accommodate a specific level needs without taking into consideration that this change may alter their performance on previous already-tested levels. The paper presents the application of reinforcement learning together with behavior trees to automatically test if modifications to the AIs of a stealth game have an impact on the user experience. Our experiments reveal that this approach provides a way of diagnosing alterations in level gameplay that correspond to the effects observed by human testers.
Tile Embedding: A General Representation for Level Generation
Authors: Jadhav Mrunal , Guzdial Matthew
Published: October 2022

In recent years, Procedural Level Generation via Machine Learning (PLGML) techniques have been applied to generate game levels with machine learning. These approaches rely on human-annotated representations of game levels. Creating annotated datasets for games requires domain knowledge and is time-consuming. Hence, though a large number of video games exist, annotated datasets are curated only for a small handful. Thus current PLGML techniques have been explored in limited domains, with Super Mario Bros. as the most common example. To address this problem, we present tile embeddings, a unified, affordance-rich representation for tile-based 2D games. To learn this embedding, we employ autoencoders trained on the visual and semantic information of tiles from a set of existing, human-annotated games. We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.
Explaining Deep Reinforcement Learning Agents in the Atari Domain through a Surrogate Model
Authors: Sieusahai Alexander , Guzdial Matthew
Published: October 2022

One major barrier to applications of deep Reinforcement Learning (RL) both inside and outside of games is the lack of explainability. In this paper, we describe a lightweight and effective method to derive explanations for deep RL agents, which we evaluate in the Atari domain. Our method relies on a transformation of the pixel-based input of the RL agent to a symbolic, interpretable input representation. We then train a surrogate model, which is itself interpretable, to replicate the behavior of the target, deep RL agent. Our experiments demonstrate that we can learn an effective surrogate that accurately approximates the underlying decision making of a target agent on a suite of Atari games.
Sabre: A Narrative Planner Supporting Intention and Deep Theory of Mind
Authors: Ware Stephen G. , Siler Cory
Published: October 2022

Sabre is a narrative planner—a centralized, omniscient decision maker that solves a multi-agent storytelling problem. The planner has an author goal it must achieve, but every action taken by an agent must make sense according to that agent's individual intentions and limited, possibly wrong beliefs. This paper describes the implementation of Sabre, which supports a rich action syntax and imposes no arbitrary limit on the depth of theory of mind. We present a search procedure for generating plans that achieve the author goals while ensuring all agent actions are explained, and we report the system's performance on several narrative planning benchmark problems.
The Definition-Context-Purpose Paradigm and Other Insights from Industry Professionals about the Definition of a Quest
Authors: Yu Kristen K. , Guzdial Matthew , Sturtevant Nathan
Published: October 2022

Among academic communities there is no single agreed upon definition of a quest. The industry perspective on this topic is also largely unknown. Thus, the purpose of this paper is to gain an understanding of the definition of a quest from industry professionals to better inform the academic community. We interviewed fifteen game developers with experience designing or implementing quests or narratives, and process the interviews using thematic analysis to identify trends. We identified a variety of personal developer definitions. However, we also discovered several themes that may inform future academic work. We introduce the definition-context-purpose paradigm as a synthesis of these trends: elements of a quest, purpose of a quest, and context of a quest. Finally, we discuss the developer’s reaction to a recently proposed quest definition as part of a push towards a general quest definition.
Assessing the Effects of Interacting with MAP-Elites
Authors: Alvarez Alberto , Font Jose , Dahlskog Steve , Togelius Julian
Published: October 2022

MAP-Elites has been successfully applied to the generation of game content and robot behaviors. However, its behavior and performance when interacted with in co-creative systems is underexplored. This paper analyzes the implications of synthetic interaction for the stability and adaptability of MAP-Elites in such scenarios. We use pre-recorded human-made level design sessions with the Interactive Constrained MAP-Elites (IC MAP-Elites). To analyze the effect of each edition step in the search space over time using different feature dimensions, we introduce Temporal Expressive Range Analysis (TERA). With TERAs, MAP-Elites is assessed in terms of its adaptability and stability to generate diverse and high-performing individuals. Our results show that interactivity, in the form of design edits and MAP-Elites adapting towards them, directs the search process to previously unexplored areas of the fitness landscape and points towards how this could improve and enrich the co-creative process with quality-diverse individuals.
Multi-Agent Cooperation in Games with Goal Oriented Action Planner: Use Case in WONDER Prototype Project
Authors: Boeda Gautier
Published: October 2022

