bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating Competitive Game Balance with Restricted Play
Authors: Jaffe Alexander , Miller Alex , Andersen Erik , Liu Yun-En , Karlin Anna , Popovic Zoran
Published: October 2022

Game balancing is the fine-tuning phase in which a functioning game is adjusted to be deep, fair, and interesting. Balancing is difficult and time-consuming, as designers must repeatedly tweak parameters, and run lengthy playtests to evaluate the effects of these changes. If designers could receive immediate feedback on their designs, they could explore a vast space of variations, and select only the most promising games for playtesting. Such automated design feedback has been difficult to achieve, as there is no mathematical formulation of game balance that unifies many of its forms. We argue for a formulation in which carefully restricted agents are played against standard agents. We develop this restricted-play balance framework, and evaluate its utility by building a tool capable of calculating measures of balance for a large family of games. By applying this tool to an educational card game, we demonstrate how the framework and tool allow designers to rapidly evaluate and iterate on the balance of their games.
Telling Interactive Player-specific Stories and Planning for It: ASD + PaSSAGE = PAST
Authors: Ramirez Alejandro , Bulitko Vadim
Published: October 2022

Artificial Intelligence (AI) techniques have been used to improve overall player satisfaction in video games. A thriving area of such research is interactive AI-assisted story-telling. One such line of research explored gains of automatically fitting the story to each individual player during the game via player modeling. Another research line used AI planning techniques to create contingency stories at design time. In this paper we propose a principled way to combine these two lines of research. We describe a system that uses a player model both at the design time and at the play time to generate and select stories fitting a specific player. We implement an early prototype and present it in the paper.
Simulating Adaptive Quests for Increased Player Impact in MMORPGs
Authors: Tomai Emmett , Salazar Rosendo
Published: October 2022

In this paper, we present adaptive quests, an extension to the dominant quest model that guides and motivates gameplay in MMORPG shared worlds. The standard model has proven effective, but is significantly incompatible with the desire for player driven change in the world. We present an incremental step to increasing player impact, discuss the problems it creates with the quest model, and show how adaptive quests can help reconcile the two. We present simulation experiments supporting not only that adaptive quests help mitigate those problems, but that they can actually improve them over the standard model.
Procedural Game Adaptation: Framing Experience Management as Changing an MDP
Authors: Thue David , Bulitko Vadim
Published: October 2022

In this paper, we present the Procedural Game Adaptation (PGA) framework, a designer-controlled way to change a game's dynamics during end-user play. We formalize a video game as a Markov Decision Process, and frame the problem as maximizing the reward of a given player by modifying the game's transition function. By learning a model of each player to estimate her rewards, PGA managers can change the game's dynamics in a player-informed way. Following a formal definition of the components of the framework, we illustrate its versatility by using it to represent two existing adaptive systems: PaSSAGE, and Left 4 Dead's AI Director.
Character Networks for Narrative Generation
Authors: Sack Graham
Published: October 2022

In this position paper, the author proposes the use of social networks of characters as an AI narrative generation mechanism. The first part of the paper offers examples of recent research by literary critics on the relationship between character networks and narrative structure. The second part of the paper offers a simple example of story generation based on a structural balance network model.
Preface
Authors: Ware Stephen , Zhu Jichen , Hodhod Rania
Published: October 2022

This workshop aims to continue staying on the forefront of intelligent computational narrative research in terms of both theory and practice. Because the community is already well-established, we included papers that we felt would prompt interesting discussion or advance intelligent narrative research. Fourteen papers were submitted this year. Of those, we accepted 10 as full papers and 3 as posters. We also accepted the submitted demonstration proposal and allotted time for two relevant narrative-related posters from the AIIDE conference to give full talks.
Adversarial Planning for Multi-Agent Pursuit-Evasion Games in Partially Observable Euclidean Space
Authors: Raboin Eric , Kuter Ugur , Nau Dana , Gupta S.
Published: October 2022

We describe a heuristic search technique for multi-agent pursuit-evasion games in partially observable Euclidean space where a team of trackers attempt to minimize their uncertainty about an evasive target. Agents' movement and observation capabilities are restricted by polygonal obstacles, while each agent's knowledge of the other agents is limited to direct observation or periodic updates from team members. Our polynomial-time algorithm is able to generate strategies for games in continuous two-dimensional Euclidean space, an improvement over past algorithms that were only applicable to simple gridworld domains. We demonstrate that our algorithm is tolerant of interruptions in communication between agents, continuing to generate good strategies despite long periods of time where agents are unable to communicate directly. Experiments also show that our technique generates effective strategies quickly, with decision times of less than a second for reasonably sized domains with six or more agents.
CLASSQ-L: A Q-Learning Algorithm for Adversarial Real-Time Strategy Games
Authors: Jaidee Ulit , Munoz-Avila Hector
Published: October 2022

