bluuebunny/crossref_metadata_2025 huggingface dataset:
Explainable CLIP-Guided 3D-Scene Generation in an AI Holodeck
Authors: Mahdavi Goloujeh Atefeh , Smith Jason , Magerko Brian
Published: October 2022

This paper describes the AI Holodeck, a co-creative software prototype that creates virtual scenes from the user input text, inspired by the fictional Holodeck virtual reality device from the science fiction series Star Trek. This application collects common-sense knowledge from annotated datasets and reference images. It uses this knowledge to populate scenes with objects found in selected environments alongside those explicitly mentioned by the user. We present the system design of the AI Holodeck, and a proposed study to measure the effects of its visualizations on user perceptions of the system's creativity.
A Demonstration of Possibilistic Hierarchical Task Networks for Believable Agent Behavior
Authors: Warnick Braeden
Published: October 2022

Believably human non-player characters (NPCs) are inherent to immersive gameplay yet developing them remains a common challenge. When the success of many modern games relies on using bots in online services to replace real players, it’s vital for the bots to seem like they make decisions like real people. This paper explains the inspirations for, ideas behind, and base functionality of a demo made to build a foundation for future work that aims to establish a framework for developing believably human NPCs. This demo simply aims to show that possibility theory can work with hierarchical task networks (HTNs) by using custom utility functions. This reliance on utility functions also means that this work provides developers who use behavior selection systems that can use utility functions to decide between branches, a clear example of how to adopt possibilistic logic in their own work. Most importantly, since this logic is simplistic, it can be easily adopted by both hobbyists and experts.
Dialogue Generation in Character-Based Interactive Storytelling
Authors: Cavazza Marc , Charles Fred
Published: October 2022

In recent years, there has been significant progress in developing Interactive Storytelling systems, in particular in terms of the underlying AI techniques. There has been an emerging consensus on the AI approach, in particular the use of planning systems. However, these have concentrated on the generation of a sequence of narrative actions, staged through the behaviour of virtual actors. In order to achieve the long-term objective of implementing interactive media that would reproduce the aesthetic qualities of traditional films, most interactive storytelling systems are still missing the ability to generate dialogues between characters. We describe an extension of our Interactive Storytelling approach which integrates dialogue generation within narrative situations. Our unit of generation is the dialogue act (pair of utterances) and one main objective is to reproduce realistic dialogue phenomena based on implicit forms of expression. The emphasis is also on adopting a unified approach relating narrative representations to the linguistic form of the generated utterances. From a representation of active narrative goals and the emotional affinity between characters, our system is able to derive a set of parameters governing various aspects of the linguistic form to be generated. We illustrate this approach on several implemented examples.
Semi-Automated Gameplay Analysis by Machine Learning
Authors: Southey Finnegan , Xiao Gang , Holte Robert , Trommelen Mark , Buchanan John
Published: October 2022

While presentation aspects like graphics and sound are important to a successful commercial game, it is likewise important that the gameplay, the non-presentational behaviour of the game, is engaging to the player. Considerable effort is invested in testing and refining gameplay throughout the development process. We present an overall view of the gameplay management problem and, more concretely, our recent research on the gameplay analysis part of this task. This consists of an active learning methodology, implemented in software tools, for largely automating the analysis of game behaviour in order to augment the abilities of game designers. The SAGA-ML (semi-automated gameplay analysis by machine learning) system is demonstrated in a real commercial context, Electronic Arts’ FIFA’99 Soccer title, where it has identified exploitable weaknesses in the game that allow easy scoring by players.
Using Natural Language to Manage NPC Dialog
Authors: Kacmarcik Gary
Published: October 2022

In this document, we describe our work applying natural language (NL) technologies to improve non-player character (NPC) dialog interactions in games, specifically role-playing games (RPGs). Our approach is to adapt the standard dialog menu interaction so that the menu items are dynamically-generated during game runtime rather than scripted during development time. In our system, menu items are constructed by manipulating abstract semantic representations stored in the NPC knowledgebase, converting them into NL text, and then ranking them so that the most relevant items are placed at the top of the menu. We demonstrate our approach in the context of a small RPG.
Automatic Design of Balanced Board Games
Authors: Hom Vincent , Marks Joe
Published: October 2022

AI techniques are already widely used in game software to provide computer-controlled opponents for human players. However, game design is a more-challenging problem than game play. Designers typically expend great effort to ensure that their games are balanced and challenging. Dynamic game-balancing techniques have been developed to modify a game-engine’s parameters in response to user play. In this paper we describe a first attempt at using AI techniques to design balanced board games like checkers and Go by modifying the rules of the game, not just the rule parameters. Our approach involves the use of a commercial general game-playing (GGP) engine that plays according to rules that are specified in a general game-definition language. We use a genetic algorithm (GA) to search the space of game rules, looking for turn-based board games that are well balanced, i.e., those that the GGP engine in self-play finds equally hard to win from either side and rarely draws. The GA finds better games than a random-search strategy that uses equivalent computational effort.
Memory-Efficient Abstractions for Pathfinding
Authors: Sturtevant Nathan
Published: October 2022