Multi-agent cooperation systems often rely on an external commander-like entity that will plan for all the agents it manages. When it comes to games development, this entity can be hidden from the player, which makes the player believe the characters are actually taking their own decisions. However, it has limitations. We believe that to achieve truly believable character-interactions between non-playable characters, real(non-scripted) communication between the agents is the key. We will introduce our multi-agent cooperation system where each AI-agent thinks for itself and communicates with the other agents to cooperate and achieves complex goals. The system will be demonstrated with a use case in our WONDER prototype project.
Automated Personalized Exposure Therapy Based on Physiological Measures Using Experience-Driven Procedural Content Generation
Authors: Mahmoudi-Nejad Athar
Published: October 2022

Our research focuses on personalized virtual reality exposure therapy (VRET) based on the Experience-Driven Procedural Content Generation (EDPCG) framework. There are existing approaches for personalized VRET; however, they are subjective and require hand-authored and predefined rules that may not generalize to all subjects. We propose a framework to personalize VRET based on predicting subjects' experiences via physiological sensors and machine learning algorithms. The framework then automatically adapts exposure parameters based on the subject's physiological response using a PCG method. We intend to conduct two human subject studies for arachnophobia and fear of public speaking.
Neural Synthesis of Sound Effects Using Flow-Based Deep Generative Models
Authors: Andreu Sergi , Villanueva Aylagas Monica
Published: October 2022

Creating variations of sound effects for video games is a time-consuming task that grows with the size and complexity of the games themselves. The process usually comprises recording source material and mixing different layers of sound to create sound effects that are perceived as diverse during gameplay. In this work, we present a method to generate controllable variations of sound effects that can be used in the creative process of sound designers. We adopt WaveFlow, a generative flow model that works directly on raw audio and has proven to perform well for speech synthesis. Using a lower-dimensional mel spectrogram as the conditioner allows both user controllability and a way for the network to generate more diversity. Additionally, it gives the model style transfer capabilities. We evaluate several models in terms of the quality and variability of the generated sounds using both quantitative and subjective evaluations. The results suggest that there is a trade-off between quality and diversity. Nevertheless, our method achieves a quality level similar to that of the training set while generating perceivable variations according to a perceptual study that includes game audio experts.
Aesthetic Bot: Interactively Evolving Game Maps on Twitter
Authors: Charity M , Togelius Julian
Published: October 2022

This paper describes the implementation of the Aesthetic Bot, an automated Twitter account that posts images of small game maps that are either user-made or generated from an evolutionary system. The bot then prompts users to vote via a poll posted in the image's thread for the most aesthetically pleasing map. This creates a rating system that allows for direct interaction with the bot in a way that is integrated seamlessly into a user's regularly updated Twitter content feed. Upon conclusion of the each voting round, the bot learns from the distribution of votes for each map to emulate user preferences for design and visual aesthetic in order to generate maps that would win future vote pairings. We discuss the ongoing results and emerging behaviors that have occurred since the release of this system from both the bot's generation of game maps and the participating Twitter users.
Sturgeon: Tile-Based Procedural Level Generation via Learned and Designed Constraints
Authors: Cooper Seth
Published: October 2022

This work describes Sturgeon, a system for tile-based level generation using constraints. We present a small mid-level constraint API that can be instantiated with various low-level solvers, including portfolio solvers. We show how this mid-level API can be used to generate levels incorporating a variety of constraints, including constraints learned from example levels and constraints provided by a designer. We incorporate a flexible constraint-based approach within the system for ensuring level goals are reachable. Finally, we demonstrate the effectiveness of the system in a variety of games and show applications ranging from infilling and repair to expressive range coverage.
Creative Wand: A System to Study Effects of Communications in Co-creative Settings
Authors: Lin Zhiyu , Agarwal Rohan , Riedl Mark
Published: October 2022

