bluuebunny/crossref_metadata_2025 huggingface dataset:
Salience Vectors for Measuring Distance between Stories
Authors: Farrell Rachelyn , Fisher Mira , Ware Stephen G.
Published: October 2022

Narrative planners generate sequences of actions that represent story plots given a story domain model. This is a useful way to create branching stories for interactive narrative systems that maintain logical consistency across multiple storylines with different content. There is a need for story comparison techniques that can enable systems like experience managers and domain authoring tools to reason about similarities and differences between multiple stories or branches. We present an algorithm for summarizing narrative plans as numeric vectors based on a cognitive model of human story perception. The vectors encode important story information and can be compared using standard distance functions to quantify the overall semantic difference between two stories. We show that this distance metric is highly accurate based on human annotations of story similarity, and compare it to several alternative approaches. We also explore variations of our method in an attempt to broaden its applicability to other types of story systems.
Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search
Authors: N. Ferreira Lucas , Mou Lili , Whitehead Jim , Lelis Levi H. S.
Published: October 2022

This paper presents a new approach for controlling emotion in symbolic music generation with Monte Carlo Tree Search. We use Monte Carlo Tree Search as a decoding mechanism to steer the probability distribution learned by a language model towards a given emotion. At every step of the decoding process, we use Predictor Upper Confidence for Trees (PUCT) to search for sequences that maximize the average values of emotion and quality as given by an emotion classifier and a discriminator, respectively. We use a language model as PUCT's policy and a combination of the emotion classifier and the discriminator as its value function. To decode the next token in a piece of music, we sample from the distribution of node visits created during the search. We evaluate the quality of the generated samples with respect to human-composed pieces using a set of objective metrics computed directly from the generated samples. We also perform a user study to evaluate how human subjects perceive the generated samples' quality and emotion. We compare PUCT against Stochastic Bi-Objective Beam Search (SBBS) and Conditional Sampling (CS). Results suggest that PUCT outperforms SBBS and CS in almost all metrics of music quality and emotion.
Searching for Balanced 2D Brawler Games: Successes and Failures of Automated Evaluation
Authors: Shields Samuel , Mawhorter Ross , Melcer Edward , Mateas Michael
Published: October 2022

Automated game design (AGD) research focuses on creating systems that can design entirely new games. This is often done by a genetic algorithm, with a fitness function that is used to find individual games that satisfy certain design criteria. However, it is difficult to tell if generated games actually have the desired emergent properties (such as balance), since the fitness function might not align well with human aesthetic judgments about such properties. This is particularly problematic when trying to automatically design balanced, fair, yet asymmetrical games for multiple players. In this paper we present an implementation of an optimization-based AGD system for brawler games, and present findings from a preliminary user study of generated games. We show that while the system successfully optimizes for our written fitness function during human play, we found that this optimization did not necessarily translate to our hypothesized human experience of the game.
Icepool: Efficient Computation of Dice Pool Probabilities
Authors: Liu Albert Julius
Published: October 2022

Mechanics involving the roll of multiple dice---a "dice pool"---commonly appear in tabletop board games and role-playing games. Existing general-purpose dice pool probability calculators resort to exhaustive enumeration of all possible sorted sequences of rolls, which can quickly become computationally intractable. We propose a dynamic programming algorithm that can efficiently compute probabilities for a wide variety of dice pool mechanics while limiting the need for bespoke optimization. We also present Icepool, a pure Python implementation of the algorithm combined with a library of common dice operations.
Narrative Planning in Large Domains through State Abstraction and Option Discovery
Authors: Fisher Mira
Published: October 2022

Low-level game environments and other simulations present a difficulty of scale for an expensive AI technique like narrative planning, which is normally constrained to environments with small state spaces. Due to this limitation, the intentional and cooperative behavior of agents guided by this technology cannot be deployed for different systems without significant additional authoring effort. I propose a process for automatically creating models for larger-scale domains such that a narrative planner can be employed in these settings. By generating an abstract domain of an environment while retaining the information needed to produce behavior appropriate to the abstract actions, agents are able to reason in a lower-complexity space and act in the higher-complexity one. This abstraction is accomplished by the development of extended-duration actions and the identification of their preconditions and effects. Together these components may be combined to form a narrative planning domain, and plans from this domain can be executed within the low-level environment.
Natural Noun Phrase Variation for Interactive Characters
Authors: Byron Donna , Dalwani Aakash , Gerritsen Ryan , Keck Mark , Mampilly Thomas , Sharma Vinay , Stoia Laura , Weale Timothy , Xu Tianfang
Published: October 2022