From an academic perspective there has been a lot of work on using state abstraction to speed path planning. But, this work often does not directly address the needs of the game development community, specifically for mechanisms that will fit the limited memory budget of most commercial games. In this paper we bring together several related pieces of work on using abstraction for pathfinding, showing how the ideas can be implemented using a minimal amount of memory. Our techniques use about 3% additional storage to compute complete paths up to 100 times faster than A*.
Hierarchical Petri Nets for Story Plots Featuring Virtual Humans
Authors: Daniel Balas Daniel Balas , Brom Cyril , Abonyi Adam , Gemrot Jakub
Published: October 2022

Petri Nets can be used for a retrospective analysis of a computer game story, for representing plots in serious games as well as for monitoring the course of the story, as recently demonstrated by several authors. It was not clear, however, whether Petri Nets can be used for representing plots and for their unfolding in games that feature large worlds inhabited by virtual humans. This paper demonstrates that this is indeed possible, presenting several scenarios from the serious game Karo, an ongoing project, which features both virtual humans driven by reactive planning and a story manager unfolding story plots represented and controlled by Petri Nets. However, a specific refinement of Petri Nets, a hierarchical model capitalising on Timed Coloured Petri Nets and non-deterministic FSMs, had to be developed for this purpose. This refinement is described here, including general discussion showing why individual features of this refinement are needed - directions for game developers considering whether to use this technique, or not.
Simulation-Based Story Generation with a Theory of Mind
Authors: Chang Hsueh-Min , Soo Von-Wun
Published: October 2022

Emergent narrative refers to simulation-based systems in which stories emerge from the autonomous interactions among character agents and/or the human player. Despite its advantages in interactive games, emergent narrative attracts concern about whether complex narratives emerge from arbitrary interactions. This problem can be alleviated if the character agents act according to what a story should require. Based on the observation that deliberate influence on others' minds constitutes the fabric of most stories, we propose that the capability for social influence is crucial for narrative agents. This paper presents a specialized planning technique called social planning, which allows a character agent to achieve its goal by reasoning about other characters' minds and influencing their actions. A prototype system based on social planning agents succeeds in generating simplified variations of Shakespeare's Othello through simulation.
The Rise of Potential Fields in Real Time Strategy Bots
Authors: Hagelbäck Johan , Johansson Stefan
Published: October 2022

Bots for Real Time Strategy (RTS) games are challenging to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. We show that the use of potential fields for implementing a bot for a real time strategy game gives us a very competitive, configurable, and non-conventional solution.
Offline Planning with Hierarchical Task Networks in Video Games
Authors: Kelly John-Paul , Botea Adi , Koenig Sven
Published: October 2022

Artificial intelligence (AI) technology can have a dramatic impact on the quality of video games. AI planning techniques are useful in a wide range of game components, including modules that control the behavior of fully autonomous units. However, planning is computationally expensive, and the CPU and memory resources available to game AI modules at runtime are scarce. Offline planning can be a good strategy to avoid runtime performance bottlenecks. In this work, we apply hierarchical task network (HTN) planning to video games. We describe a system that computes plans offline and then represents them as game scripts. This can be seen as a form of generating game scripts automatically, replacing the traditional approach of composing them by hand. We apply our ideas to the commercial game The Elder Scrolls IV: Oblivion, with encouraging results. Our system generates scripts automatically at a level of complexity that would require a great human effort to achieve.
OpenNERO: A Game Platform for AI Research and Education
Authors: Karpov Igor , Sheblak John , Miikkulainen Risto
Published: October 2022

OpenNERO is an open source game platform designed for game AI research. The software package combines features commonly available in modern game engines (such as 3D graphics, physics simulation, 3D audio rendering, networked play, and a powerful scripting interface) with an easy to use API and tools for defining machine learning tasks, environments, and agents. Flexibility and ease of use of the system are demonstrated by following the process of creating a machine learning game from scratch. The scalability of the platform is tested through the implementation of the existing NERO machine learning game using the new tools.
Demonstrating Automatic Content Generation in the Galactic Arms Race Video Game
Authors: Hastings Erin , Guha Ratan , Stanley Kenneth
Published: October 2022