Recent neural generation systems have demonstrated the potential for procedurally generating game content, images, stories, and more. However, most neural generation algorithms are “uncontrolled” in the sense that the user has little say in creative decisions beyond the initial prompt specification. Co-creative, mixed-initiative systems require user-centric means of influencing the algorithm, especially when users are unlikely to have machine learning expertise. The key to co-creative systems is the ability to communicate ideas and intent from the user to the agent, as well as from the agent to the user. Key questions in co-creative AI include: How can users express their creative intentions? How can creative AI systems communicate their beliefs, explain their moves, or instruct users to act on their behalf? When should creative AI systems take initiative? The answer to such questions and more will enable us to develop better co-creative systems that make humans more capable of expressing their creative intents. We introduce CREATIVE-WAND, a customizable framework for investigating co-creative mixed-initiative generation. CREATIVE-WAND enables plug-and-play injection of generative models and human-agent communication channels into a chat-based interface. It provides a number of dimensions along which an AI generator and humans can communicate during the co-creative process. We illustrate the CREATIVE-WAND framework by using it to study one dimension of co-creative communication—global versus local creative intent specification by the user—in the context of storytelling.
Adventures of AI Directors Early in the Development of Nightingale
Authors: Yu Kristen K. , Guzdial Matthew , Sturtevant Nathan R. , Cselinacz Morgan , Corfe Chris , Hubert Lyall Izzy , Smith Chris
Published: October 2022

Players can sometimes engage with parts of a video game that they do not enjoy if the game does not try to adapt the experience to the player’s preference. AI directors have been used in the past to tailor player experience to different people. In industry, AI directors are relatively uncommon and are typically domain-specific and rules-based. In this paper, we present a reinforcement learning-based AI director developed for the industry game Nightingale with the help of Inflexion Games. We ran an experiment to evaluate the effectiveness of the AI director in creating a desired player experience, but found inconclusive evidence. In line with this year’s theme, we present our negative results and their implications for future AI directors, along with general discussion from working closely with an industry partner.
Robust Player Plan Recognition in Digital Games with Multi-Task Multi-Label Learning
Authors: Goslen Alex , Carpenter Dan , Rowe Jonathan , Azevedo Roger , Lester James
Published: October 2022

Plan recognition is a key component of player modeling. Player plan recognition focuses on modeling how and when players select goals and formulate action sequences to achieve their goals during gameplay. By occasionally asking players to describe their plans, it is possible to devise robust plan recognition models that jointly reason about player goals and action sequences in coordination with player input. In this work, we present a player plan recognition framework that leverages data from player interactions with a planning support tool embedded in an educational game for middle school science education, CRYSTAL ISLAND. Players are prompted to use the planning tool to describe their goals and planned actions in CRYSTAL ISLAND. We use this data to devise data-driven player plan recognition models using multi-label multi-task learning. Specifically, we compare single-task and multi-task learning approaches for both goal prediction and action sequence prediction. Results indicate that multi-task learning yields significant benefits for action sequence prediction. Additionally, we find that incorporating automated detectors of plan completion in plan recognition models improves predictive performance in both tasks.
Transformer-Based Value Function Decomposition for Cooperative Multi-Agent Reinforcement Learning in StarCraft
Authors: Khan Muhammad Junaid , Ahmed Syed Hammad , Sukthankar Gita
Published: October 2022

The StarCraft II Multi-Agent Challenge (SMAC) was created to be a challenging benchmark problem for cooperative multi-agent reinforcement learning (MARL). SMAC focuses exclusively on the problem of StarCraft micromanagement and assumes that each unit is controlled individually by a learning agent that acts independently and only possesses local information; centralized training is assumed to occur with decentralized execution (CTDE). To perform well in SMAC, MARL algorithms must handle the dual problems of multi-agent credit assignment and joint action evaluation.

This paper introduces a new architecture TransMix, a transformer-based joint action-value mixing network which we show to be efficient and scalable as compared to the other state-of-the-art cooperative MARL solutions. TransMix leverages the ability of transformers to learn a richer mixing function for combining the agents' individual value functions. It achieves comparable performance to previous work on easy SMAC scenarios and outperforms other techniques on hard scenarios, as well as scenarios that are corrupted with Gaussian noise to simulate fog of war.
HeadSpace: Incorporating Action Failure and Character Beliefs into Narrative Planning
Authors: Sanghrajka Rushit , Young R. Michael , Thorne Brandon
Published: October 2022

Previous work on story planning has lacked a knowledge representation for characters that attempt actions that fail because of the characters' misconceptions about the world state. This work describes HeadSpace, a state-space heuristic search planning system that generates stories that track and manipulate characters' beliefs about the story world. The planner produces story plans with actions that are attempted but fail. We show an example story plan that contains failed-action content that cannot be generated by typical planning-based approaches to story creation, and we provide an analytical evaluation that characterizes our planner's increased expressive range relative to other narrative planners addressing character belief and/or failed action execution.
Evaluating Reader Comprehension of Plan-Based Stories Containing Failed Actions
Authors: Sanghrajka Rushit , Young R. Michael
Published: October 2022

