bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Visualizing Design Gradients for Human Designers
Authors: Guzdial Matthew , Sturtevant Nathan , Yang Carolyn
Published: October 2022

Mixed-initiative Procedural Content Generation (PCG) refers to tools or systems in which a human designer works with an algorithm to produce game content. This area of research remains relatively under-explored, with the majority of mixed-initiative PCG level design systems using a common set of search-based PCG algorithms. In this paper, we introduce a mixed-initiative tool employing Exhaustive PCG (EPCG) for puzzle level design to further explore mixed-initiative PCG. We run an online human subject study in which individuals use the tool with an EPCG component turned on or off. Our analysis of the results demonstrates that, although a majority of users did not prefer the tool, it made the level design process significantly easier, and that the tool impacted the subjects' design process. This paper describes the study results and draws lessons for mixed-initiative PCG tool design.
Challenges in Generating Juice Effects for Automatically Designed Games
Authors: Johansen Mads , Cook Michael
Published: October 2022

Automated game design research is usually most concerned with the mechanics and systems of a game, while aesthetics and effects are left to a minimum, if they are considered at all. In this project we integrate 'Squeezer', a tool for generating visual and audio effects (sometimes called 'juice') for games, with 'Puck', an automated game designer. The resulting hybrid system can design games and then generate appropriate sets of effects, making it the first automated game design system that directly engages with 'juiciness' in design. We support this work with a user study, measuring player responses to games with simple animations, effects automatically designed and arranged by Puck, and effects designed and arranged by an expert human designer. We then dissect the engineering challenges presented by integrating the two systems, and the new research questions raised by applying juice through automated game design systems.
Winnow: A Domain-Specific Language for Incremental Story Sifting
Authors: Kreminski Max , Dickinson Melanie , Mateas Michael
Published: October 2022

Story sifters attempt to automatically or semi-automatically extract nuggets of compelling narrative content from vast chronicles of game or simulation events. Though sifting has successfully been used to enable novel computational narrative play experiences, its utility is limited by the fundamentally retrospective nature of existing sifters, which can only recognize storyful event sequences once they have fully played out. To address this limitation, we introduce Winnow: a domain-specific language for specifying story sifting patterns that can be executed incrementally to detect potentially storyful event sequences while they are still playing out. We evaluate Winnow by applying it to several specific use cases and show that it is well-suited to the implementation of prospective as well as retrospective narrative intelligence.
Content Reinjection for Super Metroid
Authors: Mawhorter Ross , Aytemiz Batu , Karth Isaac , Smith Adam
Published: October 2022

Academic procedural content generation (PCG) efforts often yield plausible game content but stop short of fully integrating it into the games that inspired it. This misses opportunities to discover game- and platform-specific constraints that were previously ignored in evaluations of playability (e.g. how invisible objects in a level design are used to explicitly control camera movement). Grappling with existing games can ensure that the PCG community is solving realistic problems, rather than convenient abstractions of them. In this paper, we use technical knowledge from the ROM hacking community along with the WaveFunctionCollapse example-driven generator to reinject controllably-generated level designs into the commercial Super Metroid ROM image (rather than a clone) for execution on physical Nintendo hardware. Our work charts a path for more realistic evaluation of the playability of generated content and highlights challenges for deploying generative methods. These challenges can spark a conversation about the ways that abstractions are used in PCG research.
tile2tile: Learning Game Filters for Platformer Style Transfer
Authors: Sarkar Anurag , Cooper Seth
Published: October 2022

We present tile2tile, an approach for style transfer between levels of tile-based platformer games. Our method involves training models that translate levels from a lower-resolution sketch representation based on tile affordances to the original tile representation for a given game. This enables these models, which we refer to as filters, to translate level sketches into the style of a specific game. Moreover, by converting a level of one game into sketch form and then translating the resulting sketch into the tiles of another game, we obtain a method of style transfer between two games. We use Markov random fields and autoencoders for learning the game filters and apply them to demonstrate style transfer between levels of Super Mario Bros, Kid Icarus, Mega Man and Metroid.
Effects of Player-Level Matchmaking Methods in a Live Citizen Science Game
Authors: Stoneman Alexander D. , Miller Josh Aaron , Cooper Seth
Published: October 2022

Citizen science games must balance task difficulty with player skill to ensure optimal engagement and performance. This issue has been previously addressed via player-level matchmaking, a dynamic difficulty adjustment method in which player and level ratings are used to present levels best suited for players' individual abilities. However, this work has been done in small, isolated test games and left out potential techniques that could further improve player performance. Therefore, we examined the effects of player-level matchmaking in Foldit, a live citizen science game. An experiment with 221 players demonstrated that dynamic matchmaking approaches led to significantly more levels completed, as well as a more challenging highest level completed, compared to random level ordering, but not greater than a static approach. We conclude that player-level matchmaking is worth consideration in the context of live citizen science games, potentially paired with other dynamic difficulty adjustment methods.
Never a Dull Moment: Believable Dynamic Character Beat Generation between Game World Events
Authors: Mitchell Kyle , Pettijohn Camden , McCoy Joshua
Published: October 2022