In most modern video games, content (e.g. models, levels, weapons, etc.) shipped with the game is static and unchanging, or at best, randomized within a narrow set of parameters. However, if game content could be constantly renewed, players would remain engaged longer. To realize this ambition, the content-generating NeuroEvolution of Augmenting Topologies (cgNEAT) algorithm automatically evolves novel game content based on player preferences, as the game is played. To demonstrate this approach, the Galactic Arms Race (GAR) video game, which incorporates cgNEAT, will be presented. In GAR, players pilot space ships and fight enemies to acquire novel particle system weapons that are evolved by the game. The live demo will show how GAR players can discover a wide variety of weapons that are not only novel, but also based on and extended from previous content that they preferred in the past. The implication of cgNEAT is that it is now possible to create games that generate their own content, potentially significantly reducing the cost of content creation and increasing the replay value of games.
Optimization of Platform Game Levels for Player Experience
Authors: Pedersen Chris , Togelius Julian , Yannakakis Georgios
Published: October 2022

We demonstrate an approach to modelling the effects of certain parameters of platform game levels on the players' experience of the game. A version of Super Mario Bros has been adapted for generation of parameterized levels, and experiments are conducted over the web to collect data on the relationship between level design parameters and aspects of player experience. These relationships have been learned using preference learning of neural networks. The acquired models will form the basis for artificial evolution of game levels that elicit desired player emotions.
CamOn: A Real-Time Autonomous Camera System
Authors: Burelli Paolo , Jhala Arnav
Published: October 2022

This demonstration presents CamOn, an autonomous camera control system for real-time 3D games. CamOn employs multiple Artificial Potential Fields (APFs), a robot motion planning technique, to control both the location and orientation of the camera. Scene geometry from the 3D environment contributes to the potential field that is used to determine position and movement of the camera. Composition constraints for the camera are modelled as potential fields for controlling the view target of the camera. CamOn combines the compositional benefits of constraint-based camera systems, and improves on real-time motion planning of the camera. Moreover, the recasting of camera constraints into potential fields is visually more accessible to game designers and has the potential to be implemented as a plug-in to 3D level design and editing tools currently available with games.
A Unified Spatial Representation for Navigation Systems
Authors: Ramsey Michael
Published: October 2022

The purpose of this paper is to outline the core components of a practical navigation system which uses a novel technique for spatial representation in a commercial entertainment product. This paper is based upon the navigation system developed for The World of Zoo (WOZ) by Blue Fang Games, LLC and published by THQ. WOZ placed the following requirement on our in game agents (which are animals, such as tigers and penguins): depending on the animals species they were required to locomote across land, water, exhibit the ability to climb and eventually to fly – all in a seamless manner. Animal locomotion in WOZ is driven by accumulating the root motion of multiple blended animations; this required a unique approach to the spatial representation of our environments. The system needed not only to take into account the defacto static environments that were created by the level designers, but also the dynamic structures that the animals use (depending on the players interactions at that particular moment). There was also the extra challenge of developing a system that was as straightforward as possible for level designers to work within. As Anthony J.D' Angelo so succinctly stated, “Don't reinvent the wheel. Just realign it.” It is with this sage advice in mind that we reevaluated traditional navigable representations, in conjunction with how our animals should move through their environments. As important as the navigation framework was to the development of WOZ, the way the thought processes developed preceding the implementation is also of interest; as the re-understanding of what navigation is composed of (in virtually any environment) guided our decisions through the design and implementation stages.
Generating Story Analogues
Authors: Riedl Mark , Leon Carlos
Published: October 2022

In this paper, we describe a computational system that generates story analogues based on previous stories. Unlike many previous works on story generation that attempt to produce a story artifact from a set of high-level specifications, we describe an approach that generates relatively novel stories by transforming existing stories to new contexts. We describe an algorithm for using analogical reasoning to find similarities between story contexts in order to map events from an existing story to a novel context.
Full 3D Spatial Decomposition for the Generation of Navigation Meshes
Authors: Hale D. Hunter , Youngblood G. Michael
Published: October 2022

We present a novel algorithm developed for decomposing world-space into arbitrary sided high-order polyhedrons for use as navmeshes or other techniques requiring 3D world spatial decomposition. The Adaptive Space Filling Volumes 3D (ASFV3D) algorithm works by seeding world-space with a series of unit cubes. Each cube is then provided with the opportunity to grow to its maximum extent before encountering an obstruction. ASFV3D implements an automatic subdividing system to convert cubes into higher-order polyhedrons while still maintaining the convex property. This allows for the generation of navigation meshes with high degrees of coverage while still allowing the use of large navigation regions—providing for easier agent navigation in virtual worlds. Compared to the Space-filling Volumes and Automatic Path Node Generation navigation mesh decomposition methods, ASFV3D provides more complete coverage and a less complex navigation mesh.
Evaluating the Authorial Leverage of Drama Management
Authors: Chen Sherol , Nelson Mark , Mateas Michael
Published: October 2022