A growing number of algorithms for story planning include the ability to create stories with failed actions -- in particular failed actions that occur because of the mistaken beliefs of the characters attempting them. To date, most of these systems have been evaluated analytically, primarily by comparing their expressive range to prior story generation systems. Empirical evaluation of these systems has been preliminary. In this paper, we outline a general comprehension-based approach to the evaluation of plan-based story generation. We describe how we specialize it for use evaluating story plans containing failed actions, and we describe the design and results of an experiment using this approach to evaluate plot lines produced by HeadSpace, a system that models the beliefs of characters and uses that model to generate plot lines containing actions that are attempted but that fail.
Using Multi-Armed Bandits to Dynamically Update Player Models in an Experience Managed Environment
Authors: Vinogradov Anton , Harrison Brent
Published: October 2022

Players are often considered to be static in their preferred play styles, but this is often untrue. While in most games this is not an issue, in games where experience managers (ExpMs) control the experience, a shift in a player's preferences can lead to loss of engagement and churn. When an ExpM makes changes to the game world, the game world is now biased in favor of the current player model which will then influence how the ExpM will observe the player's actions, potentially leading to a biased and incorrect player model. In these situations, it is beneficial for the ExpM to recalculate the player model in an efficient manner. In this paper we show that we can use the techniques used to solve multi-armed bandits along with our own idea of distractions to minimize the time it takes to identify what a player's preferences are after they change, compensate for the bias of the game world, and to minimize the number of intrusive elements added to the game world. To evaluate these claims, we use a text-only interactive fiction environment specifically created to be experience managed and to exhibit bias. Our experiments show that multi-armed bandit algorithms can quickly recalculate a player model in response to shifts in a player's preferences compared to several baseline methods.
World Models with an Entity-Based Representation
Authors: Yousefzadeh Khameneh Nazanin , Guzdial Matthew
Published: October 2022

Reinforcement learning (RL) is a powerful way to solve sequential decision-making tasks. However training an RL agent in a complex environment requires a large amount of interactions, which is non-ideal when acting in an environment is costly or dangerous. One alternative is to learn an approximation of the real environment, referred to as a world model. This simulator can be used to train an agent and transfer the learned policy to the real environment. Unfortunately, training world models have traditionally required a significant number of interactions in the real environment. This brings us to the same problem when it is costly or dangerous to act in the real environment. To address this problem, we present an entity-based representation and corresponding architecture, which allows for greater data efficiency in world model training. Our approach outperforms other world model baselines in an initial application to the game Pong.
EM-Glue: A Platform for Decoupling Experience Managers and Environments
Authors: Mori Giulio , Thue David , Schiffel Stephan
Published: October 2022

Experience Management uses AI technologies to improve people's experiences within an interactive application by changing the environment while the experience is underway. Game-related research in this field has a trend where each experience manager is built in a way that is tightly integrated with the environment that it can change. One consequence of this integration is that it becomes difficult to compare one manager to another in a single environment, or a single manager to itself across multiple environments. With this paper, we propose a solution for decoupling experience managers from the environments that they can change, through the use of an intermediate software platform. We describe the structure of the platform, a protocol that facilitates communication between a manager and an environment, and how normal communication happens. Moreover, we introduce the Camelot Wrapper, software built to extend the interactive visualization engine Camelot and connect it to our platform.
Reasoning with Ontologies for Non-player Character’s Decision-Making in Games
Authors: Lapeyrade Sylvain
Published: October 2022

In most games, the decision-making of non-player characters (NPCs) is usually constructed using variants of state machines, behaviour trees, utility-based AI or planning. These methods are relatively simple to implement, but have drawbacks in that it can be difficult to create complex non-hard-coded behaviour for many agents and to maintain the algorithms, especially when scaling up. Game designers usually think of their games with rules that closely resemble logic rules. A methodology is introduced to design both general and modular behaviour using a logic reasoner with hierarchical ontologies. This approach is combined with the well-founded semantics (WFS) to solve the problem of representation and reasoning despite the lack of NPC knowledge.
Open-World Narrative Generation to Answer Players’ Questions
Authors: Siler Cory
Published: October 2022