Interactive characters that cohabit a shared space with human partners need to generate and interpret references to elements of the virtual world. Natural language allows for a wide range of phrasings for referring to any particular object, and this variation is thought to reflect not only spatial but also cognitive and linguistic factors. Our study attempts to account for the variability in referring forms found in a set of dialogs of two human partners performing a treasure-hunt task in a virtual world. A decision tree classifier was built that predicts the form of 51% of the referring expressions, compared to a baseline of 39% achieved by a heuristic classifier. The classification algorithm can be used by conversational characters to generate referring expressions of the appropriate form.
A Goal-Based Architecture for Opposing Player AI
Authors: Dill Kevin , Pap Denis
Published: October 2022

This paper describes a goal-based architecture which provides a single source for all high level decisions made by AI players in real-time strategy games. The architecture is easily extensible, flexible enough to be rapidly adapted to multiple different games, and powerful enough to provide a good challenge on a random, unexplored map without unfair advantages or visible cheating. This framework was applied successfully in the development of two games at TimeGate Studios — Kohan2: Kings of War and Axis and Allies.
nuWar: A Prototype Sketch-Based Strategy Game
Authors: Dunham Greg , Forbus Kenneth , Usher Jeffrey
Published: October 2022

Today's military strategy games provide unrealistic interfaces for players to interact with their units: Commanders don't use mice and menus, they sketch. Developing strategy games currently involves grafting AI capabilities on top of a separate simulation engine, with new hand-crafted strategies for each game. We are experimenting with a novel approach for solving both problems. We started with nuSketch Battlespace, a knowledge-rich sketch understanding system developed for military users, and built a game engine, nuWar, on top of it. nuWar is a prototype two-player tactical war game which can be played either hot-seat or over a network. nuWar uses sketching as the primary way for players to express their intent to their subordinate commanders. The underlying ontology used by nuSketch Battlespace is used in both the simulation engine and in the bots which serve as subordinate commanders. We describe the architecture of nuWar, focusing on how it uses sketching, how the simulation engine is built upon the rich representational facilities of nuSketch Battlespace, and how the bots work. We discuss the tradeoffs we have found in this approach so far, and describe our plans for future work.
Sequence Learning by Backward Chaining in Synthetic Characters
Authors: Gorniak Peter , Blumberg Bruce
Published: October 2022

Many learning algorithms concern themselves with learning from large amounts of data without human interaction. Synthetic characters that interact with human beings present a wholly different problem: they must learn quickly from few examples provided by a non-expert teacher. Training must be intuitive, provide feedback, and still allow training of nontrivial new behaviours. We present a learning mechanism that allows an autonomous synthetic character to learn sequences of actions from natural interaction with a human trainer. The synthetic character learns from only a handful of training examples, in a realtime and complex environment. Building on an existing framework for training a virtual dog to perform single actions on command and explore its action and state space, we give the dog the ability to notice consistent reward patterns that follow sequences of actions. Using an approximate online algorithm to check the Markov property for an action, the dog can discover action sequences that reliably predict rewards and turn these sequences into actions, allowing them to be associated with speech commands. This framework leads to a natural and easy training procedure that is a version of Backward Chaining, a technique commonly used by animal trainers to teach sequences of actions.
Negative Behavior Space in the Design of Interactive Agents
Authors: Tomlinson Bill
Published: October 2022

When defining the behavioral repertoire of an interactive artificial intelligence, it is helpful for designers to have a systematic way to specify which behaviors the AI will be able to perform and which behaviors it will not. The idea of a behavior space is often used in designing and implementing the behaviors that the AI will be able to perform. This paper proposes the idea of a “negative behavior space” – a set of behaviors that will explicitly not be implemented – as a useful concept for the development of AIs for games and other interactive media. Examples of negative behavior spaces are given from the development processes of two interactive installations: AlphaWolf, which was exhibited in the Emerging Technologies program at SIGGRAPH 2001, and the Virtual Raft Project, which will be shown in the Interactivity program at CHI 2005 and in the Emerging Technologies program at SIGGRAPH 2005. By explicitly excluding certain sets of behaviors from the development of an AI, designers may create interactive experiences that are clearer and more enjoyable for the player, and may also make their own work easier.
Real-Time Simulation of Herds Moving Over Terrain
Authors: Gompert Joel
Published: October 2022

We present a method for animating herds of animals to follow terrain surfaces in real time. This method involves making modifications to Reynold’s boids algorithm. The modifications use local properties of the terrain and thus have low complexity. They produce naturally behaving herds that follow the terrain, swerving around hills and attempting to reduce energy expenditure. The terrain-following rule has a parameter that can be adjusted to produce different behaviors. Empirical analysis shows how this parameter affects energy expenditure of the animals while traveling over the terrain. See demo: http://consystlab.unl.edu/our_work/herds.html
Real-Time Learning in the NERO Video Game
Authors: Stanley Kenneth , Cornelius Ryan , Miikkulainen Risto , D’Silva Thomas , Gold Aliza
Published: October 2022