This work presents a heterogeneous system using ABL, CiF, and a virtual game world to demonstrate how dynamic character beats can be used to preserve believability across game world events. We describe the individual components used in our system and detail how each component contributes to the alignment of physical and mental contexts of NPC agents. Finally, we provide a brief account of the demo experience showcasing our system’s capabilities.
Adding Smart Opponents to a First-Person Shooter Video Game through Evolutionary Design
Authors: Overholtzer C. , Levy Simon
Published: October 2022

We demonstrate how a first-person shooter (FPS) video game can be made more fun and challenging by replacing the hard-wired behavior of opponents with behaviors evolved via an evolutionary algorithm. Using the opensource FPS game Cube as a platform, we replaced the agents' (opponents) hard-wired behavior with binary DNA supporting a much richer variety of agent responses. Survival-of-the-fittest ensured that only those agents whose DNA allowed them to avoid being killed by the human player would continue on to the next "generation" (game). Mutating the DNA of the survivors provided enough variability in behavior to make the agent's actions unpredictable. Our demo will show how this approach produces an increasingly challenging level of play, more fine-tuned to the skills of an individual human player than the traditional approach using pre-programmed levels of difficulty or simply adding more opponents.
Building Robust Planning and Execution Systems for Virtual Worlds
Authors: Dini Don , Van Lent Michael , Carpenter Paul , Iyer Kumar
Published: October 2022

Planning and execution systems have been used in a wide variety of systems to create practical and successful automation. They have been used for everything from performing scientific research on the surface of Mars to controlling enemy characters in video games to performing military air campaign planning. After reviewing past work on these various planning and execution systems, we believe that most lack one or more key components contained in another system. To enable future researchers to build more complete systems, and avoid possible serious system failure, we identify the major technical problems any implementer of such a system would have to face. In addition we cite recent solutions to each of these technical problems. We limit our focus to planning and execution for virtual worlds and the unique problems faced therein.
Intelligent Tutoring Systems for Commercial Games: The Virtual Combat Training Center Tutor and Simulation
Authors: Murray William
Published: October 2022

Game manuals and tutorial scenarios are insufficient for new players to learn games of deep complexity such as highly realistic tactical simulations of modern battlefields. Adding post-game after-action reviews improves the situation, but these typically do not provide guidance during the mission and tend to focus on quantitative feedback, rather than specifics about what the player did wrong and how to improve. Intelligent tutoring system (ITS) technology provides a higher level of interactivity and a more specific qualitative analysis to guide players during game play. This use of an AI technology is demonstrated with the integration of an ITS component with the tactical simulation Armored Task Force (ATF) resulting in a combined system called the the Virtual Combat Training Center (V-CTC). V-CTC simulates the Army's combat training center at Fort Irwin and its instructors, called observer / controllers. The ATF game itself was modified to send an event stream over TCP-IP sockets to the ITS component, which interprets the events and acts accordingly. V-CTC was originally intended for a military context: either classroom use, field instruction, or embedded deployment. However, in non-military games, tutors (or non-player characters acting in that role) may well enhance the gaming experience of players. Such players might otherwise become frustrated with learning very challenging games, or simply fail to appreciate the tactical possibilities and depth of strategy possible in a well-designed game.
Designing a Reinforcement Learning-Based Adaptive AI for Large-Scale Strategy Games
Authors: Madeira Charles , Corruble Vincent , Ramalho Geber
Published: October 2022

This paper investigates the challenges posed by the application of reinforcement learning to large-scale strategy games. In this context, we present steps and techniques which synthesize new ideas with state-of-the-art techniques from several areas of machine learning in a novel integrated learning approach for this kind of games. The performance of the approach is demonstrated on the task of learning valuable game strategies for a commercial wargame.
Plausible Environment Reconstruction Using Bayesian Networks
Authors: Price Robert , Goodwin Scott
Published: October 2022

To save precious time and space, many games and simulations use static terrain and fixed (or random) reconstruction of areas that a player leaves and later revisits. This can result in noticeable differences between the reconstructed area and the player's recollections (or expectations). These differences can lessen a player's immersion in the game, or the usefulness of the simulation. We propose an approach for environment reconstruction that uses a Bayesian Network to quickly and easily calculate likely effects that external factors have on the environment. The reconstruction of revisited areas becomes less disconcerting and permits the incorporation of plausible changes based on unobserved, yet reasonably expected, events that could have occurred during the player's absence.
AI Wall Building in Empire Earth II
Authors: Teich Tara , Davis Ian
Published: October 2022