A drama manager (DM) monitors an interactive experience, such as a computer game, and intervenes to shape the global experience so that it satisfies the author’s expressive goals without decreasing a player’s interactive agency. Most research on drama management has proposed AI architectures and provided abstract evaluations of their effectiveness; a smaller body of work has also evaluated the effect of drama management on player experience. Little attention has been paid, however, to evaluating the authorial leverage provided by a drama-management architecture: determining, for a given architecture, the additional non-linear story complexity a drama manager affords over traditional scripting methods. In this paper, we propose three  criteria for evaluating the authorial leverage of a DM: (1) the script-and-trigger complexity of the DM story policy; (2) the degree of   policy change given changes to story elements; and (3) the average story branching factor for DM policies versus script-and-trigger policies for stories of equivalent quality. We apply these criteria to declarative optimization-based drama management (DODM) by using decision tree learning to capture equivalent trigger logic, and show that DODM does in fact provide authorial leverage
Behavior Compilation for AI in Games
Authors: Orkin Jeff , Smith Tynan , Roy Deb
Published: October 2022

In order to cooperate effectively with human players, characters need to infer the tasks players are pursuing and select contextually appropriate responses. This process of parsing a serial input stream of observations to infer a hierarchical task structure is much like the process of compiling source code. We draw an analogy between compiling source code and compiling behavior, and propose modeling the cognitive system of a character as a compiler, which tokenizes observations and infers a hierarchical task structure. An evaluation comparing automatically compiled behavior to human annotation demonstrates the potential for this approach to enable AI characters to understand the behavior and infer the tasks of human partners.
A Demonstration of ScriptEase II
Authors: Church Matthew , Graves Eric , Duncan Jason , Lari Adel , Miller Robin , Desai Neesha , Zhao Richard , Carbonaro Mike , Schaeffer Jonathan , Sturtevant Nathan , Szafron Duane
Published: October 2022

This demonstration describes ScriptEase II, a tool that allows game story authors to generate scripts that control objects in video games by manipulating high level story patterns and game objects. ScriptEase II can generate scripting code for any game engine for which a translator is written. Currently there are translators for Neverwinter Nights and real Pinball games.
A Step Towards the Future of Role-Playing Games: The SpyFeet Mobile RPG Project
Authors: Reed Aaron , Samuel Ben , Sullivan Anne , Grant Ricky , Grow April , Lazaro Justin , Mahal Jennifer , Kurniawan Sri , Walker Marilyn , Wardrip-Fruin Noah
Published: October 2022

Meaningful choice has often been identified as a key component in a player's engagement with an interactive narrative, but branching stories require tremendous amounts of hand-authored content, in amounts that increase exponentially rather than linearly as more choice points are added. Previous approaches to reducing authorial burden for computer RPGs have relied on creating better tools to manage existing unwieldy structures of quests and dialogue trees. We hypothesize that reducing authorial burden and increasing agency are two sides of the same coin, requiring specific advancements in two related areas of design and technology research: (1) dynamic story management architecture that represents story events abstractly and allows story elements to be selected and re-ordered in response to player choices, and (2) dynamic dialogue generation to allow a single story event to be revealed differently by different characters and in the context of dynamic relationships between those characters and the player. This paper describes SpyFeet, a playable prototype of a storytellingsystem designed to test this hypothesis.
The SimpleFPS Planning Domain: A PDDL Benchmark for Proactive NPCs
Authors: Vassos Stavros , Papakonstantinou Michail
Published: October 2022

In this paper we focus on proactive behavior for non-player characters (NPCs) in the first-person shooter (FPS) genre of video games based on goal-oriented planning. Some recent approaches for applying real-time planning in commercial video games show that the existing hardware is starting to follow up on the computing resources needed for such techniques to work well. Nonetheless, it is not clear under which conditions real-time efficiency can be guaranteed. In this paper we give a precise specification of SimpleFPS, a STRIPS planning domain expressed in PDDL that captures some basic planning tasks that may be useful in a first person shooter video game. This is intended to work as a first step towards quantifying the performance of different planning techniques that may be used in real-time to guide the behavior of NPCs. We present a simple tool we developed for generating random planning problem instances in PDDL with user defined properties, and show some preliminary results based on SimpleFPS instances that vary in the size of the domain and two well-known planners from the planning community.
A Phone That Cures Your Flu: Generating Imaginary Gadgets in Fictions with Planning and Analogies
Authors: Li Boyang , Riedl Mark
Published: October 2022