If game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. The real-time NeuroEvolution of Augmenting Topologies (rtNEAT) method, which can evolve increasingly complex artificial neural networks in real time as a game is being played, will be presented. The rtNEAT method makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. In order to demonstrate this concept, the NeuroEvolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players' teams. The live demo will show how agents in NERO adapt in real time as they interact with the player. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.
Improved Heuristics for Optimal Pathfinding on Game Maps
Authors: Björnsson Yngvi , Halldórsson Kári
Published: October 2022

As computer game worlds get more elaborate the more visible pathfinding performance bottlenecks become. The heuristic functions typically used for guiding A*-based path inding are too simplistic to provide the search with the necessary guidance in such large and complex game worlds. This may result in A*-search exploring the entire game map in order to find a path between two distant locations. This article presents two effective heuristics for estimating distances between locations in large and complex game maps. The former, the dead-end heuristic, eliminates from the search map areas that are provably irrele- vant for the current query, whereas the second heuristic uses so-called gateways to improve its estimates. Empirical evaluation on actual game maps shows that both heuristics reduce the exploration and time complexity of A* search significantly over a standard octile distance metric.
CrowdMixer: Multiple Agent Types in Situation-Based Crowd Simulations
Authors: Blyth Shannon , Hamilton Howard
Published: October 2022

This paper presents a scalable approach to crowd simulation that can generate complex and varied simulations by using multiple types of individuals in a crowd. Efficiency is attained by using a situation-based approach where an individual agent adopts behaviors according to its situation, which corresponds to a subspace of the universe.
Lightweight Simulation of Air Traffic Control Using Simple Temporal Networks
Authors: Knight Russell
Published: October 2022

Flight simulators are becoming more sophisticated and realistic, and the requirements of those using them are becoming more demanding. Air traffic control simulation for such simulators is now approaching the real problem of air traffic control, where several aircraft need to be scheduled for safe approach and landing or for safe passage through the air space that an air traffic controller is responsible for. Also, the computational resources available to dedicate to air traffic control simulation are sparse, given that most simulations focus on a realistic graphical user interface and aircraft physics modeling. We provide a formulation of the air traffic control problem and a solver for this problem that makes use of temporal constraint networks and simple geometric reasoning. We provide results showing that this approach is practical for realistic simulated problems.
The Self Organization of Context for Learning in Multiagent Games
Authors: White Chris , Brogan David
Published: October 2022

Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, tile coding with linear function approximation has been widely used to circumvent the curse of dimensionality, but it suffers from the drawback that human-guided identification of features is required to create effective tilings. The challenge is to find tilings that preserve the context necessary to evaluate the value of a state-action pair while limit- ing memory requirements. The technique presented in this paper addresses the difficulty of identifying context in high-dimensional domains. We have chosen RoboCup simulated soccer as a domain because its high-dimensional continuous state space makes it a formidable challenge for reinforcement learning algorithms. Using self-organizing maps and reinforcement learning in a two-pass process, our technique scales to large state spaces without requiring a large amount of domain knowledge to automatically form abstractions over the state space. Results show that our algorithm learns to play the game of soccer better than a contemporary hand-coded opponent.
Incremental Heuristic Search in Games: The Quest for Speed
Authors: Likhachev Maxim , Koenig Sven
Published: October 2022

Robot path-planning methods can be used in real-time computer games but then need to run fast to ensure that the game characters move in real time, an issue addressed by incremental heuristic search methods. In this paper, we demon- strate how one can speed up D* Lite, an incremental heuristic search method that implements planning with the freespace assumption to move game characters in initially unknown or partially unknown terrain to given goal coordinates. We speed up D* Lite by implementing its priority queue with buckets rather than a binary heap. This non-trivial change reduces its runtime by a factor of two and effectively doubles the number of game characters that real-time computer games can afford.
Authoring an Interactive Narrative with Declarative Optimization-Based Drama Management
Authors: Nelson Mark , Ashmore Calvin , Mateas Michael
Published: October 2022