We present CLASSQ-L (for: class Q-learning) an application of the Q-learning reinforcement learning algorithm to play complete Wargus games. Wargus is a real-time strategy game where players control armies consisting of units of different classes (e.g., archers, knights). CLASSQ-L uses a single table for each class  of unit so that each unit is controlled and updates its class’ Q-table. This enables rapid learning as in Wargus there are many units of the same class. We present initial results of CLASSQ-L against a variety of opponents.
Adversarial Policy Switching with Application to RTS Games
Authors: King Brian , Fern Alan , Hostetler Jesse
Published: October 2022

Complex games such as RTS games are naturally formalized as Markov games. Given a Markov game, it is often possible to hand-code or learn a set of policies that capture the diversity of possible strategies. It is also often possible to hand-code or learn an abstract simulator of the game that can estimate the outcome of playing two strategies against one another from any state. We consider how to use such policy sets and simulators to make decisions in large Markov games. Prior work has considered the problem using an approach we call minimax policy switching. At each decision epoch, all policy pairs are simulated against each other from the current state, and the minimax policy is chosen and used to select actions until the next decision epoch. While intuitively appealing, we show that this switching policy can have arbitrarily poor worst case performance. In response, we describe a modified algorithm, monotone policy switching, whose worst case performance, under certain conditions, is provably no worse than the minimax fixed policy in the set. We evaluate these switching policies in both a simulated RTS game and the real game Wargus. The results show the effectiveness of policy switching when the simulator is accurate, and also highlight challenges in the face of inaccurate simulations.
Creative Partnerships with Technology: How Creativity Is Enhanced Through Interactions with Generative Computational Systems
Authors: Brown Andrew
Published: October 2022

This paper discusses emerging creative practices that involve interacting with generative computational systems, and the effect of such cybernetic interactions on our conceptions of creativity and agency. As computing systems have become more powerful in recent years, real time interaction with intelligent computational processes and models has emerged as a basis for innovative creative practices. Examples of these practices include interactive digital media installations, generative art works, live coding performances, virtual theatre, interactive cinema, and adaptive processes in computer games. In these types of activities computational systems have assumed a significant level of agency, or autonomy, that provoke questions about shared authorship and originality that are redefining our relationship with technologies and prompting new questions about human capabilities, values and the meaning of productive activities.
Improvising Musical Structure with Hierarchical Neural Nets
Authors: Smith Benjamin , Garnett Guy
Published: October 2022

Neural networks and recurrent neural networks have been employed to learn, generalize, and generate musical examples and pieces. Yet, these models typically suffer from an inability to characterize and reproduce the long-term dependencies of musical structure, resulting in products that seem to wander aimlessly. We describe and examine three novel hierarchical models that explicitly operate on multiple structural levels. A three layer model is presented, then a weighting policy is added with two different methods of control attempting to maximize global network learning. While the results do not have sufficient structure beyond the phrase or section level, they do evince autonomous generation of recognizable medium-level structures.
Organizing Committee
Authors: Pasquier Philippe
Published: October 2022

List of Organizers and Organizing Committee of the Musical Metacreation 2012 AIIDE Workshop
Finding Image Regions with Human Computation and Games with a Purpose
Authors: Lux Mathias , Müller Alexander , Guggenberger Mario
Published: October 2022

Manual image annotation is a tedious and time-consuming task, while automated methods are error prone and limited in their results. Human computation, and especially games with a purpose, have shown potential to create high quality annotations by "hiding the complexity" of the actual annotation task and employing the "wisdom of the crowds". In this demo paper we present two games with a single purpose: finding regions in images that correspond to given terms. We discuss approach, implementation, and preliminary results of our work and give an outlook to immediate future work.
Modeling Topics in User Dialog for Interactive Tablet Media
Authors: Boteanu Adrian , Chernova Sonia
Published: October 2022

In this paper, we present a set of crowdsourcing and data processing techniques for annotating, segmenting and analyzing spoken dialog data to track topics of discussion between multiple users. Specifically, our system records the dialog between the parent and child as they interact with a reading game on a tablet, crowdsources the audio data to obtain transcribed text, and models topics of discussion from speech transcription using ConceptNet, a freely available commonsense knowledge base. We present preliminary results evaluating our technique using dialog collected using an interactive reading game for children 3-5 years of age. We successfully demonstrate the ability to form discussion topics by grouping words with similar meaning. The presented approach is entirely domain independent and in future work can be applied to a broad range of interactive entertainment applications, such as mobile devices, tablets and games.
Artificial Intelligence and Personalization Opportunities for Serious Games
Authors: Brisson António , Pereira Gonçalo , Prada Rui , Paiva Ana , Louchart Sandy , Suttie Neil , Lim Theo , Lopes Ricardo , Bidarra Rafael , Bellotti Francesco , Kravcik Milos , Oliveira Manuel
Published: October 2022