Planning-based narrative generation is effective at producing stories with a logically-sound flow of events, but it can be limiting due to the rigidity of its constraints and the high burden on the domain author to define story-world objects, initial states, and author and character goals. Giving the system the freedom to add objects and events to the story-world history arbitrarily can improve variety and reduce authorial burden, but risks leading to stories that seem jarringly contrived to the audience. I propose to use question-answering as the antidote to contrivance in a highly-generative interactive narrative system: By modeling the player's beliefs about the story world, inferring the implicit questions the player may be asking through their interactions, and answering those questions in a way consistent with the player's prior knowledge, a system could focus on creating cohesion in the ways that matter most to the player while accepting a degree of contrivance in the details that the player is likely to overlook.
Quagents: A Game Platform for Intelligent Agent
Authors: Brown Chris , Ferguson George , Barnum Peter , Hu Bo , Costello David
Published: October 2022

The Quagents system provides a flexible interface to the functionality of a game engine. The goal is to make interactive games a useful research and teaching vehicle for academics. Quagents is freely available, and runs under Unix/Linux, Windows, and Mac OS X. Intelligent agent controllers may be programmed in any language that supports sockets. A communications protocol between the controller and the bot resembles that between the high-level software and low-level controller of a mobile robot. More complex APIs may be built on the protocol that support complex interactions like trading. We describe the Quagent architecture and research and teaching applications.
Towards Learned Anticipation in Complex Stochastic Environments
Authors: Darken Christian
Published: October 2022

I describe a novel methodology by which a software agent can learn to predict future events in complex stochastic environments. It is particularly relevant to environments that are constructed specifically so as to be able to support highperformance software agents, such as video games. I present results gathered from a first prototype of our approach. The technique presented may have applications that range beyond improving agent performance, in particular to user modeling in the service of automated game testing.
Scalable Solutions for Interactive Virtual Humans That Can Manipulate Objects
Authors: Kallmann Marcelo
Published: October 2022

This paper presents scalable solutions for achieving virtual humans able to manipulate objects in interactive virtual environments. The scalability trades computational time with the ability of addressing increasingly difficult constraints. In time-critical environments, arm motions are computed in few milliseconds using fast analytical Inverse Kinematics. For other types of applications where collision-free motions are required, a randomized motion planner capable of generating motions of average complexity in about a second of computation time is employed. The steps required for defining and computing different types of manipulations are described in this paper.
Controlling Unreal Tournament 2004 Bots with the Logic-Based Action Language GOLOG
Authors: Jacobs Stefan , Ferrein Alexander , Lakemeyer Gerhard
Published: October 2022

Computer games and the accompanying entertainment industry branch has become a major market factor. AI techniques are successfully applied to tasks like path planning or intelligent swarm behavior. On the decision-making level the state of the art are state machines with a xed set of behaviors. The perception of the computer player is perfect. They exactly know where the other players are located, even if they cannot see them. This approach seems to be limited for intelligent decision making. Therefore, we propose another approach. We use a variant of the logic-based action language GOLOG for implementing so-called game bots in the UNREAL TOURNAMENT 2004 environment. First results show that we can compete with the omniscience game bots in the Unreal domain.
Interactive Story Writing Using ScriptEase
Authors: Schaeffer Jonathan , Carbonarro Mike , Cutumisu Maria , McNaughton Matthew , Onuczko Curtis , Roy Thomas , Szafron Duane
Published: October 2022

ScriptEase is a tool for writing interactive stories in roleplaying games that frees the author from doing explicit computer programming. A story is created by selecting and customizing patterns for the plot, encounters, behaviors and conversations. It has been implemented as a front end to BioWare's Neverwinter Nights game. We will describe our experiences using ScriptEase as part of a high school English curriculum.
Using Multi-Agent System Technologies in Risk Bots
Authors: Johansson Stefan , Olsson Fredrik
Published: October 2022

We present a multi-agent architecture for playing the game of Risk which is a multi-player board game in which the players control armies and try to conquer the world through attacking each others territories. Our solution puts an agent in every territory, and let them negotiate about what actions to priori- tize in the phases of placing armies, attack, and fortify. The results of a tournament of 13 participating bots, shows out- standing results for our solution, MA R S which ends first or second (out of six participants) in 507 out of 792 matches.
Incorporating Advice into Neuroevolution of Adaptive Agents
Authors: Yong Chern , Stanley Kenneth , Miikkulainen Risto , Karpov Igor
Published: October 2022