Drama managers reconfigure a game in reaction to a player's actions. In declarative optimization-based drama management (DODM), a game's story is abstracted as a sequence of plot points; possible drama manager interventions are abstracted as a set of DM actions. The author defines an func- tion evaluating story quality, and some optimization method (currently reinforcement learning) chooses DM actions so as to maximize expected story quality according to that evaluation function. While previous work has developed this ap- proach at a technical level and discussed its potential applications, no work to date has used DODM to write real games. We report on our experiences designing a game in the Neverwinter Nights engine, entitled The Guilty, in which we use DODM to create a dynamic plot that in a previous design iteration we had found difficult to create with other techniques.
Using Environmental Annotations and Affordances to Model Culture
Authors: McAlinden Ryan , Van Lent Michael , Clevenger William , Tien Wen
Published: October 2022

This paper details the demonstration of an annotation and affordance-based software model intended to introduce cultural and social influences into a non-player character’s (NPC) decision-making process. We describe how recent research has supported the need to begin incorporating the effects of culture into the interactive digital domain. The technical approach is presented that describes the software techniques for embedding and utilizing culturally-specific information inside of a virtual environment, as well as the design and implementation of a deterministic Markov Decision Process (MDP) to model the affects of culture on the AI.
The NERO Video Game
Authors: Stanley Kenneth , Karpov Igor , Miikkulainen Risto , Gold Aliza
Published: October 2022

In the NeuroEvolving Robotic Operatives (NERO) game, the player trains a team of virtual robots for combat against other players' teams. The virtual robots learn in real time through interacting with the player. Since NERO was originally released in June, 2005, it has been downloaded over 50,000 times, appeared on Slashdot, and won several honors. A significant update was released in November, 2005, including improved performance, new battle mode options, and faster learning. The virtual robots learn using the real-time NeuroEvolution of Augmenting Topologies (rt-NEAT) method, which can evolve increasingly complex artificial agents in NERO adapt in real time as they interact with the player in the new, updated game. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.
Building Test Beds for AI with the Q3 Mode Base
Authors: Youngblood G. , Nolen Billy , Ross Michael , Holder Lawrence
Published: October 2022

We extend the open source Quake 3 Arena code base released by Id Software to include modification friendly code segments, art asset blueprints, external agent control and perception interfaces, game interaction logging, and a tool set that allows the creation of test beds for artificial agents. Our current modification work in the Urban Combat Testbed using the Q3 Mod Base is currently being used to study transfer learning in human and agent players and as an AI teaching tool.
Improving Narrative Consistency in Planning-Based Interactive Storytelling
Authors: Barros Leandro , Musse Soraia
Published: October 2022

Interactive Storytelling (IS) systems are an emerging class of interactive entertainment applications with emphasis in narrative aspects. This paper deals with stories that have plots centered on a riddle, and describes a mechanism that can be introduced to planning-based IS in order to make the generated stories follow a specified tension arc. Results from experiments with users suggest that the proposed method is capable to bring improvements to these systems.
Motivational Ambient and Latent Behaviors in Computer RPGs
Authors: Cutumisu Maria , Szafron Duane , Schaeffer Jonathan , Waugh Kevin
Published: October 2022

Character behaviors in computer role-playing games have a significant impact on game-play, but are often difficult for game authors to implement and adapt. We present a behavior model that requires no manual script writing. In this model, behaviors can be interrupted and resumed, they can transition to other behaviors depending on game events, and they can be chosen based on motivations. We have extended the generative pattern approach of ScriptEase to support behaviors. We describe an implementation of this model that generates scripting code for a commercial game, Neverwinter Nights.
ShapeShifting Screen Media: A Declarative Computational Model for Interactive Reconfigurable Moving Image Narratives
Authors: Ursu Marian , Cook Jonathan , Zsombori Vilmos , Kegel Ian , Williams Doug , Hausenblas Michael , Tuomola Mika
Published: October 2022

This paper presents an approach and a computational model, implemented in a software system, for the creation and delivery of interactive moving image productions made with filmed material that level the aesthetic and editing quality of existing linear film and television programmes. They have been validated through a number of productions, notably through a dramatic production delivered on a national television channel in 12 episodes. The focus of the paper is on the declarative formal language, called Narrative Structure Language (NSL), which we developed for the representation of such narratives, and on an accompanying reasoning engine.
Lightweight Procedural Animation with Believable Physical Interactions
Authors: Horswill Ian
Published: October 2022

I describe a procedural animation system that uses techniques from behavior-based robot control, combined with a minimalist physical simulation, to produce believable character motions in a dynamic world. Although less realistic than motion capture or full biomechanical simulation, the system produces compelling, responsive character behavior. It is also fast, supports believable physical interactions between characters such as hugging, and makes it easy to author new behaviors.
Learning and Playing in Wubble World
Authors: Kerr Wesley , Cohen Paul , Chang Yu-Han
Published: October 2022