Real-Time Strategy games are among the most popular genres of commercial PC games, and also have widely applicable analogs in the field of Serious Games such as military simulations, city planning, and other forms of simulation involving multi-agent coordination and an underlying economy. One of the core tasks in playing a traditional Real-Time Strategy game is building a base in an effective manner and defending it well. Creating an AI that can construct a successful wall was one of the more challenging areas of development on Empire Earth II, as building a wall requires analysis of the terrain and techniques from computational geometry. An effective wall can hold off enemy troops and keep battles away from the delicate economy inside the base.
Believable and Reactive Crowds in Next Generation Games
Authors: Kruszewski Paul
Published: October 2022

The creation of life-like believable characters is emerging as the central focus of next-generation game development and is viewed as critical to obtaining true mass-market appeal. Reactive crowd simulation is a critical component in creating believable character. The demo runs on a Xbox 360 and shows the following crowd behaviors. A typical downtown is populated with pedestrians and vehicles. The pedestrians mill about the town staying on the sidewalk and cross only at street corners (although some decide will jay walk from time to time). Occasionally, they will stop to talk with one another. Cars drive around the city and obey the traffic lights. The ambient behaviour is interrupted by traumatic event; in this case a terrorist pulls out a run and starts firing at the crowd who now move into a panic behaviour. Some crowd members run away immediately from the terrorist while others first cower in fear before running away. Crowd members who do not hear the weapon fire but see a panicking pedestrian may also panic or continue about their business depending on how cool they are. Vehicles also flee the terrorist and will drive on sidewalks and over pedestrians to save themselves.
Automatic Story Generation for Computer Role-Playing Games
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 track and maintain. Game adventures often have simple plots, called sub- quests, that are independent from the main plot. Sub-quests are important, as they add value to the open-world appeal of the game, but they still have to be scripted. We have created a prototype of a tool that helps by automatically producing design pattern specifications for sub-quests. The specifications can be entered into an existing tool, called ScriptEase, to generate scripting code for Neverwinter Nights adventures, without doing any manual scripting. The sub-quest patterns produced are logically consistent, ensuring the story can be completed by the player. The sub-quests are also designed to produce a better story by having the author adjust the amount of interactivity between the sub-quests. The entire process is done with little input from the author.
Wubble World
Authors: Hewlett Daniel , Hoversten Shane , Kerr Wesley , Cohen Paul , Chang Yu-Han
Published: October 2022

We introduce Wubble World, a virtual environment for learning situated language. In Wubble World children create avatars, called wubbles, which can interact with other other children's avatars through free-form spontaneous play or structured language games. Wubbles can also learn language from direct interaction with children, since the system uses principles from developmental psychology to restrict the complexity of this learning task: a shared attention model that includes deictic pointing, and a concept acquisition system that allows for rapid learning of new words from a limited number of exposures.Since we have complete knowledge of the state and structure of the virtual environment, we are able to track correspondences between utterances and the scene in which they are uttered. This sentence/scene corpus will be a valuable resource as we attempt to tackle more sophisticated language learning tasks, such as the acquisition of syntax and verb semantics from world dynamics.
Interactive Storytelling: A Player Modelling Approach
Authors: Thue David , Bulitko Vadim , Spetch Marcia , Wasylishen Eric
Published: October 2022

In recent years, the fields of Interactive Storytelling and Player Modelling have independently enjoyed increased interest in both academia and the computer games industry. The combination of these technologies, however, remains largely unexplored. In this paper, we present PaSSAGE (Player-Specific Stories via Automatically Generated Events), an interactive storytelling system that uses player modelling to automatically learn a model of the player's preferred style of play, and then uses that model to dynamically select the content of an interactive story. Results from a user study evaluating the entertainment value of adaptive stories created by our system as well as two fixed, pre-authored stories indicate that automatically adapting a story based on learned player preferences can increase the enjoyment of playing a computer role-playing game for certain types of players.
Automatic Rule Ordering for Dynamic Scripting
Authors: Timuri Timor , Spronck Pieter , Van den Heri Jaap
Published: October 2022