Neuroevolution is a promising learning method in tasks with extremely large state and action spaces and hidden states. Recent advances allow neuroevolution to take place in real time, making it possible to e.g. construct video games with adaptive agents. Often some of the desired behaviors for such agents are known, and it would make sense to prescribe them, rather than requiring evolution to discover them. This paper presents a technique for incorporating human-generated advice in real time into neuroevolution. The advice is given in a formal language and converted to a neural network structure through KBANN. The NEAT neuroevolution method then incorporates the structure into existing networks through evolution of network weights and topology. The method is evaluated in the NERO video game, where it makes learning faster even when the tasks change and novel ways of making use of the advice are required. Such ability to incorporate human knowledge into neuroevolution in real time may prove useful in several interactive adaptive domains in the future.
A Framework for Summarizing Game Experiences as Narratives
Authors: Cheong Yun-Gyung , Young R.
Published: October 2022

Online role playing games may be enjoyed periodically for months or years at a time. Players often log onto the game and play for a short time, and then log off for a long period of time before returning to the game, even though events continue in the persistent game world. Providing a summary of events that have occurred while a player was away from the game would help the player maintain game context. In this paper, we present a computational framework that generates a summary from game log messages.
Modeling Self-Adapting and Multicultural Non-Player Characters in Artificial Worlds
Authors: Marin Samuel
Published: October 2022

Modeling the behavior of Non-Player Characters (NPCs) so that they can act properly in an artificial world is a difficult, yet essential task in the development of modern computer games, often requiring never- ending adjustments of handwritten rules in a way to enable NPCs to perform well in the game world. This pa- per introduces an evolutionary method relying on high-level rule-based behaviors, genetic algorithms and speciation. By interconnecting these three areas, we pro- pose a technique that has the potential to provide multicultural NPCs with the ability to adapt themselves to an artificial worl
A Testbed for Evaluating AI Research Systems in Commercial Games
Authors: Aha David , Molineaux Matthew , Moore Philip
Published: October 2022

Many AI researchers want to test the utility of their prototypes in complex task environments, such as those defined by commercial gaming simulators. Also, many developers of commercial games need to solve tasks (e.g., game balancing, providing rational agent behaviors) that can be addressed by these systems. However, integrating them with gaming simulators requires substantial effort. We will demonstrate TIELT, a testbed designed to assist with evaluating research prototypes in these task environments.
Mixing Story and Simulation in Interactive Narrative
Authors: Riedl Mark , Stern Andrew , Dini Don
Published: October 2022

Simulation is a common feature in computer entertainment. However, in computer games simulation and story are often kept distinct by interleaving interactive play and cut scenes. We describe a technique for an interactive narrative system that more closely integrates simulation and storyline. The technique uses a combination of semi-autonomous character agents and high-level story direction. The storyline is decomposed into directives to character agents to achieve particular world states. Otherwise, character agents are allowed to behave autonomously. When the player’s actions create inconsistency between the simulation state and storyline, the storyline is dynamically adapted and repaired to resolve any inconsistencies.
Dynamic Generation of Dilemma-Based Interactive Narratives
Authors: Barber Heather , Kudenko Daniel
Published: October 2022

This paper presents a system which automatically generates interactive stories. These are focused on dilemmas in order to create dramatic tension. The system is provided with knowledge of generic story actions and dilemmas based on those cliches encountered in many of today’s soap operas. The story designer is only required to provide genre specific storyworld knowledge, such as information on characters and their relations, locations and actions. These dilemmas and story actions are instantiated for the given storyworld and a story planner creates sequences of actions that each lead to a dilemma for a character (who can be the user). The user interacts with the story by making decisions on relevant dilemmas and by freely choosing their own actions. Using this input the system chooses and adapts future story lines according to the user's past behaviour.
A Lightweight Intelligent Virtual Cinematography System for Machinima Production
Authors: Elson David , Riedl Mark
Published: October 2022

Machinima is a low-cost alternative to full production filmmaking. However, creating quality cinematic visualizations with existing machinima techniques still requires a high degree of talent and effort. We introduce a lightweight artificial intelligence system, Cambot, that can be used to assist in machinima production. Cambot takes a script as input and produces a cinematic visualization. Unlike other virtual cinematography systems, Cambot favors an offline algorithm coupled with an extensible library of specific modular and reusable facets of cinematic knowledge. One of the advantages of this approach to virtual cinematography is a tight coordination between the positions and movements of the camera and the actors.
SORTS: A Human-Level Approach to Real-Time Strategy AI
Authors: Wintermute Sam , Xu Joseph , Laird John
Published: October 2022