Children do not learn the meanings of words from parsing and understanding gigabytes of text; instead meanings are learned from competent speakers who relate language to what's happening in the child's environment. We present a word learning algorithm that operates in a video game environment where the players fill the role of the competent speakers and train softbots to learn language as a child would. We provide empirical evidence that the word learning algorithm successfully learns the meanings for some words in this environment and the children enjoy playing the game.
Logical Agents for Language and Action
Authors: Martin Magnusson Martin Magnusson , Doherty Patrick
Published: October 2022

Game developers are faced with the difficult task of creating non-player characters with convincing behavior. This commonly involves an exhaustive specification of their actions in every conceivable situation that might arise. The process is laborious and the results do not apply in new and unforeseen circumstances. We present an agent architecture where game characters have a basic understanding of their environment that enables them to figure out what to do by themselves. Knowledge about facts and actions is encoded in a formal logic where automated reasoning technology can apply the knowledge to answer novel questions in dialog and to plan actions in new situations. A number of examples serve to demonstrate how the technology meets the challenges of application in a simple computer game prototype. We envision this technology being used to create more flexible game characters with a deeper understanding of the world they inhabit.
Automatic Generation of Game Level Solutions as Storyboards
Authors: Pizzi David , Cavazza Marc , Whittaker Alex , Lugrin Jean-Luc
Published: October 2022

Interactive Storytelling techniques are attracting much interest for their potential to develop new game genres but also as another form of procedural content generation, specifically dedicated to game events rather than objects or characters. However, one issue constantly raised by game developers, when discussing gameplay implications of Interactive Storytelling techniques, is the possible loss of designer control over the dynamically generated storyline. Joint research with industry has suggested a new potential use for Interactive Storytelling technologies, which stands precisely as an assistance to game design. Its basic philosophy is to generate various/all possible solutions to a given game level using the player character as the main agent, and gameplay actions as the basic elements of solution generation. We present a fully-implemented prototype which uses the blockbuster game Hitman as an application. This system uses Heuristic Search Planning to generate level solutions, each legal game action being described as a planning operator. The description of the initial state, the level's objective as well as the level layout, constitute the input data. Other parameters for the simulation include the Hitman's style, which influences the choice of certain actions and privileges a certain style of solution (e.g. stealth versus violent). As a design tool, it seemed appropriate to generate visual output which would be consistent with the current design process. In order to achieve this, we have adapted original Hitman storyboards for their use with a generated solution: we attach elements of storyboards to the planning operators so that a complete solution generates a comic strip similar to an instantiated storyboard for the solution generated. We illustrate system behaviour with specific examples of solution generation.
Constructing Complex NPC Behavior via Multi-Objective Neuroevolution
Authors: Schrum Jacob , Miikkulainen Risto
Published: October 2022

It is difficult to discover effective behavior for NPCs automatically. For instance, evolutionary methods can learn sophisticated behaviors based on a single objective, but realistic game playing requires different behaviors at different times. Such complex behavior is difficult to achieve. What is needed are multi-objective methods that reward different behaviors separately, and allow them to be combined to produce multi-modal behavior. While such methods exist, they have not yet been applied to generating multi-modal behavior for NPCs. This paper presents such an application: In a domain with noisy evaluations and contradictory fitness objectives, evolution based on a scalar fitness function is inferior to multi-objective optimization. The multi-objective approach produces agents that excel at the task and develop complex, interesting behaviors.
Implementation Walkthrough of a Homegrown “Abstract State Machine” Style System in a Commercial Sports Game
Authors: Schwab Brian
Published: October 2022

When I began working at Sony Computer Entertainment of America in 2002, the AI system they were using was very dated. Over the next few years, I designed and developed an almost completely data driven system that has proven to be very powerful, extremely extensible, and designer friendly. This system uses a homegrown data structure, the use of which in many ways resembles the software method of using Abstract State Machines for decomposing complex logical constructs iteratively. This paper will provide an overview of the construction and usage of the system, as well as the pros and cons of this type of game AI engine.
Integrating Story-Centric and Character-Centric Processes for Authoring Interactive Drama
Authors: Si Mei , Marsella Stacy , Riedl Mark
Published: October 2022

Computer aided interactive drama has been widely applied for entertainment and pedagogy. Most existing approaches for authoring interactive drama use either story-centric or character-centric processes. In this work, we present a new framework that integrates both types of processes to support authoring. This framework uses a multi-agent system to control virtual characters in a story. The characters’ motivations are encoded as the agents’ goals, and are configured based on well-structured story paths generated using a partial order planner. This framework allows the use of a planner that has an equivalent model of the story as that in the multi-agent system or models the story at a more abstract level, e.g. only the plot points. We explore the use of this new framework for authoring interactive dramas. Preliminary examples of application are presented.
A Demonstration of Agent Learning with Action-Dependent Learning Rates in Computer Role-Playing Games
Authors: Cutumisu Maria , Szafron Duane
Published: October 2022