Most computational story generation systems lack the ability to generate new types of imaginary objects that play functional roles in stories, such as lightsabers in Star Wars. We present an algorithm that generates such imaginary objects, which we call gadgets, in order to extend the ontological expressivity of existing, planning-based story generation systems. The behavior of a gadget is represented as a plan including typical events that happen when the gadget is used. Our algorithm creates gadgets by extrapolating and merging one or more commonly known objects in order to achieve a narrative goal provided by an existing story generator. We extend partial-order planning to establish open conditions based on analogies between concepts related respectively to common objects and the gadget. We show the algorithm is capable of generating gadgets created by human.
Approaching a Player Model of Game Story Comprehension Through Affordance in Interactive Narrative
Authors: Young R. Michael , Cardona-Rivera Rogelio
Published: October 2022

A growing body of work in games research, both generative and analytic, seeks to characterize the relationship between a player’s understanding of an interactive narrative and her options for action within it. This paper provides several definitions that collectively serve as a basis for a model of the user’s comprehension of an unfolding story in a game. Central to this approach, we define the notion of narrative affordance. In essence, a game provides a narrative affordance for some course of action when a player can imagine that course of action as part of a story that completes their current story experience. To define narrative affordance, we draw links from cognitive models of narrative comprehension and a range of research on affordance, which we couple with planning approaches to story and discourse generation. In our approach, we view the creation of an interactive narrative that provides a high degree of agency as a discourse generation problem. We posit that an interactive narrative system must reason about the content and organization of its communication with a player in order to prompt a player’s understanding about the game’s story and her role in it. This paper ends by pointing toward a research direction intended to provide insight into a range of aspects of interactive narrative, including role, genre, choice and agency.
RoleModelVis: A Visualization of Logical Story Models
Authors: Chen Sherol , Deunsing Andrew , Kong Peter , Jhala Arnav , Wardrip-Fruin Noah , Mateas Michael
Published: October 2022

In this demo we present a visualization of formalized representations of story. Introducing the interactive to storytelling requires the management of experiences that a user creates by their decisions. These sorts of variations can have impact on not only the user, but also the retrievable content appropriate to present to the user. The overall contribution of this work is to identify the player impact of story variation by modeling supplementary variations, and systematically responding to player interaction through supplementary variation, while respecting the author’s intentions by maintaining the integrity of the core story skeleton.
Reaching Cognitive Consensus with Improvisational Agents
Authors: Hodhod Rania , Magerko Brian
Published: October 2022

A common approach to interactive narrative involves imbuing the computer with all of the potential story pre-authored story experiences (e.g. as beats, plot points, planning operators, etc.). This has resulted in an accepted paradigm where stories are not created by or with the user; rather, the user is given piecemeal access to the story from the gatekeeper of story knowledge: the computer (e.g. as an AI drama manager). This article describes a formal process that provides for the equal co-creation of story-rich experiences, where neither the user nor computer is in a privileged position in an interactive narrative. It describes a new formal approach that acts as a first step for the real-time co-creation of narrative in games that rely on the negotiated shared mental model between a human actor and an AI improv agent.
Combining Search-Based Procedural Content Generation and Social Gaming in the Petalz Video Game
Authors: Risi Sebastian , Lehman Joel , D'Ambrosio David , Hall Ryan , Stanley Kenneth
Published: October 2022

Search-based procedural content generation methods allow video games to introduce new content continually, thereby engaging the player for a longer time while reducing the burden on developers. However, games so far have not explored the potential economic value of unique evolved artifacts. Building on this insight, this paper presents for the first time a Facebook game called Petalz in which players can share flowers they breed themselves with other players through a global marketplace. In particular, the market in this social game allows players to set the price of their evolved aesthetically-pleasing flowers in virtual currency. Furthermore, the transaction in which one player buys seeds from another creates a new social element that links the players in the transaction. The combination of unique user-generated content and social gaming in Petalz facilitates meaningful collaboration between users, positively influences the dynamics of the game, and opens new possibilities in digital entertainment.
Evolutionary Learning of Goal Priorities in a Real-Time Strategy Game
Authors: Young Jay , Hawes Nick
Published: October 2022

We present a drive-based agent capable of playing the real-time strategy computer game Starcraft. Success at this task requires the ability to engage in autonomous, goal-directed behaviour, as well as techniques to manage the problem of potential goal conflicts. To address this, we show how a case-injected genetic algorithm can be used to learn goal priority profiles for use in goal management. This is achieved by learning how goals might be re-prioritised under certain operating conditions, and how priority profiles can be used to dynamically guide high-level strategies. Our dynamic system shows greatly improved results over a version equipped with static knowledge, and a version that only partially exploits the space of learned strategies. However, our work raises questions about how a system must know about its own design in order to best exploit its own competences.
Game-Based Data Capture for Player Metrics
Authors: Normoyle Aline , Drake John , Likhachev Maxim , Safonova Alla
Published: October 2022