We developed knowledge-rich agents to play real-time strategy games by interfacing the ORTS game engine to the Soar cognitive architecture. The middleware we developed supports grouping, attention, coordinated path finding, and FSM control of low-level unit behaviors. The middleware attempts to provide information humans use to reason about RTS games, and facilitates creating agent behaviors in Soar. Agents implemented with this system won two out of three categories in the AIIDE 2006 ORTS competition.
Learning a Table Soccer Robot a New Action Sequence by Observing and Imitating
Authors: Zhang Dapeng , Nebel Bernhard
Published: October 2022

We define ShapeShifting programmes as interactive and reconfigurable moving image productions that adapt their content, on the fly, to suit the preferences of the viewers or engagers. They are automatically edited at the time of delivery. We have developed a paradigm, a computational model and an accompanying software system for the creation and delivery of ShapeShifting Screen Media programmes. These are all generic - genre and production independent. They employ AI techniques including: logic programming, ontologies, symbolic representations, normative statements, and constraint satisfaction heuristics. This paper outlines the Authoring Environment component of the aforementioned software system.
Level Annotation and Test by Autonomous Exploration: Abbreviated Version
Authors: Darken Christian
Published: October 2022

This paper proposes the use of an autonomous exploring agent to generate and annotate the waypoint graph as an off-line process during level development. The explorer incrementally generates the waypoint graph as it explores the level via the same motion model used for player movement, and then revisits the waypoints to annotate them using image-based techniques. Points where the explorer becomes stuck or falls off of the level are flagged for later investigation by a level designer.
A Comparative Analysis of Story Representations for Interactive Narrative Systems
Authors: Magerko Brian
Published: October 2022

Interactive narrative is a blossoming field, with new systems and approaches being developed each year. However, despite this burgeoning array of designs, little work has been done in empirically evaluating and comparing them (Weyhrauch 1997; Nelson and Mateas 2005; Magerko 2007). Such a comparison is complicated by the fact that different systems are used to create different narratives; it is difficult to differentiate between the authored content and the system capabilities in an empirical setting. This paper proposes a comparative analysis approach to determining the strengths and weaknesses of interactive narrative systems. Such a comparison is a positive first step towards understanding and evaluating current and future interactive narrative systems.
Player Autonomy versus Designer Intent: A Case Study of Interactive Tour Guides
Authors: Roberts David , Cantino Andrew , Isbell Charles
Published: October 2022

We explore the tradeoff between player autonomy and designer intent by simulating a system of autonomous museum tour guides. Visitors may have different art preferences or may wish to visit different exhibits on multiple visits. Often, these desires conflict. For example, visitors may wish to see the museum's most popular work, but that could cause congestion, ruining the experience. Thus, our task is to build a set of guides that can satisfy their visitors' goals while also providing quality experiences for all. We present the results of a case study indicating that there is a space in the design spectrum between fully author-controlled narrative and complete player autonomy that reduces the frequency of bad experiences while allowing visitors to realize their own goals.
Emotionally Driven Natural Language Generation for Personality Rich Characters in Interactive Games
Authors: Strong Christina , Mehta Manish , Mishra Kinshuk , Jones Alistair , Ram Ashwin
Published: October 2022

Natural Language Generation for personality rich characters represents one of the important directions for believable agents research. The typical approach to interactive NLG is to hand-author textual responses to different situations. In this paper we address NLG for interactive games. Specifically, we present a novel template-based system that provides two distinct advantages over existing systems. First, our system not only works for dialogue, but enables a character's personality and emotional state to influence the feel of the utterance. Second, our templates are resuable across characters, thus decreasing the burden on the game author. We briefly describe our system and present results of a preliminary evaluation study.
A Demonstration of SQUEGE: A CRPG Sub-Quest Generator
Authors: Onuczko Curtis , Szafron Duane , Schaeffer Jonathan , Cutumisu Maria , Siegel Jeff , Waugh Kevin , Schumacher Allan
Published: October 2022