Effectiveness of Serious Games (SG) depends very much on their capacity to provide the right balance be- tween gaming and educational experience. This require- ment raises challenges regarding realization of their in- telligence and personalization. We aim to overcome the problems of research fragmentation and identify some of the main issues by presenting a summary of relevant contributions from Artificial Intelligence and Personal- ization, together with a discussion on their future direc- tions. In this paper, we summarize approaches to user and learning goals modeling, user engagement, various levels of game adaptation, how new sensors and mobile technology can better identify the context of the user, content adaptation and reusability.
Metaphor Computing
Authors: Fu Dan , Bishop Matt
Published: October 2022

We define metaphor computing as a way to transform difficult computational problems into easier human-solvable problems, and transform solutions back into computational solutions. This report explores initial ideas.
Evaluation of Game Designs for Human Computation
Authors: Carranza Julie , Krause Markus
Published: October 2022

In recent years various games have been developed to generate useful data for scientific and commercial purposes. Current human computation games are tailored around a task they aim to solve, adding game mechanics to conceal monotonous workflows. These gamification approaches, although providing valuable gaming experience, do not cover the wide range of experiences seen in digital games today. This work presents a new use for design concepts for human computation games and an evaluation of player experiences.
Representing the Human to the Systems That They Use
Authors: Cohn Joseph , O'Neill Elizabeth
Published: October 2022

The systems with which humans interact are becoming increasingly complex; there is a corresponding need for such systems to anticipate and understand human action. Current approaches to develop this ability do not robustly represent human users to the systems with which they are interacting. Fusing neurocognitive models with existing approaches may provide an effective way to capture and represent neural action in a way that behaviors can be predicted and shared with the systems that humans are operating.
Organizing Committee
Authors: Swanson Reid , Niehaus James
Published: October 2022

Organizers and Program Committee for the Human Computation and Serious Games 2012 AIIDE Joint Workshop
Toward a Narrative Comprehension Model of Cinematic Generation for 3D Virtual Environments
Authors: Cassell Bradley
Published: October 2022

Most systems for generating cinematic shot sequences for virtual environments focus on the low-level problems of camera placement. While this approach will create a sequence of camera shots which film individual events in a virtual environment, it does not account for the high-level effects shot sequences have on viewer inferences. There are systems which are based on well known cinematography principles such as the rule of thirds and other framing principals, however these usually utilize schemas or predefined shots and do not reason about the high level cognitive effects on the viewer. In this paper a system is proposed which can reason directly about these high-level cognitive and narrative effects of a shot sequence on the viewer’s mental state.
Creating Model-Based Adaptive Environments Using Game-Specific and Game-Dependent Analytics
Authors: Harrison Brent
Published: October 2022

My research involves creating and evaluating adaptive gameenvironments using player models created using data-driventechniques and algorithms. I hypothesize that I will be able tochange parts of a game to elicit certain behaviors from players,and that these changes will also result in an increase ofengagement and/or intrinsic motivation.
Scythe AI: A Tool for Modular Reuse of Game AI
Authors: Dragert Christopher , Kienzle Jorg , Verbrugge Clark
Published: October 2022

Scythe AI is a tool designed for game AI developers to enable modular reuse of NPC AI. In this demonstration, we show how Scythe AI can be used to compose and manage statechart-based AI-Modules to produce new AIs for a target game. The tool includes a streamlined importing process to introduce new modules, library management of modules, an error and warning system to assist in new AI construction, and can export directly to a defined target game.
Preface
Authors: Sukthankar Gita , Horswill Ian
Published: October 2022

The computer game industry has become a multibillion-dollar commercial enterprise, comparable in size and scope to the film industry. Game system requirements are an important driver of hardware and soware innovation in the computer industry, and games have expanded to fill market niches opened by new platforms such as mobile phones, consoles, tablets, and social media. Artificial intelligence is a major component contributing to this success, creating the conditions for more complex virtual environments, realistic non-player characters, and engaging experiences.
Prom Week
Authors: McCoy Josh , Treanor Mike , Samuel Ben , Reed Aaron , Mateas Michael , Wardrip-Fruin Noah
Published: October 2022

This paper outlines Prom Week, a game which leverages the social AI system Comme il Faut (CiF) to create a novel experience in which social relationships are playable.
Learning Interrogation Strategies while Considering Deceptions in Detective Interactive Stories
Authors: Chen Guan-Yi , Kao Edward , Soo Von-Wun
Published: October 2022