The goal of adaptive game AI is to enhance computer-controlled game-playing agents with (1) the ability to self-correct mistakes, and (2) creativity in responding to new situations. Dynamic scripting is a reinforcement learning technique that realises fast and reliable online adaptation of game AI. It employs knowledge bases which contain rules that can be included in game scripts. To be successful, dynamic scripting requires a mechanism to order the rules that are selected for scripts. So far, rule ordering was achieved by a manually-tuned priority value for each rule. In the present research, we propose three mechanisms to order rules automatically for dynamic scripting. We performed experiments in which we let dynamic scripting, using each of the three mechanisms, play against manually-designed tactics. Our results show that dynamic scripting with automatic rule ordering generates game AI that is at least as effective as dynamic scripting with manually-tuned priority values. Moreover, it has the ability to generate novel game AI with significantly increased effectiveness. The costs are a slight decrease in learning efficiency. So, we may conclude that automatic rule ordering is a valuable enhancement for dynamic scripting.
A Demonstration of ScriptEase Motivational Ambient and Latent Behaviors for Computer RPGs
Authors: Cutumisu Maria , Szafron Duane , Schaeffer Jonathan , Waugh Kevin , Onuczko Curtis , Siegel Jeff , Schumacher Allan
Published: October 2022

This demonstration describes the generation of ambient and latent NPC behavior scripts using generative behavior patterns with ScriptEase. Our behavior model supports behavior roles, a powerful mechanism that allows an NPC to change behavior sets during the story. Our motivation model for selecting ambient behaviors generates more realistic NPC behaviors and our novel collaboration protocol simplifies and extends the NPCs' collaborations with a broader range of NPCs. We demonstrate motivational ambient and latent behaviors for a guard NPC in a custom Neverwinter Nights game module. With ScriptEase, game authors can use, adapt, and create behavior patterns that generate complex, versatile, and engaging NPC behaviors
Agent Learning Using Action-Dependent Learning Rates in Computer Role-Playing Games
Authors: Cutumisu Maria , Szafron Duane , Bowling Michael , Sutton Richard
Published: October 2022

We introduce the ALeRT (Action-dependent Learning Rates with Trends) algorithm that makes two modifications to the learning rate and one change to the exploration rate of traditional reinforcement learning techniques. Our learning rates are action-dependent and increase or decrease based on trends in reward sequences. Our exploration rate decreases when the agent is learning successfully and increases otherwise. These improvements result in faster learning. We implemented this algorithm in NWScript, a scripting language used by BioWare Corp.’s Neverwinter Nights game, with the goal of improving the behaviours of game agents so that they react more intelligently to game events. Our goal is to provide an agent with the ability to (1) discover favourable policies in a multiagent computer role-playing game situation and (2) adapt to sudden changes in the environment.
Learning to be a Bot: Reinforcement Learning in Shooter Games
Authors: McPartland Michelle , Gallagher Marcus
Published: October 2022

This paper demonstrates the applicability of reinforcement learning for first person shooter bot artificial intelligence. Reinforcement learning is a machine learning technique where an agent learns a problem through interaction with the environment. The Sarsa(λ) algorithm will be applied to a first person shooter bot controller to learn the tasks of (1) navigation and item collection, and (2) combat. The results will show the validity and diversity of reinforcement learning in a first person shooter environment.
Stochastic Plan Optimization in Real-Time Strategy Games
Authors: Trusty Andrew , Santiago Ontañón Santiago , Ram Ashwin
Published: October 2022

We present a domain independent off-line adaptation technique called Stochastic Plan Optimization for finding and improving plans in real-time strategy games. Our method is based on ideas from genetic algorithms but we utilize a different representation for our plans and an alternate initialization procedure for our search process. The key to our technique is the use of expert plans to initialize our search in the most relevant parts of plan space. Our experiments validate this approach using our existing case based reasoning system Darmok in the real-time strategy game Wargus, a clone of Warcraft II.
An Intelligent IDE for Behavior Authoring in Real-Time Strategy Games
Authors: Virmani Suhas , Kanetkar Yatin , Mehta Manish , Ontañón Santiago , Ram Ashwin
Published: October 2022

Behavior authoring for computer games involves writing behaviors in a programming language and then iteratively refining them by detecting issues with them. The main bottlenecks are a) the effort required to author the behaviors and b) the revision cycle as, for most games, it is practically impossible to write a behavior for the computer game AI in a single attempt. The main problem is that the current development environments (IDE) are typically mere text editors that can only help the author by pointing out syntactical errors. In this paper we present an intelligent IDE (iIDE) that has the following capabilities: it allows the author to program initial versions of the behaviors through demonstration, presents visualizations of behavior execution for revision, lets the author define failure conditions on the existing behavior set, and select appropriate fixes for the failure conditions to correct the behaviors. We describe the underlying techniques that support these capabilities inside our implemented iIDE and the future steps that need to be carried out to improve the iIDE.We also provide details on a preliminary user study showing how the new features inside the iIDE can help authors in behavior authoring and debugging in a real time strategy game.
PaSSAGE: A Demonstration of Player Modelling in Interactive Storytelling
Authors: Thue David , Bulitko Vadim , Spetch Marcia Spetch
Published: October 2022