We demonstrate combat scenarios between two NPCs in the Neverwinter Nights (NWN) game in which an NPC uses a new learning algorithm ALeRT (Action-dependent Learning Rates with Trends) and the other NPC uses a static strategy (NWN default and optimal) or a dynamic strategy (dynamic scripting). We implemented the ALeRT algorithm in NWScript, a scripting language used by NWN, with the goal to improve the behaviours of game agents. We show how our agent learns and adapts to changes in the environment.
IDtension – Highly Interactive Drama
Authors: Szilas Nicolas
Published: October 2022

IDtension is an Interactive Drama project initiated 10 years ago. It provides long term solutions to the problem of combining narrativity and interactivity. The demo consists of a playable text-based interactive drama called "The Mutiny". The user is given many possibilities of meaningful narrative actions (around 100 in the middle of the story), which are fully interpreted by the narrative engine to generate story events on the fly. IDtension is based on a narrative-centered goal structure, second order narrative predicates and a model of the user. Actions are selected according to an innovative history-based interface.
Examining Extended Dynamic Scripting in a Tactical Game Framework
Authors: Ludwig Jeremy , Farley Arthur
Published: October 2022

Dynamic scripting is a reinforcement learning algorithm designed specifically to learn appropriate tactics for an agent in a modern computer game, such as Neverwinter Nights. This reinforcement learning algorithm has previously been extended to support the automatic construction of new abstract states to improve its context sensitivity and integrated with a graphical behavior modeling architecture to allow for hierarchical dynamic scripting and task decomposition. In this paper, we describe a tactical abstract game representation language that was designed specifically to make it easier to define abstract games that include the large amount of uncertainty found in modern computer games. We then use this framework to examine the effectiveness of the extended version of the dynamic scripting algorithm, using Q-learning and the original dynamic scripting algorithms as benchmarks. Results and discussion are provided for three different abstract games: one based on combat in role-playing games and two based on different aspects of real-time strategy games.
Bringing Interactive Storytelling to Industry: Designing a Reactive Narrative Encounter System
Authors: Kline Daniel
Published: October 2022

The commercial games industry is struggling to find a form of narrative for single-player story-based games that takes advantage of the medium. Current methods are limited to structured narrative or simulation, both of which have serious game design constraints. Drama Management represents a potential new model. This talk will cover the author's research into bringing interactive storytelling to single-player story games. Focusing on the design and production needs of current titles, different groups of pen-and-paper RPG Game Masters were studied, and their interactive narrative approach was algorithmically replicated for video game storytelling on a per-encounter level. To verify these results, an “Encounter Manager” pen-and-paper algorithm was tested in place of a human Game Master, achieving similar storytelling results and exposing new conclusions. Implementing these techniques in several video game projects has shown diverse payoffs, not just in narrative, replayability, and pacing but also in risk-reduction, art production, open world and linear level design, and new game genres. However, the uncertainty and risk of a new model of game production combined with the lack of a driving need in commercial games has so far proven a major hurdle for this approach. Looking forward, similarities with other procedural narrative efforts are explored, and future steps are proposed.
Adapting Game-Playing Agents to Game Requirements
Authors: Jones Joshua , Parnin Chris , Sinharoy Avik , Rugaber Spencer , Goel Ashok
Published: October 2022

We examine the problem of self-adaptation in game-playing agents as the game requirements evolve incrementally. The goal of our current work is to develop an interactive environment in which the game designer generates requirements for a new version of a game, and the legacy software agents from previous versions of the game adapt themselves to the new game requirements. We are developing and testing our metareasoning technique for adapting a game-playing agents in Freeciv, a mature program in the domain of turn-based, multi-player strategy games. In this paper, we first present an analysis of adaptations to FreeCiv, next describe our general approach, and then describe a specific adaptation scenario.
A Synthetic Mind Model for Virtual Actors in Interactive Storytelling Systems
Authors: Araujo Samir , Chaimowicz Luiz
Published: October 2022

Interactive Storytelling (IS) is a growing research area that has attracted a large number of researchers in recent years. However, there are few works exploring the perspective of creating IS systems that conduct emergent stories using autonomous intelligent agents as virtual actors. In this paper, we propose a model of a synthetic mind for virtual actors that can make them interpret a role of a given character in a specific narrative. We discuss the model architecture, implement a prototype, and present some proof-of-concept experiments to show its effectiveness.
WeQuest: A Mobile Alternate Reality Gaming Platform and Intelligent End-User Authoring Tool
Authors: Barve Chinmay , Hajarnis Sanjeet , Karnik Devika , Riedl Mark
Published: October 2022