The strategies for interactive characters to select appropriate dialogues remain as an open issue in related research areas. In this paper we propose an approach based on reinforcement learning to learn the strategy of interrogation dialogue from one virtual agent toward another. The emotion variation of the suspect agent is modeled with a hazard function, and the detective agent must learn its interrogation strategies based on the emotion state of the suspect agent. The reinforcement learning reward schemes are evaluated to choose the proper reward in the dialogue. Our contribution is twofold. Firstly, we proposed a new framework of reinforcement learning to model dialogue strategies. Secondly, background knowledge and emotion states of agents are brought into the dialogue strategies. The resulted dialogue strategy in our experiment is sensitive in detecting lies from the suspect, and with it the interrogator may receive more correct answer.
Modular Computational Critics for Games
Authors: Osborn Joseph , Grow April , Mateas Michael
Published: October 2022

Formal game modeling tools could support the automated analysis of game rules and rapid automated play-testing, but are not widely used. Furthermore, existing game design support tools are often limited to very specific classes of game, require significant programming expertise to use or customize, or are fully-automatic tools with limited affordances for human designers. We therefore propose a framework for authoring computational game design critics and a new game definition language (Gamelan) grounded in the conventions of board game rules. We show how a set of these critics could have detected specific, attested design problems in the development of Donald X. Vaccarino's influential card game Dominion. We also illustrate an extension of this approach to other collectible card games, turn-taking games, and games in general.
ScriptEase II: Platform Independent Story Creation Using High-Level Patterns
Authors: Schenk Kevin , Lari Adel , Church Matthew , Graves Eric , Duncan Jason , Miller Robin , Desai Neesha , Zhao Richard , Szafron Duane , Carbonaro Mike , Schaeffer Jonathan
Published: October 2022

As the video game industry grows, both developers and creative authors seek new ways to simplify the process of controlling story content using scripts. This paper describes a story model and its software implementation, ScriptEase II, designed to solve this game design bottleneck. ScriptEase II is the second generation of the ScriptEase system, whose goal was to enable game authors with no programming ability to generate scripting code from high-level game patterns. ScriptEase II differs from the original in two important ways. First, ScriptEase II uses game-dependent translators to generate scripts for any game engine. Second, ScriptEase II uses a drag-and-drop interface that simplifies the story component creation menus that grew cumbersome in the original ScriptEase. The feasibility of code generation has been validated using three different game engines and the advantages of the simple drag-and-drop interface have been validated by a user study.
Guided Emotional State Regulation: Understanding and Shaping Players’ Affective Experiences in Digital Games
Authors: Nogueira Pedro , Rodrigues Rui , Oliveira Eugénio , Nacke Lennart
Published: October 2022

Designing adaptive games for individual emotional experiences is a tricky task, especially when detecting a player’s emotional state in real time requires physiological sensing hardware and signal processing software. There is currently a lack of software that can identify and learn how emotional states in games are triggered. To address this problem, we developed a system capable of understanding the fundamental relations between emotional responses and their eliciting events. We propose time-evolving Affective Reaction Models (ARM), which learn new affective reactions and manage conflicting ones. These models are then meant to provide information on how a set of predetermined game parameters (e.g., enemy and item spawning, music and lighting effects) should be adapted, to modulate the player’s emotional state. In this paper, we propose and describe a framework for modulating player emotions and the main components involved in regulating players’ affective experience. We expect our technique will allow game designers to focus on defining high-level rules for generating gameplay experiences instead of having to create and test different content for each player type.
Integrating Monte Carlo Tree Search with Knowledge-Based Methods to Create Engaging Play in a Commercial Mobile Game
Authors: Whitehouse Daniel , Cowling Peter , Powley Edward , Rollason Jeff
Published: October 2022

Monte Carlo Tree Search (MCTS) has produced many recent breakthroughs in game AI research, particularly in computer Go. In this paper we consider how MCTS can be applied to create engaging AI for a popular commercial mobile phone game: Spades by AI Factory, which has been downloaded more than 2.5 million times. In particular, we show how MCTS can be integrated with knowledge-based methods to create an interesting, fun and strong player which makes far fewer plays that could be perceived by human observers as blunders than MCTS without the injection of knowledge. These blunders are particularly noticeable for Spades, where a human player must co-operate with an AI partner. MCTS gives objectively stronger play than the knowledge-based approach used in previous versions of the game and offers the flexibility to customise behaviour whilst maintaining a reusable core, with a reduced development cycle compared to purely knowledge-based techniques.
Mission-Based Scenario Modeling and Generation for Virtual Training
Authors: Luo Linbo , Yin Haiyan , Zhong Jinghui , Cai Wentong , Lees Michael , Zhou Suiping
Published: October 2022