This demonstration presents PaSSAGE (Player-Specific Stories via Automatically Generated Events), an Artificial Intelligence system that uses player modelling to dynamically select the content of an interactive story. Through player modelling, PaSSAGE provides game designers with the opportunity to delay some of their design decisions to run-time, allowing further refinement based on an automatically learned model of its current player's preferences. The hands-on component of this demonstration allows players to interact with a PaSSAGE-created story while simultaneously observing its inner workings on a secondary screen.
Difficulty Scaling through Incongruity
Authors: Van Lankveld Giel , Spronck Pieter , Rauterberg Matthias
Published: October 2022

In this paper we discuss our work on using the incongruity measure from psychological literature to scale the difficulty level of a game online to the capabilities of the human player. Our approach has been implemented in a small game called Glove.
Improving Offensive Performance Through Opponent Modeling
Authors: Laviers Kennard , Sukthankar Gita , Aha David , Molineaux Matthew
Published: October 2022

Although in theory opponent modeling can be useful in any adversarial domain, in practice it is both difficult to do accurately and to use effectively to improve game play. In this paper, we present an approach for online opponent modeling and illustrate how it can be used to improve offensive performance in the Rush 2008 football game.  In football, team behaviors have an observable spatio-temporal structure, defined by the relative physical positions of team members over time; we demonstrate that this structure can be exploited to recognize football plays at a very early stage of the play using a supervised learning method.  Based on the teams' play history, our system evaluates the competitive advantage of executing a play switch based on the potential of other plays to increase the yardage gained and the similarity of the candidate plays to the current play.  In this paper, we investigate two types of play switches: 1) whole team and 2) subgroup.  Both types of play switches improve offensive performance, but modifying the behavior of only a key subgroup of offensive players yields greater improvements in yardage gained.
Constructing Game Agents from Video of Human Behavior
Authors: Li Nan , Stracuzzi David , Cleveland Gary , Konik Tolga , Shapiro Dan , Molineaux Matthew , Aha David , Ali Kamal
Published: October 2022

Developing computer game agents is often a lengthy and expensive undertaking. Detailed domain knowledge and decision-making procedures must be encoded into the agent to achieve realistic behavior. In this paper, we simplify this process by using the ICARUS cognitive architecture to construct game agents. The system acquires structured, high fidelity methods for agents that utilize a vocabulary of concepts familiar to game experts. We demonstrate our approach by first acquiring behaviors for football agents from video footage of college football games, and then applying the agents in a football simulator.
Concepts for Interactive Digital Storytelling: From Table-top to Game-AI
Authors: Van Velsen Martin , Williams Josh , Verhulsdonck Gustav
Published: October 2022

In the current environment of digital games and immersive role playing systems, we often overlook previous methods of conveying and experiencing narrative based entertainment. We present a fresh perspective on interactive digital storytelling systems based on table-top role playing games. Table-top games offer players the ability to negotiate and determine outcomes of a game with a referee. This cooperative strategy provides opportunities for studying new approaches to AI in interactive narrative. Using table-top role playing games as a model, we propose terminology and concepts that are different from the normally applied traditional literary or dramaturgical perspectives.
An Offline Planning Approach to Game Plotline Adaptation
Authors: Li Boyang , Riedl Mark
Published: October 2022

Role-playing games, and other types of contemporary video games, usually contain a main storyline consisting of several causally related quests. As players have different motivations, tastes and preferences, it can be beneficial to customize game plotlines. In this paper, we present an offline algorithm for adapting human-authored game plotlines for computer role-playing games to suit the unique needs of individual players, thereby customizing gaming experiences and enhancing re-playability. Our approach uses an plan refinement technique based on partial-order planning to (a) optimize the global structure of the plotline according to input from a player model, (b) maintain plotline coherence, and (c) facilitate authorial intent by preserving as much of the original plotline as possible. A theoretical analysis of the authorial leverage and a user study suggest the benefits of this approach.
Using Machine Translation to Convert Between Difficulties in Rhythm Games
Authors: Gold Kevin , Olivier Alex
Published: October 2022

A method is presented for converting between Guitar Hero difficulty levels by treating the problem as one of machine translation, with the different difficulties as different languages. The Guitar Hero I and II discs provide aligned corpora with which to train bigram-based language models and translation models. Given an Expert sequence, the model can create sequences of Hard, Medium, or Easy difficulty that retain the feel of the original, while obeying heuristics typical of those difficulties. Training the model requires a single pass through the corpus, while translation is quadratic in the length of the Expert sequence. The method outperforms a recurrent neural network in producing sequences that match the hand-designed levels. The method may make it easier for amateurs to produce content for the Rock Band Network.
A Non-Modal Approach to Integrating Dialogue and Action
Authors: Hanson Philip , Rich Charles
Published: October 2022