An Alternate Reality Game (ARG) is an interactive narrative that uses the real world as a platform. An ARG layers a fictional world over the real world such that, as a player moves through the real world, a narrative structure plays out. Although ARGs are growing in popularity, they are significantly limited in several ways that prevent ARGs from being utilized by mainstream game players. First, there is a substantial cost in running an ARG, limiting the number of players that can participate in an ARG at any given time. Second, ARG storylines reference real world geographical locations and landmarks in the real world to advance the narrative structure. In this paper, we introduce a suite of technologies designed to overcome scalability issues of ARGs by automating the delivery of the game and by encouraging end-user authoring of new location-specific storylines.
Modeling User Knowledge with Dynamic Bayesian Networks in Interactive Narrative Environments
Authors: Rowe Jonathan , Lester James
Published: October 2022

Recent years have seen a growing interest in interactive narrative systems that dynamically adapt story experiences in response to users’ actions, preferences, and goals. However, relatively little empirical work has investigated runtime models of user knowledge for informing interactive narrative adaptations. User knowledge about plot scenarios, story environments, and interaction strategies is critical in a range of interactive narrative contexts, such as mystery and detective genre stories, as well as narrative scenarios for education and training. This paper proposes a dynamic Bayesian network approach for modeling user knowledge in interactive narrative environments. A preliminary version of the model has been implemented for the Crystal Island interactive narrative-centered learning environment. Results from an initial empirical evaluation suggest several future directions for the design and evaluation of user knowledge models for guiding interactive narrative generation and adaptation.
A Monte Carlo Approach for Football Play Generation
Authors: Laviers Kennard , Sukthankar Gita
Published: October 2022

Learning effective policies in multi-agent adversarial games is a significant challenge since the search space can be prohibitively large when the actions of all the agents are considered simultaneously. Recent advances in Monte Carlo search methods have produced good results in single-agent games like Go with very large search spaces. In this paper, we propose a variation on the Monte Carlo method, UCT (Upper Confidence Bound Trees), for multi-agent, continuous-valued, adversarial games and demonstrate its utility at generating American football plays for Rush Football 2008. In football, like in many other multi-agent games, the actions of all of the agents are not equally crucial to gameplay success. By automatically identifying key players from historical game play, we can focus the UCT search on player groupings that have the largest impact on yardage gains in a particular formation.
Minstrel Remixed: User Interface and Demonstration
Authors: Tearse Brandon , Mawhorter Peter , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

This demo features a user interface for authoring stories and story fragments for use by the Minstrel Remixed story generation system. It also demonstrates Minstrel Remixed in use, allowing users to author story fragments and then have Minstrel Remixed expand these fragments and generate stories based on them. The focus is on the interface for story-fragment authoring, which exposes Minstrel's graph- of-frames knowledge representation format to the user in an interactive manner. It also exposes Minstrel Remixed's story generation capabilities as they exist currently, including the Author-Level Planning (ALP) and Transform Adapt Recall Methods (TRAM) systems.
A Bayesian Model for Plan Recognition in RTS Games Applied to StarCraft
Authors: Synnaeve Gabriel , Bessière Pierre
Published: October 2022

The task of keyhole (unobtrusive) plan recognition is central to adaptive game AI. “Tech trees” or “build trees” are the core of real-time strategy (RTS) game strategic (long term) planning. This paper presents a generic and simple Bayesian model for RTS build tree prediction from noisy observations, which parameters are learned from replays (game logs). This unsupervised machine learning approach involves minimal work for the game developers as it leverage players’ data (com- mon in RTS). We applied it to StarCraft1 and showed that it yields high quality and robust predictions, that can feed an adaptive AI.
Learning and Evaluating Human-Like NPC Behaviors in Dynamic Games
Authors: Chang Yu-Han , Maheswaran Rajiv , Levinboim Tomer , Rajan Vasudev
Published: October 2022

We address the challenges of evaluating the fidelity of AI agents that are attempting to produce human-like behaviors in games. To create a believable and engaging game play experience, designers must ensure that their non-player characters (NPCs) behave in a human-like manner. Today, with the wide popularity of massively-multi-player online games, this goal may seem less important. However, if we can reliably produce human-like NPCs, this can open up an entirely new genre of game play. In this paper, we focus on emulating human behaviors in strategic game settings, and focus on a Social Ultimatum Game as the testbed for developing and evaluating a set of metrics for comparing various autonomous agents to human behavior collected from live experiments.
A Generative Computational Model for Human Hide and Seek Behavior
Authors: Cenkner Andrew , Bulitko Vadim , Spetch Marcia
Published: October 2022