Automated scenario generation for virtual training has become an emerging research problem, as manual authoring is often time consuming and costly. In this paper, we present a mission-based scenario modeling and generation framework for virtual training. In particular, we consider the issue of how the timing of the events in a scenario can impact the training process and how to incorporate such impact into the scenario generation. To this end, our framework is designed to explicitly capture the propagated effect of an event and its influence to other events. For representing mission-based scenarios, the concepts of mission objectives, events, and scenario beats are introduced. The generation process is designed to generate scenarios that can tailor to trainer's preference and adapt to different trainees' skill levels. The efficacy of the proposed framework is demonstrated through an empirical study of human players in a food distribution training game.
Creating Playable Social Experiences through Whole-Body Interaction with Virtual Characters
Authors: Shapiro Daniel , McCoy Josh , Grow April , Samuel Ben , Stern Andrew , Swanson Reid , Treanor Mike , Mateas Michael
Published: October 2022

This paper describes work towards the goal of enabling unscripted interaction with non-player characters in virtual environments.  We hypothesize that we can define a layer of social affordances, based on physical and non-verbal signals exchanged between individuals and groups, which can be reused across games.  We have implemented a first version of that substrate that employs whole body interaction with virtual characters and generates nuanced, real-time character performance in response.  We describe the playable experience produced by the system, the implementation architecture (based on the behavior specification technology used in Façade, the social model employed in Prom Week, and gesture recognition technology), and illustrate the key behaviors and programming idioms that enable character performance.  These idioms include orthogonal coding of attitudes and activities, use of relational rules to nominate social behavior, use of volition rules to rank options, and priority based interleaving of character animations.
A Declarative Domain Model Can Serve as Design Document
Authors: Llansó David , Gómez-Martín Pedro , Gómez-Martín Marco , González-Calero Pedro
Published: October 2022

Detailed design documents have been criticized as a hard to maintain artifacts that may easily become useless while a game under development keeps evolving. In this paper we propose the use of declarative domain modelling as a communication tool and a form of contract between designers and programmers. We show how this model, including entities and actions relevant for the game design, can also serve to support debugging tools for game designers.
Distance-Based Construction Behaviors for Dynamic Grid-Based Worlds
Authors: Myers Charles , Karnick Pushpak
Published: October 2022

This paper proposes a method for the development of AI for autonomous agents in game worlds modeled by fixed regular grids.  This approach uses context behaviors driven by distance maps to support multiple agents in a dynamic environment.  This paper introduces the notion of hierarchical distance maps which allow for higher-level goals to be easily specified by designers.  We also discuss potential applications of our approach in the design and development of agents and behaviors in the block world genre.
Designing Procedurally Generated Levels
Authors: Linden Roland , Lopes Ricardo , Bidarra Rafael
Published: October 2022

There is an increasing demand to improve the procedural generation of game levels. Our approach empowers game designers to author and control level generators, by expressing gameplay-related design constraints. Graph grammars, resulting from these designer-expressed constraints, can generate sequences of desired player actions as well as their associated target content. These action graphs are used to determine layouts and content for game levels. We showcase this approach with a case study on a dungeon crawler game. Results allow us to conclude that our control mechanisms are both expressive and powerful, effectively supporting designers to procedurally generate levels.
Implementation Cost and Efficiency for AI Experience Managers
Authors: Thue David , Bulitko Vadim , Hamilton Howard
Published: October 2022

The study of Artificial Intelligence (AI) experience managers seeks to create software agents that can support compelling, interactive user experiences without needing any online guidance from human experts. Evaluating the utility of such AI managers is important in both academia and industry, both for measuring our progress in the field and for estimating a given manager's practical viability. While several methods have been studied that evaluate a manager's effectiveness, relatively few have explored the question of how costly a manager might be to implement in practice. We explore the latter question in this paper, presenting a formal way to estimate the cost of implementing an AI experience manager at scale.
A Modular Reinforcement Learning Framework for Interactive Narrative Planning
Authors: Rowe Jonathan , Lester James
Published: October 2022

A key functionality provided by interactive narrative systems is narrative adaptation: tailoring story experiences in response to users’ actions and needs. We present a data-driven framework for dynamically tailoring events in interactive narratives using modular reinforcement learning. The framework involves decomposing an interactive narrative into multiple concurrent sub-problems, formalized as adaptable event sequences (AESs). Each AES is modeled as an independent Markov decision process (MDP). Policies for each MDP are induced using a corpus of user interaction data from an interactive narrative system with exploratory narrative adaptation policies. Rewards are computed based on users’ experiential outcomes. Conflicts between multiple policies are handled using arbitration procedures. In addition to introducing the framework, we describe a corpus of user interaction data from a testbed interactive narrative, CRYSTAL ISLAND, for inducing narrative adaptation policies. Empirical findings suggest that the framework can effectively shape users’ interactive narrative experiences.
Organizers
Authors: Cavazza Marc , Si Mei , Zook Alexander
Published: October 2022