Player metrics are an invaluable resource for game designers and QA analysts who wish to understand players, monitor and improve game play, and test design hypotheses. Usually such metrics are collected in a straightforward manner by passively recording players; however, such an approach has several potential drawbacks. First, passive recording might fail to record metrics which correspond to an infrequent player behavior. Secondly, passive recording can be a costly, laborious, and memory intensive process, even with the aid of tools. In this paper, we explore the potential for an active approach to player metric collection which strives to collect data more efficiently, and thus with less cost. We use an online, iterative approach which models the relationship between player metrics and in-game situations probabilistically using a Markov Decision Process (MDP) and solves it for the best game configurations to run. To analyze the benefits and limitations of this approach, we implemented a system, called GAMELAB, for recording player metrics in Second Life.
Mining Rules from Player Experience and Activity Data
Authors: Gow Jeremy , Colton Simon , Cairns Paul , Miller Paul
Published: October 2022

Feedback on player experience and behaviour can be invaluable to game designers, but there is need for specialised knowledge discovery tools to deal with high volume playtest data. We describe a study witha commercial third-person shooter, in which integrated player activity and experience data was captured and mined for design-relevant knowledge. We demonstrate that association rule learning and rule templates can be used to extractmeaningful rules relating player activity and experience during combat. We found that the number, type and quality of rules varies between experiences, and is affected by feature distributions. Further work is required on rule selection and evaluation.
Aesthetic Considerations for Automated Platformer Design
Authors: Cook Michael , Colton Simon , Pease Alison
Published: October 2022

We describe ANGELINA3, a system that can automatically develop games along a defined theme, by selecting appropriate multimedia content from a variety of sources and incorporating it into a game's design. We discuss these capabilities in the context of the FACE model for assessing progress in the building of creative systems, and discuss how ANGELINA3 can be improved through further work.
TEAM-IT : Location-Based Gaming in Real and Virtual Environments
Authors: Frazier Spencer , Newnan Alex , Maheswaran Rajiv , Chang Yu-Han , Frangoudes Fotos
Published: October 2022

Location-based games are an emerging paradigm fortraining, simulation, entertainment, health and many other domains. In this paper, we consider the role of artificialagents in such games. We also examine how human teams perform when given the same game, playedin both a real environment with mobile devices and alsoin a virtual environment that replicates the real environment.We perform the first direct comparison of real andvirtual instantiations of the same location-based game.We show the similarities and differences in game playand then investigate how adding an advice-giving agentchanges the experience.
Toward Autonomous Crowd-Powered Creation of Interactive Narratives
Authors: Li Boyang , Lee-Urban Stephen , Riedl Mark
Published: October 2022

Interactive narrative is a form of storytelling that adapts to actions performed by users who assume the roles of story characters. To date, interactive narratives are built by hand. In this paper, we introduce Scheherazade, an intelligent system that automatically creates an interactive narrative about any topic from crowdsourced narratives. Our system leverages the experience and creativity of humans by crowdsourcing a corpus of linear narrative examples. It then constructs an executable plot graph, which is a knowledge structure that defines the legal space of an interactive narrative, by learning the plot events, execution precedence, and event separations. We demonstrate the system can successfully construct an interactive narrative based on noisy human input.
Toward Narrative Schema-Based Goal Recognition Models for Interactive Narrative Environments
Authors: Baikadi Alok , Rowe Jonathan , Mott Bradford , Lester James
Published: October 2022

Computational models for goal recognition hold great promise for enhancing the capabilities of drama managers and director agents for interactive narratives. The problem of goal recognition, and its more general form, plan recognition, have been the subjects of extensive investigation in the AI community. However, relatively little effort has been undertaken to examine goal recognition in interactive narrative. In this paper, we propose a research agenda to improve the accuracy of goal recognition models for interactive narratives using explicit representations of narrative structure inspired by the natural language processing community. We describe a particular category of narrative representations, narrative schemas, that we anticipate will effectively capture patterns of player behavior in interactive narratives and improve the accuracy of goal recognition models.
The Intentional Fast-Forward Narrative Planner
Authors: Ware Stephen
Published: October 2022

The Intentional Fast-Forward (IFF) planner is an attempt to apply fast forward-chaining state-space search methods to intentional planning---planning such that every action is directed toward some character's goal. The IFF heuristic is based on Hoffmann's original Fast Forward heuristic (2001), which solves a simplified version of the problem and uses that solution as a guide for the real problem. IFF incorporates constraints imposed by intentional planning to narrow down the set of steps which can be taken next, and it identifies fruitless branches of the search space early.
Quantum Composition and Improvisation
Authors: Parson Dale
Published: October 2022