We have developed and demonstrated an experimental authoring and run-time tool, called Disco for Games, that supports the creation of games in which dialogue and action are integrated without the need for changing modes. This tool is based on collaborative discourse theory and hierarchical task networks, in which utterances are treated as actions, and has a number of additional benefits including better modeling of interruptions, automatic dialogue generation, plan recognition and automatic failure retry.
Adversarial Navigation Mesh Alteration
Authors: Hale David , Youngblood G.
Published: October 2022

Game environments are becoming more and more mutable from the actions of both Players and Non Player Characters (NPCs). However, current generation AI agents do not take advantage of the tactical abilities these mutable worlds provide. We propose a method to make the game agents aware of the mutability of the world by extending their repertoire of abilities to include world alteration commands and some evaluation functions, which determine when and where to alter the world for the greatest tactical gain. Primarily, our work focuses on the Adversarial Navigation Mesh Alteration (ANMA) algorithm, which evaluates potential changes to the map in adversarial environments from an attacker and defender point of view. We present an empirical evaluation of the ANMA algorithm in a Capture The Flag (CTF) simulation environment with several teams of agents. One group of agents (adaptive) lacks the ability to initiate world deformations, but they can respond and re-plan to take advantage of world modifications. The second team of agents (builders) can only generate additional paths through the world using the attacker portion of ANMA. The third team of agents (universal) is able to fully deform the world by generating new paths or removing existing paths using both the attacker and defender sections of ANMA. We evaluated these teams and observed that builder agents beat adaptive agents at a rate of 1.33 to 1. The more advanced universal agents beat adaptive agents at a rate of 2.75 to 1 and builder agents 1.4 to 1.
A Command Language for Taskable Virtual Agents
Authors: Langley Pat , Trivedi Nishant , Banister Matt
Published: October 2022

In this paper, we report progress on making synthetic characters more taskable. In particular, we present an English-like command language that lets one specify complex behaviors an agent should carry out in a virtual environment. We also report compilers that translate English commands into a formal notation and formal statements into procedures for Icarus, an agent architecture that supports reactive execution. To demonstrate the benefits of such taskability, we have integrated Icarus with Twig, which provides a simulated physical environment with humanoid agents. We use the command language to specify three complex activities, including responding to an object contingently, collecting and storing a set of objects, and negotiating with another agent in order to purchase an item. We also discuss related work on controlling synthetic characters, along with paths for additional research on taskability.
A Particle Model for State Estimation in Real-Time Strategy Games
Authors: Weber Ben , Mateas Michael , Jhala Arnav
Published: October 2022

A big challenge for creating human-level game AI is building agents capable of operating in imperfect information environments. In real-time strategy games the technological progress of an opponent and locations of enemy units are partially observable. To overcome this limitation, we explore a particle-based approach for estimating the location of enemy units that have been encountered. We represent state estimation as an optimization problem, and automatically learn parameters for the particle model by mining a corpus of expert StarCraft replays. The particle model tracks opponent units and provides conditions for activating tactical behaviors in our StarCraft bot. Our results show that incorporating a learned particle model improves the performance of EISBot by 10% over baseline approaches.
A Sparse Grid Representation for Dynamic Three-Dimensional Worlds
Authors: Sturtevant Nathan
Published: October 2022

Grid representations offer many advantages for path planning. Lookups in grids are fast, due to the uniform memory layout, and it is easy to modify grids. But, grids often have significant memory requirements, they cannot directly represent more complex surfaces, and path planning is slower due to their high granularity representation of the world. The speed of path planning on grids has been addressed using abstract representations, such as has been documented in work on Dragon Age: Origins. The abstract representation used in this game was compact, preventing permanent changes to the grid. In this paper we introduce a sparse grid representation, where grid cells are only stored where necessary. From this sparse representation we incrementally build an abstract graph which represents possible movement in the world at a high-level of granularity. This sparse representation also allows the representation of three-dimensional worlds. This representation allows the world to be incrementally changed in under a millisecond, reducing the maximum memory required to store a map and abstraction from Dragon Age: Origins by nearly one megabyte. Fundamentally, the representation allows previously allocated but unused memory to be used in ways that result in higher-quality planning and more intelligent agents.
Tactical Multi-Unit Pathplanning with GCLS
Authors: Nareyek Alexander , Goenawan Aditya
Published: October 2022