List of organizers of the Intelligent Narrative Technologies workshop held at the Ninth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.
Expansion on Description-Based Design of Melodies
Authors: Sarwate Avneesh , Fiebrink Rebecca
Published: October 2022

This work-in-progress paper describes attempted improvements on Pachet’s Description-Based Design (DBD), a system that uses machine learning to generate melodies. We discuss in depth both Description-Based Design and our extensions to Pachet’s original approach. We also present a user study in which users had some success in transforming melodies and describe the implications of these results for future work.
Prediction and Proactivity in Real-Time Interactive Music Systems
Authors: Brown Andrew , Gifford Toby
Published: October 2022

We advocate for the use of predictive techniques in interactive computer music systems. We suggest that the inclusion of prediction can assist in the design of proactive rather than reactive computational performance partners. We summarize the significant role prediction plays in human musical decisions, and the only modest use of prediction in interactive music systems to date. After describing how we are working toward employing predictive processes in our own metacreation software we reflect on future extensions to these approaches.
Towards a Taxonomy of Musical Metacreation: Reflections on the First Musical Metacreation Weekend
Authors: Eigenfeldt Arne , Bown Oliver , Pasquier Philippe , Martin Aengus
Published: October 2022

The Musical Metacreation Weekend (MuMeWe), a series of four concerts presenting works of metacreation, was held in June 2013. These concerts offered an opportunity to review how different composers and system designers are approaching generative practices. We propose a taxonomy of musical metacreation, with specific reference to works and systems presented at MuMeWe, in an effort to begin a discussion of methods of measuring metacreative musical works. The seven stages of this taxonomy are loosely based upon the autonomy of the system, specifically in its ability to make high-level musical decisions during the course of the composition and/or performance. We conclude with a discussion of how these levels could be interpreted, and the potential difficulties involved in their specification and terminology.
Food Opera: A New Genre for Audio-gustatory Expression
Authors: Houge Benjamin , Friedrichs Jutta
Published: October 2022

“Food opera” is the term that the authors have applied to a new genre of audio-gustatory experience, in which a multi-course meal is paired with real-time, algorithmically generated music, deployed over a massively multichannel sound system. This paper presents an overview of the system used to deploy the sonic component of these events, while also exploring the history and creative potential of this unique multisensory format.
Feature Selection and Composition Using PyOracle
Authors: Surges Greg , Dubnov Shlomo
Published: October 2022

A system is described which uses the Audio Oracle algorithm for music analysis and machine improvisation. Some improvements on previous Factor Oracle-based systems are presented, including automatic model calibration based on measures from Music Information Dynamics, facilities for compositional structuring and automation, and an audio-based query mode which uses the input signal to influence the output of the generative system.
Harmonic Navigator: A Gesture-Driven, Corpus-Based Approach to Music Analysis, Composition, and Performance
Authors: Manaris Bill , Johnson David , Vassilandonakis Yiorgos
Published: October 2022

We present a novel, real-time system for exploring harmonic spaces of musical styles, to generate music in collaboration with human performers utilizing gesture devices (such as the Kinect) together with MIDI and OSC instruments / controllers. This corpus-based environment incorporates statistical and evolutionary components for exploring potential flows through harmonic spaces, utilizing power-law (Zipf-based) metrics for fitness evaluation. It supports visual exploration and navigation of harmonic transition probabilities through interactive gesture control. These probabilities are computed from musical corpora (in MIDI format). Herein we utilize the Classical Music Archives 14,000+ MIDI corpus, among others. The user interface supports real-time exploration of the balance between predictability and surprise for musical composition and performance, and may be used in a variety of musical contexts and applications.
Producing Immersive Interactive Narratives in Complex 3D Open Worlds
Authors: Shoulson Alexander
Published: October 2022

Interactive virtual worlds present a unique opportunity for simulation, training, and entertainment in settings that are otherwise prohibitive to recreate or control. In order to produce a truly immersive experience, it is important to populate these 3D environments with fully articulated virtual humans capable of exhibiting functional, purposeful behavior and complex interactions with other objects in the environment. In this paper, we discuss the challenges inherent in conducting interactive narratives in rich virtual worlds with autonomous characters, and present our ongoing work on dynamic story generation using the event-centric authoring paradigm. Unlike traditional character behavior systems, events enable an author to design character interactions using a centralized control structure that temporarily co-opts character autonomy and treats participants as limbs of the same entity. This allows us to create a narrative action space based on complex occurrences an author would like to see in the world, like a riot or protest, rather than in the space of individual character actions like opening a door.
Organizers
Authors: Smith Gillian , Smith Adam
Published: October 2022