Quantum mechanical systems exist as superpositions of complementary states that collapse to classical, concrete states upon becoming entangled with the measurement apparatus of observer-participants. A musical composition and its performance constitute a quantum system. Historically, conventional musical notation has presented the appearance of a composition as a deterministic, concrete entity, with interpretation approached as an extrinsic act. This historical perspective inhabits a subspace of the available quantum space. A quantum musical system unifies the composition, instruments, situated performance and perception as a superposition of musical events that collapses to concrete musical events via the interactions and perceptions of performers and audience. A composer captures superposed musical events via implicit or explicit conditional event probabilities, and human and/or machine performers create music by collapsing interrelated probabilities to zeros and ones via observer-participancy.
Preface
Authors: Pasquier Philippe , Eigenfeldt Arne , Bown Oliver
Published: October 2022

Thee Musical Metacreation workshop aims to bring together artists, practitioners and researchers interested in developing software and systems that autonomously (or interactively) recognize, learn, represent, complete, accompany, compose, or interpret music.
Limitations of Choice-Based Interactive Evolution for Game Level Design
Authors: Liapis Antonios , Yannakakis Georgios , Togelius Julian
Published: October 2022

This paper presents a tool geared towards the collaboration of a human and an artificial designer for the creation of game content. The framework combines procedural content generation using stochastic search with user input in the form of an initial goal statement as well as preference of generated results. Feedback from industry experts in a pilot user experiment showcased the limitations of this approach and the protocol chosen for evaluating the authoring tool. The limitations are discussed with respect to the suitability of interactive evolution for creative design and the design of experimental protocols for evaluating authoring tools for games.
A Review of Student Modeling Techniques in Intelligent Tutoring Systems
Authors: Harrison Brent , Roberts David
Published: October 2022

In this paper, we survey techniques used in intelligent tutoring systems (ITSs) to model student knowledge. The three techniques that we review in detail are knowledge tracing, performance factor analysis, and matrix factorization. We also briefly cover other techniques that have been used. This review is meant to be a repository of knowledge for those who want to integrate these techniques into serious games. It is also meant to increase awareness and interest as to the techniques available that can be integrated into serious games.
Ropossum: An Authoring Tool for Designing, Optimizing and Solving Cut the Rope Levels
Authors: Shaker Noor , Shaker Mohammad , Togelius Julian
Published: October 2022

We present a demonstration of Ropossum, an authoring tool for the generation and testing of levels of the physics-based game, Cut the Rope. Ropossum integrates many features: (1) automatic design of complete solvable content, (2) incorporation of designer’s input through the creation of complete or partial designs, (3) automatic check for playability and (4) optimization of a given design based on playability. The system includes a physics engine to simulate the game and an evolutionary framework to evolve content as well as an AI reasoning agent to check for playability. The system is optimised to allow on-line feedback and realtime interaction.
Automated Generation of Diverse NPC-Controlling FSMs Using Nondeterministic Planning Techniques
Authors: Coman Alexandra , Munoz-Avila Hector
Published: October 2022

We study the problem of generating a set of Finite State Machines (FSMs) modeling the behavior of multiple, distinct NPCs. We observe that nondeterministic planning techniques can be used to generate FSMs by following conventions typically used when manually creating FSMs modeling NPC behavior. We implement our ideas in DivNDP, the first algorithm for automated diverse FSM generation.
Modeling Unit Classes as Agents in Real-Time Strategy Games
Authors: Jaidee Ulit , Munoz-Avila Hector
Published: October 2022

We present CLASSQL, a multi-agent model for playing real-time strategy games, where learning and control of our own team’s units is decentralized; each agent uses its own reinforcement learning process to learn and control units of the same class. Coordination between these agents occurs as a result of a common reward function shared by all agents and synergistic relations in a carefully crafted state and action model for each class. We present results of CLASSQL against the built-in AI in a variety of maps using the Wargus real-time strategy game.
A Nondeterministic Temporal Planning Model for Generating Narratives with Continuous Change in Interactive Storytelling
Authors: Abelha Paulo , Gottin Vinicius , Ciarlini Angelo , Araujo Eric , Furtado Antonio , Feijo Bruno , Silva Fabio , Pozzer Cesar
Published: October 2022

In this paper, we propose a temporal planning model for real-time generation of narratives in Interactive Storytelling systems. The model takes into account continuous branched time and the specification of constraints defined as temporal formulae over dramatic properties of the narrative (e.g. joy or tension). In order to address real-time generation, dramatic properties are modeled as varying linearly and events go through a preprocessing stage. As proof of concept, the model is incorporated into an existing storytelling system, LOGTELL, which provides a way to logically specify genres; allows user interaction to influence events in the unrolling narrative; and dramatizes the story in a 3D computer graphics world. To illustrate the generation of narratives, we present a simple narrative example in the Swords and Dragons genre.
Modeling Autobiographical Memory for Believable Agents
Authors: Kope Andrew , Rose Caroline , Katchabaw Michael
Published: October 2022