In this paper, we are considering advanced pathplanning problems that feature finding paths for multiple units subject to rich path constraints. Examples of richer constraints are the following of other units or to stay out of sight of a specific unit. Little attention has so far been given to richer pathplanning problem where the objective is more than reaching a specific destination from a starting point such that the path length is minimized. Richer pathplanning problems occur in many complex real-world scenarios, ranging from computer games to military movement planning. In this paper, a novel way to formally specify such problems and a new local-search strategy to solve such problems are proposed and demonstrated by a prototype implementation. Among the design goals are real-time computability as well as extendibility for new constraints and search heuristics.
Towards a Non-Disruptive, Practical and Objective Automated Playtesting Process
Authors: Tan Chek Tien , Johnston Andrew
Published: October 2022

Playtesting is the primary process that allows a game designer to access game quality. Current playtesting methods are often intrusive to play, involves much manual labor, and might not even portray the player's true feedback. This paper aims to alleviate these shortcomings by presenting the position that state of the art artificial intelligence techniques can construct automated playtesting systems that supplement or even substitute this process to a certain extent. Several potential research directions are proposed in this theme. A work-in-progress report is also included to demonstrate the conceptual feasibility of the potentials of this research area.
The Gold Standard: Automatically Generating Puzzle Game Levels
Authors: Williams-King David , Denzinger Jörg , Aycock John , Stephenson Ben
Published: October 2022

KGoldrunner is a puzzle-oriented platform game with dynamic elements. This paper describes Goldspinner, an automatic level generation system for KGoldrunner. Goldspinner has two parts: a genetic algorithm that generates candidate levels, and simulations that use an AI agent to attempt to solve the level from the player's perspective. Our genetic algorithm determines how "good" a candidate level is by examining many different properties of the level, all based on its static aspects. Once the genetic algorithm identifies a good candidate, simulations are performed to evaluate the dynamic aspects of the level. Levels that are statically good may not be dynamically good (or even solvable), making simulation an essential aspect of our level generation system. By carefully optimizing our genetic algorithm and simulation agent we have created an efficient system capable of generating interesting levels in real time.
Towards an Empathizing and Adaptive Storyteller System
Authors: Bae Byung Chull , Brunete Alberto , Malik Usman , Dimara Evanthia , Jermsurawong Jermsak , Mavridis Nikolaos
Published: October 2022

This paper describes our ongoing effort to build an empathizing and adaptive storyteller system. The system under development aims to utilize emotional expressions generated from an avatar or a humanoid robot in addition to the listener’s responses which are monitored in real time, in order to deliver a story in an effective manner. We conducted a pilot study and the results were analyzed in two ways: first, through a survey questionnaire analysis based on the participant’s subjective ratings; second, through automated video analysis based on the participant’s emotional facial expression and eye blinking. The survey questionnaire results show that male participants have a tendency of more empathizing with a story character when a virtual storyteller is present, as compared to audio-only narration. The video analysis results show that the number of eye blinking of the participants is thought to be reciprocal to their attention.
A Formal Game for Eliciting Story Structure from Authors
Authors: Roberts David , Hansen Andrew
Published: October 2022

We address the problem of determining the structure of a set of plot points for an interactive narrative. To do so, we define a formal two-player game where a computer can play with an author to learn the structural representation of the story. This technique will allow for authors unfamiliar, or uncomfortable, with mathematical structures to create the inputs interactive narrative algorithms require. We include the underlying mathematical theory as a foundation of our approach, and characterize it's effectiveness through a series of simulation experiments. Results indicate there is promise in using formal games to aid in authoring interactive narrative structures.
Organizers
Authors: Ware Stephen
Published: October 2022

Organizers and program committee for the Intelligent Narrative Technologies 2012 AIIDE Workshop
Kiting in RTS Games Using Influence Maps
Authors: Uriarte Alberto , Ontañón Santiago
Published: October 2022

Influence Maps have been successfully used in controlling the navigation of multiple units. In this paper, we apply the idea to the problem of simulating a kiting behavior (also known as ¨attack and flee'¨) in the context of real-time strategy (RTS) games. We present our approach and evaluate it in the popular RTS game StarCraft, where we analyze the benefits that our approach brings to a StarCraft playing bot.
Organizers
Authors: Buro Michael
Published: October 2022

List of organizers and program committee for the Artificial Intelligence in Adversarial Real-Time Games 2012 AIIDE Workshop.
Autonomy in Music-Generating Systems
Authors: Bown Oliver , Martin Aengus
Published: October 2022