List of organizers of the AIIDE-13 Doctoral Consortium held during the Ninth Artificial Intelligence and Interactive Digital Entertainment Conference.
TaskLint: Automated Detection of Ambiguities in Task Instructions
Authors: Manam V. K. Chaithanya , Thomas Joseph Divyan , Quinn Alexander J.
Published: October 2022

Clear instructions are a necessity for obtaining accurate results from crowd workers. Even small ambiguities can force workers to choose an interpretation arbitrarily, resulting in errors and inconsistency. Crisp instructions require significant time to design, test, and iterate. Recent approaches have engaged workers to detect and correct ambiguities. However, this process increases the time and money required to obtain accurate, consistent results. We present TaskLint, a system to automatically detect problems with task instructions. Leveraging a diverse set of existing NLP tools, TaskLint identifies words and sentences that might foretell worker confusion. This is analogous to static analysis tools for code ("linters"), which detect possible features in code that might indicate the presence of bugs. Our evaluation of TaskLint using task instructions created by novices confirms the potential for static tools to improve task clarity and the accuracy of results, while also highlighting several challenges.
HSI: Human Saliency Imitator for Benchmarking Saliency-Based Model Explanations
Authors: Yang Yi , Zheng Yueyuan , Deng Didan , Zhang Jindi , Huang Yongxiang , Yang Yumeng , Hsiao Janet H. , Cao Caleb Chen
Published: October 2022

Model explanations are generated by XAI (explainable AI) methods to help people understand and interpret machine learning models. To study XAI methods from the human perspective, we propose a human-based benchmark dataset, i.e., human saliency benchmark (HSB), for evaluating saliency-based XAI methods. Different from existing human saliency annotations where class-related features are manually and subjectively labeled, this benchmark collects more objective human attention on vision information with a precise eye-tracking device and a novel crowdsourcing experiment. Taking the labor cost of human experiment into consideration, we further explore the potential of utilizing a prediction model trained on HSB to mimic saliency annotating by humans. Hence, a dense prediction problem is formulated, and we propose an encoder-decoder architecture which combines multi-modal and multi-scale features to produce the human saliency maps. Accordingly, a pretraining-finetuning method is designed to address the model training problem. Finally, we arrive at a model trained on HSB named human saliency imitator (HSI). We show, through an extensive evaluation, that HSI can successfully predict human saliency on our HSB dataset, and the HSI-generated human saliency dataset on ImageNet showcases the ability of benchmarking XAI methods both qualitatively and quantitatively.
The Work Exchange: Peer-to-Peer Enterprise Crowdsourcing
Authors: Dill Stephen , Kern Robert , Flint Erika , Cefkin Melissa
Published: October 2022

The Work Exchange is a peer-to-peer enterprise crowdsourcing system, where colleagues within a company can share work with each other. Employees can post work that they need help with, and other employees can search for and select work that they’re interested in. It supports a variety of mechanisms for the work requester to choose who will do the work, including selection, bids, proposals, and contests. A point system is used to encourage participation and track productivity. The Work Exchange is a work in progress, with a prototype ready for demonstration, and a pilot study launching soon within a 500,000 person enterprise.
Discovery of Player Strategies in a Serious Game
Authors: Li Hua , Munoz-Avila Hector , Ke Lei , Symborski Carl , Alonso Rafael
Published: October 2022

Serious games are popular computer games that frequently simulate real-world events or processes designed for the purpose of solving a problem. Although they are often entertaining, their main purpose is to train or educate users. Not surprisingly, users exhibit different game play behaviors because of their diverse background and game experience. To improve the educational effectiveness of these games, it is important to understand and learn from the interaction between the users and the game engine. This paper presents a study attempting to apply machine learning techniques to the game log to discover: a) strategies that are common to players interacting with serious games and b) variances in the demographics of the player base for these strategies. This is an empirical study with end-user data while playing Missing, a serious game developed to help mitigate biases that people may exhibit when analyzing plausible hypothesis for observed events. We found a set of common strategies and interesting variances in player demographics associated with these strategies.
Interpretation of Crowdsourced Activities Using Provenance Network Analysis
Authors: Huynh Trung Dong , Ebden Mark , Venanzi Matteo , Ramchurn Sarvapali , Roberts Stephen , Moreau Luc
Published: October 2022

Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.
Scalable Preference Aggregation in Social Networks
Authors: Dhamal Swapnil , Narahari Y.
Published: October 2022