We present a multi-layer hierarchical connectionist network model for simulating human autobiographical memory in believable agents. Grounded in psychological theory, this model improves on previous attempts to model agents’ event knowledge by providing a more dynamic and non-deterministic representation of autobiographical memories. From this model, a Java-based proof-of-concept prototype system was created for use as an enabling technology in video games. This prototype was leveraged in the creation of a Minecraft modification (mod) implementation of the model that is able to demonstrate context-dependent recall and the effects of recency on memory recall. Wider implications of the model in agent and game design are discussed.
Shape-Keeping Technique and Its Application to Checkmate Problem Composition
Authors: Ishitobi Taichi , Cincotti Alessandro , Iida Hiroyuki
Published: October 2022

The checkmate problem in Shogi (Japanese Chess) is a puzzle within the game itself. These puzzles have enjoyed a long play and have been the subject of centuries of analysis. The subject of this research is defining the aesthetic criteria of great Shogi problems, and finding new methods for composing interesting checkmate problems in Shogi. First we examine the results of previous studies of aesthetics in Shogi checkmate problems. For this purpose, we focus on the Proof Number Search algorithm and record the data while solving checkmate problems. We analyzed these data and we calculated the proof number related to the evaluation of the checkmate problem. Good checkmate problems have large proof numbers. Next, we present a new technique for automatic composition of checkmate problems in Shogi. This technique uses already existing checkmate problems in Shogi and develops them further. Finally, we can compose new checkmate problems which have bigger proof numbers than original ones. This work is not yet sufficient unto itself.
From Mechanics to Meaning and Back Again: Exploring Techniques for the Contextualisation of Code
Authors: Cook Michael , Colton Simon
Published: October 2022

Code generation is a promising new area for the automatic production of mechanics and systems in games. Generated code alone is not sufficient for inclusion in a rich, fully-designed game, however - it lacks context to bind the functionality of code to the metaphorical setting of the game. In this paper we explore potential solutions to this problem, both in terms of creative systems which co-operate with human content, and the possibility for contextual meaning in autonomous, human-free creative systems as well.
Preface
Authors: Nelson Mark
Published: October 2022

“Game AI” usually brings to mind control of opponents and other characters. We are interested in a different way that AI can intersect games: during the design process. How can retrieval, inference, knowledge representation, learning, and search loosen the bottlenecks in the game design process? How can AI be put to use in ideation, prototyping, feedback, visualization, synthesis and verification of designed artifacts (puzzles, missions, maps, mechanics, stories ...)? How can AI provide assistance to game designers and/or share the creative responsibilities in design?
Evaluating the Use of Action Templates to Create Suspense in Narrative Planning
Authors: Fendt Matthew , Roberts David , Young R.
Published: October 2022

The IRIS story generation system is a narrative planner that is designed to create suspenseful stories at the story-action level. It uses a set of action templates that are injected into the planning process to create suspense around a given goal. The action templates were evaluated independent of the rest of the IRIS system against a series of other techniques designed to create suspense. Results show that it is possible to use these set of action templates to create localized moments of suspense in a story at the story-action level without the need for discourse-level suspense.
Identifying Personal Narratives in Chinese Weblog Posts
Authors: Gordon Andrew , Huangfu Luwen , Sagae Kenji , Mao Wenji , Chen Wen
Published: October 2022

Automated text classification technologies have enabled researchers to amass enormous collections of personal narratives posted to English-language weblogs. In this paper, we explore analogous approaches to identify personal narratives in Chinese weblog posts as a precursor to the future empirical studies of cross-cultural differences in narrative structure. We describe the collection of over half a million posts from a popular Chinese weblog hosting service, and the manual annotation of story and nonstory content in sampled posts. Using supervised machine learning methods, we developed an automated text classifier for personal narratives in Chinese posts, achieving classification accuracy comparable to previous work in English. Using this classifier, we automatically identify over sixty-four thousand personal narratives for use in future cross-cultural analyses and Chinese-language applications of narrative corpora.
Generating Believable Stories in Large Domains
Authors: Kartal Bilal , Koenig John , Guy Stephen
Published: October 2022

Planning-based techniques are a very powerful tool for automated story generation. However, as the number of possible actions increases, traditional planning techniques suffer from a combinatorial explosion due to large branching factors. In this work, we apply Monte Carlo Tree Search (MCTS) techniques to generate stories in domains with large numbers of possible actions (100+). Our approach employs a Bayesian story evaluation method to guide the planning towards believable stories that reach a user defined goal. We generate stories in a novel domain with different type of story goals. Our approach shows an order of magnitude improvement in performance over traditional search techniques.