The word autonomy is often used in the discussion of software-based music-generating systems. Whilst the term conveys a very clear concept — the sense of self-determination of a system — attempts to formalise autonomy are at an early stage, and the term is subject to a range of interpretations when practically applied. We consider how the evaluation of music-generating systems will be enhanced by a clearer understanding of autonomy and its application to music. We discuss existing definitions and approaches to quantifying autonomy and consider, through a series of examples, the information that is required in order to make precise formal judgements about autonomy, and the identification of relevant levels at which the principle of autonomy applies in music. We conclude that automated measures can supplement human evaluation of autonomy, but that (a) automated measures must be supported by sound reasoning about the features and timescales used in the measurement, and (b) they are improved by a having knowledge of the internal working of the system, rather than taking a black box approach. We consider multi-dimensional representations of system behaviour that may capture a richer sense of the notion of autonomy. Finally, we propose an approach to automatically probing music systems as a means of determining an autonomy `portrait'.
Computational Music Theory
Authors: Boenn Georg , Brain Martin , De Vos Marina , Ffitch John
Published: October 2022

One of the goals of the study of music theory is to develop sets of rules to describe different styles of music. By formalising these rules so that their semantics are machine intelligible, it is possible to use computers to reason about and analyse these rules -- computational music theory. Anton is an automatic composition system based on this approach. It formalises the rules of Renaissance Counterpoint using AnsProlog and uses an answer set solver to compose pieces. This paper discusses Anton, presenting the ideas behind the system and focusing on the challenges of modelling and synthesising rhythm.
Meta-Score, a Novel PWGL Editor Designed for the Structural, Temporal, and Procedural Description of a Musical Composition
Authors: Kuuskankare Mika
Published: October 2022

In this paper we introduce a prototype of ’meta-score’, a novel visual editor in PWGL, aimed at defining the structural, temporal and procedural properties of a musical composition. Meta-score is a music notation editor, thus, the score can be created manually by inputting the information using a GUI. However, meta-score extends the concept of a musical score so that the musical content can be defined not only manually but also procedurally. The composition is defined by placing scores (hence the name meta-score) on a timeline, creating dependencies between the objects, and defining the compositional processes associated with them. Meta-score presents the users with a three-stage compositional process beginning from the sketching of the overall structure along with the associated harmonic, rhythmic and melodic material; continuing with the procedural description of the composition and ending with the automatic production of the performance score. In this paper, we describe the present state of meta-score.
Embracing the Bias of the Machine: Exploring Non-Human Fitness Functions
Authors: Eigenfeldt Arne
Published: October 2022

Autonomous aesthetic evaluation is the Holy Grail of generative music, and one of the great challenges of computational creativity. Unlike most other computational activities, there is no notion of optimality in evaluating creative output: there are subjective impressions involved, and framing obviously plays a big role. When developing metacreative systems, a purely objective fitness function is not available: the designer is thus faced with how much of their own aesthetic to include. Can a generative system be free of the designer’s bias? This paper presents a system that incorporates an aesthetic selection process that allows for both human-designed and non-human fitness functions.
Music Design with Audio Oracle Using Information Rate
Authors: Dubnov Shlomo , Assayag Gerard
Published: October 2022

In the proposed demo we will present a method for design of musical composition using the Audio Oracle (AO) - a machine improvisation method that constructs and produces variation from a music recording using a graph of repeated factors found in the recording. The compositional / improvisation use of AO involves controlling the rate of recombination, the length of common history (length of the repeated factors) and selection of regions in the AO where the machine operates. One of the challenges in working with AO is understanding the generative potential of different audio materials and marking and allocating regions in a recording where AO should operate to achieve the desired musical result. Recently we introduced a novel analysis method "on top" of the AO structure that captures aspects of order and complexity that we call Information Rate. This measure, belonging broadly to a field of study know as Musical Information Dynamics, is related to Bense formulation of aesthetics in terms of entropy and compression. In the demo we will briefly explain the theory behind Audio Oracle and Information Rate and demonstrate a process of designing a composition / structured improvisation based on analysis of the AO graph. In contrast to the usual live interaction method where both the audio input to the oracle and the improvisation are done "on the fly", in this presentation the audio analysis will be done prior to the presentation, while during the talk we will show the process of planning a composition and then do a live performance with the AO based on this design.
Applying Learning by Observation and Case-Based Reasoning to Improve Commercial RTS Game AI
Authors: Robertson Glen
Published: October 2022

This document summarises my research in the area of Real-Time Strategy (RTS) video game Artificial Intelligence (AI). The main objective of this research is to increase the quality of AI used in commercial RTS games, which has seen little improvement over the past decade. This objective will be addressed by investigating the use of a learning by observation, case-based reasoning agent, which can be applied to new RTS games with minimal development effort. To be successful, this agent must compare favourably with standard commercial RTS AI techniques: it must be easier to apply, have reasonable resource requirements, and produce a better player. Currently, a prototype implementation has been produced for the game StarCraft, and it has demonstrated the need for processing large sets of input data into a more concise form for use at run-time.