In social choice theory, preference aggregation refers to computing an aggregate preference over a set of alternatives given individual preferences of all the agents. In real-world scenarios, it may not be feasible to gather preferences from all the agents. Moreover, determining the aggregate preference is computationally intensive. In this paper, we show that the aggregate preference of the agents in a social network can be computed efficiently and with sufficient accuracy using preferences elicited from a small subset of critical nodes in the network. Our methodology uses a model developed based on real-world data obtained using a survey on human subjects, and exploits network structure and homophily of relationships. Our approach guarantees good performance for aggregation rules that satisfy a property which we call expected weak insensitivity. We demonstrate empirically that many practically relevant aggregation rules satisfy this property. We also show that two natural objective functions in this context satisfy certain properties, which makes our methodology attractive for scalable preference aggregation over large scale social networks. We conclude that our approach is superior to random polling while aggregating preferences related to individualistic metrics, whereas random polling is acceptable in the case of social metrics.
CrowdBand: An Automated Crowdsourcing Sound Composition System
Authors: Pietrowicz Mary , Chopra Danish , Sadeghi Amin , Chandra Puneet , Bailey Brian , Karahalios Karrie
Published: October 2022

CrowdBand, a sound composition system, demonstrates how a crowd can create works that meet requested criteria and fulfill the aesthetic character given by keyword description and examples. CrowdBand allows flexibility in music composition in terms of duration of the music, completion time and cost of music composition by giving the requestor two modes - thrifty and normal. CrowdBand’s workflow divides the composition task into three sections: requesting fundamental sounds, assembling sounds into compositions, and evaluating the results. Based on the crowd workers’ responses, we conclude that crowdsourced workers who are non-musicians can design sound and create novel sound compositions through CrowdBand. We also conclude that CrowdBand gives the musically-untrained crowd workers the ability to use common compositional techniques, such as sound layering, vertical stacking of sounds to create harmonic effects, related melodic lines (contrapuntal techniques), and transitions between aesthetic notions, or sound themes. Finally, we show improved, faster results with successive simplification and examples.
99designs: An Analysis of Creative Competition in Crowdsourced Design
Authors: Araujo Ricardo
Published: October 2022

We provide a study of crowdsourced design based on the popular service 99designs, a website where users create design contests for other users to submit their ideas for evaluation, competing for a monetary amount offered. By quantitatively analyzing a large number of contests, we report on the dynamics of the interactions between contests and designers. We show results on the effects of different financial incentives and contests' properties on the number and quality of submissions, as well as on how rewards are distributed across designers. We find that higher financial incentives do not translate to more effort by individual designers, but nonetheless have an impact on the quality outcome of contests by attracting a larger pool of designers. We also show that a majority of contests are dominated by a disproportionate few designers, which are both more active and effective in the service.
Does History Help? An Experiment on How Context Affects Crowdsourcing Dialogue Annotation
Authors: Nouri Elnaz
Published: October 2022

Crowds of people can potentially solve some problems faster than individuals. Crowd sourced data can be leveraged to benefit the crowd by providing information or solutions faster than traditional means. Many tasks needed for developing dialogue systems such as annotation can benefit from crowdsourcing as well. We investigate how to outsource dialogue data annotation through Amazon Mechanical Turk. We are in particular interested in empirically analyzing how much context from previous parts of the dialogue (e.g. previous dialogue turns) is needed to be provided before the target part (dialogue turn) is presented to the annotator. The answer to this question is essentially important for leveraging crowd sourced data for appropriate and efficient response and coordination. We study the effect of presenting different numbers of previous data (turns) to the Turkers in annotating sentiments of dyadic negotiation dialogs on the inter annotator reliability and comparison to the gold standard.
Designing a Crowdsourcing Tool to Analyze Relationships Among Jazz Musicians: The Case of Linked Jazz 52nd Street
Authors: Thorsen Hilary , Pattuelli M.
Published: October 2022

We discuss the design of Linked Jazz 52nd Street, a crowdsourcing tool using linked data technology to build a social network of jazz musicians through the analysis of their personal and professional relationships.
Reducing Error in Context-Sensitive Crowdsourced Tasks
Authors: Haas Daniel , Greenstein Matthew , Kamalov Kainar , Marcus Adam , Olszewski Marek , Piette Marc
Published: October 2022

Most research in quality control in crowdsourced workflows has focused on microtasks, wherein quality can be improved by assigning tasks to multiple workers and interpreting the output as a function of workers' agreement. Not all work fits into microtask frameworks, however, especially work that requires significant training or time per task. In such a context-heavy crowd work system with limited budget for task redundancy, we propose three novel techniques for reducing task error: (1) A self-policing crowd hierarchy in which trusted workers review, correct, and improve entry-level workers' output (2) predictive modeling of task error that improves data quality through targeted redundancy, and (3) holistic modeling of worker performance that supports crowd management strategies designed to improve average crowd worker quality and allocate training to the workers that need the most assistance.