Hiding and seeking is a cognitive ability frequently demonstrated by humans in both real life and video games. We use machine learning to automatically construct the first computational model of hide/seek behavior in adult humans in a video game like setting. The model is then run generatively in a novel environment and its behavior is found indistinguishable from actual human behavior by a panel of human judges.  In doing so the artificial intelligence agent using the model appears to have passed a version of the Turing test for hiding and seeking.
Knowledge Guided Development of Videogames
Authors: Llansó David , Gómez-Martín Marco , Gómez-Martín Pedro , González-Calero Pedro
Published: October 2022

Due to the changing nature of videogames, the component-based architecture is the design of choice for managing game entities instead of the traditional static class hierarchies. A component-based architecture lets programmers edit entities as collections of components, which provide the entity with new functionalities. Such architecture promotes flexibility but makes the code more difficult to understand because entities are built at runtime by linking components. In this paper we present a semi-automatic process for moving from a class hierarchy to a component-based architecture. Through the application of Formal Concept Analysis we propose a novel technique for automatically identifying candidate distributions of responsibilities among components.
AIIDE-12 Organization
Authors: Riedl Mark , Sukthankar Gita
Published: October 2022

Organizers of the 2012 AIIDE AAAI Conference.
AIIDE 2012 StarCraft Competition
Authors: Buro Michael , Churchill David
Published: October 2022

In 2012, AIIDE will host the Third Annual StarCraft AI Competition. Participants are given the task of building the best performing AI system for the popu- lar real-time strategy game StarCraft Brood War, which was released by Blizzard Entertainment in 1998. The goals of the competition are to provide a testbed for real-time AI systems and to promote game AI research by exhibiting AI techniques such as scripting, planning, optimization, spatial reasoning, and opponent modeling in a fast-paced popular video game.
Invited Talks
Authors: Hilleman Rich , Michalowski Marek , Popovic Zoran , Vilhjalmsson Hannes
Published: October 2022

Abstracts of the invited talks presented at the 2012 AAAI AIIDE Conference.
Statechart-Based AI in Practice
Authors: Dragert Christopher , Kienzle Jorg , Verbrugge Clark
Published: October 2022

Layered Statechart-based AI shows considerable promise by being a highly modular, reusable, and designer friendly approach to game AI. Here we demonstrate the viability of this approach by replicating the functionality of a full-featured and commercial-scale behaviour tree AI within a non-commercial game framework. As well as demonstrating that layered Statecharts are both usable and amply expressive, our experience highlights the value of several, previously unidentified design considerations, such as sensor patterns, the necessity of subsumption, and the utility of orthogonal regions. These observations point towards simplified, higher-level AI construction techniques that can reduce the complexity of AI design and further enhance reuse.
When Players Quit (Playing Scrabble)
Authors: Harrison Brent , Roberts David
Published: October 2022

What features contribute to player enjoyment and player retentionhas been a popular research topic in video games research;however, the question of what causes players to quit agame has received little attention by comparison. In this paper,we examine 5 quantitative features of the game Scrabblesquein order to determine what behaviors are predictors ofa player prematurely ending a game session. We identified afeature transformation that notably improves prediction accuracy.We used a naive Bayes model to determine that there areseveral transformed feature sequences that are accurate predictorsof players terminating game sessions before the endof the game.We also identify several trends that exist in thesesequences to give a more general idea as to what behaviorsare characteristic early indicators of players quitting.
On Case Base Formation in Real-Time Heuristic Search
Authors: Bulitko Vadim , Rayner Chris , Lawrence Ramon
Published: October 2022

Real-time heuristic search algorithms obey a constant limit on planning time per move. Agents using these algorithms can execute each move as it is computed, suggesting a strong potential for application to real-time video-game AI. Recently, a breakthrough in real-time heuristic search performance was achieved through the use of case-based reasoning. In this framework, the agent optimally solves a set of problems and stores their solutions in a case base. Then, given any new problem, it seeks a similar case in the case base and uses its solution as an aid to solve the problem at hand. A number of ad hoc approaches to the case base formation problem have been proposed and empirically shown to perform well. In this paper, we investigate a theoretically driven approach to solving the problem. We mathematically relate properties of a case base to the suboptimality of the solutions it produces and subsequently develop an algorithm that addresses these properties directly. An empirical evaluation shows our new algorithm outperforms the existing state of the art on contemporary video-game pathfinding benchmarks.