Scripting the plot in a computer role-playing game requires a large number of scripts that are difficult to program, track and maintain. Game adventures often include simple plots, called side-quests, that are independent from the main plot. Side-quests are important, as they add value to the open-world appeal of the game (e.g., for acquiring experience or resources), but they still need scripts. We have designed a tool to aid in the rapid creation of side-quests. The game designer provides the game setting and a list of objects in the setting. Our tool uses this information to create an outline for the side-quests. Then we use ScriptEase, a generative design pattern tool, to generate scripts from the side-quest outlines for the Neverwinter Nights game. A game designer can also adapt these outlines after the generation process, to add value such as humour to the side-quests.
Intelligent Story Architecture for Training (ISAT)
Authors: Stensrud Brian , Holt Lisa , Magerko Brian
Published: October 2022

The Interactive Story Architecture for Training (ISAT) is designed to address the limitations of computer games for advanced distributed learning (ADL) and to fully realize the potential of games to become engaging and individualized training environments. The central component of the ISAT architecture is an intelligent director agent responsible for individualizing the training experience. To achieve this, the director tracks the trainee's demonstration of knowledge and skills during the training experience. Using that information, the director plays a role similar to that of a schoolhouse trainer, customizing training scenarios to meet individual trainee needs. The director can react to trainee actions within a scenario, dynamically adapting the environment to the learning needs of trainee as well as the dramatic needs of the scene.
Authoring Environment for ShapeShifting Screen Media Productions
Authors: Ursu Marian , Cook Jonathan , Zsombori Vilmos , Kegel Ian , Stevens Tim , Mayer Harald , Hausenblas Michael
Published: October 2022

We define ShapeShifting programmes as interactive and reconfigurable moving image productions that adapt their content, on the fly, to suit the preferences of the viewers or engagers. They are automatically edited at the time of delivery. We have developed a paradigm, a computational model and an accompanying software system for the creation and delivery of ShapeShifting Screen Media programmes. These are all generic - genre and production independent. They employ AI techniques including: logic programming, ontologies, symbolic representations, normative statements, and constraint satisfaction heuristics. This paper outlines the Authoring Environment component of the aforementioned software system.
Modeling Culturally and Emotionally Affected Behavior
Authors: Bulitko Vadim , Solomon Steven , Gratch Jonathan , Van Lent Michael
Published: October 2022

Culture and emotions have a profound impact on human behavior. Consequently, high-fidelity simulated interactive environments (e.g., trainers and computer games) that involve virtual humans must model socio-cultural and emotional effects on agent behavior. In this paper we discuss two recently fielded systems that do so independently: Culturally Affected Behavior (CAB) and EMotion and Adaptation (EMA). We then propose a simple language that combines the two systems in a natural way thereby enabling simultaneous simulation of culturally and emotionally affected behavior. The proposed language is based on matrix algebra and can be easily implemented on single- or multi-core hardware with an off-the-shelf matrix package (e.g., MATLAB or a C++ library). We then show how to extend the combined culture and emotion model with an explicit representation of religion and personality profiles.
Effects of Communication on the Evolution of Squad Behaviours
Authors: Doherty Darren , O’Riordan Colm
Published: October 2022

As the non-playable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviours that are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective team behaviours for shooter games. GP has been used to evolve teams capable of defeating a single powerful enemy agent in a number of environments without the use of any explicit team communication. The aim of this paper is to explore the effects of communication on the evolution of effective squad behaviours. Thus, NPCs are given the ability to communicate their perceived information during evolution. The results show that communication between team members enables an improvement in average team effectiveness.
Dynamic Expansion of Behaviour Trees
Authors: Florez-Puga Gonzalo , Gómez-Martín Marco , Díaz-Agudo Belén , González-Calero Pedro
Published: October 2022

Artificial intelligence in games is typically used for creating player's opponents. Manual edition of intelligent behaviors for Non-Player Characters (NPCs) of games is a cumbersome task that needs experienced designers. Our research aims to assist designers in this task. Behaviours typically use recurring patterns, so that experience and reuse are crucial aspects for behavior design. The use of hierarchical state machines allows working on different abstraction levels, sharing transitions and reusing pieces from the more detailed levels. However, the static nature of the design process does not release the designer from the burden to completely specify each behaviour. Our approach applies Case-Based Reasoning (CBR) techniques to retrieve and reuse stored behaviors represented as hierarchical state machines (actually, behaviour trees). In this paper we focus on dynamic retrieval of behaviours taking into account the world state and the underlying goals to select the most appropriate state machine to guide the NPC behaviour. The global behaviour of the NPC is dynamically built in run time querying the CBR system. We exemplify our approach through a serious game, developed by our research group, with gameplay elements from First-Person Shooter (FPS) games.