bluuebunny/crossref_metadata_2025 huggingface dataset:
Planning Is the Game: Action Planning as a Design Tool and Game Mechanism
Authors: Kadlec Rudolf , Toth Csaba , Cerny Martin , Bartak Roman , Brom Cyril
Published: October 2022

Recent development in game AI has seen action planning and its derivates being adapted for controlling agents in classical types of games, such as FPSs or RPGs. Complementary, one can seek new types of gameplay elements inspired by planning. We propose and formally define a new game "genre" called anticipation games and demonstrate that planning can be used as their key concept both at design time and run time. In an anticipation game, a human player observes a computer controlled agent or agents, tries to predict their actions and indirectly helps them to achieve their goal. The paper describes an example prototype of an anticipation game we developed. The player helps a burglar steal an artifact from a museum guarded by guard agents. The burglar has incomplete knowledge of the environment and his plan will contain pitfalls. The player has to identify these pitfalls by observing burglar's behavior and change the environment so that the burglar replans and avoids the pitfalls. The game prototype is evaluated in a small-scale human-subject study, which suggests that the anticipation game concept is promising.
Fast Heuristic Search for RTS Game Combat Scenarios
Authors: Churchill David , Saffidine Abdallah , Buro Michael
Published: October 2022

Heuristic search has been very successful in abstract game domains such as Chess and Go. In video games, however, adoption has been slow due to the fact that state and move spaces are much larger, real-time constraints are harsher, and constraints on computational resources are tighter. In this paper we present a fast search method — Alpha-Beta search for durative moves— that can defeat commonly used AI scripts in RTS game combat scenarios of up to 8 vs. 8 units running on a single core in under 5ms per search episode. This performance is achieved by using standard search enhancements such as transposition tables and iterative deepening, and novel usage of combat AI scripts for sorting moves and state evaluation via playouts. We also present evidence that commonly used combat scripts are highly exploitable — opening the door for a promising line of research on opponent combat modelling.
Towards Interest And Engagement, A Framework For Adaptive Storytelling
Authors: Garber-Barron Michael , Si Mei
Published: October 2022

A storyteller builds a narrative that captivates the audience, immersing them in the story. Storytelling is an interactive process. Though the listeners cannot affect what happens in the story, a good narrator observes the audience's responses and adjusts his/her storytelling accordingly. We present an automated storytelling agent that is aimed at achieving the same effect. While presenting a story, the user is given chances to give comments or ask questions. The agent estimates the user's preferences towards various topics from these responses and weighs the factors of novelty, current interest, and consistency for generating the next part of the narration. We describe the components of the agent, and an example of applying it for narrating a Chinese fantasy story.
Towards Adaptive Quest Narrative in Shared, Persistent Virtual Worlds
Authors: Tomai Emmett
Published: October 2022

In this paper, we discuss motivations for studying interactive narrative in shared, persistent worlds using the established conventions of quest-based MMORPGs.  We present a framework for categorizing the various techniques used in these games according to the interaction between the world model and the quest model.  Using this framework we generalize recent games to present a more dynamic world model, and investigate extensions to the quest model to support storytelling through adaptive quest narratives.
Incorporating Search Algorithms into RTS Game Agents
Authors: Churchill David , Buro Michael
Published: October 2022

Real-time strategy (RTS) games are known to be one of the most complex gamegenres for humans to play, as well as one of the most difficult games forcomputer AI agents to play well. To tackle the task of applying AI to RTSgames, recent techniques have focused on a divide-and-conquer approach,splitting the game into strategic components, and developing separate systemsto solve each. This trend gives rise to a new problem: how to tie thesesystems together into a functional real-time strategy game playing agent. Inthis paper we discuss the architecture of UAlbertaBot, our entry into the 2011/2012 StarCraft AI competitions, and the techniques used to include heuristic search based AI systems for the intelligent automation of both build order planning and unit control for combat scenarios.
FreshJam: Suggesting Continuations of Melodic Fragments in a Specific Style
Authors: Collins Tom , Coulon Christian
Published: October 2022

Imagine that a budding composer suffers from writer's block partway through devising a melody. A system called FreshJam is demonstrated, which offers a solution to this problem in the form of an interactive composition assistant; an algorithm that analyzes the notes composed so far, makes a comparison with an indexed corpus of existing music, and suggests a possible next note by choosing randomly among continuations of matched melody fragments. We provide a demonstration of FreshJam as an aid in stylistic composition, and of its potential to be more iterative than existing composition assistants such as PG Music's Band in a Box or Microsoft's Songsmith.
Demo: A Computer-Assisted Approach to Composing with MaestroGenesis
Authors: Szerlip Paul , Hoover Amy , Stanley Kenneth
Published: October 2022

This demonstration presents MaestroGenesis, a program that helps users create complete polyphonic musical pieces from as little as a simple, human composed monophonic melody. MaestroGenesis creates music by exploiting two key ideas behind the functional scaffolding for musical composition (FSMC) approach: (1) that music a function of time and (2) that functional transformations of initial human starting melodies, or scaffolds, inherit some of the essential human qualities contained in the scaffold. Music in FSMC is represented as a functional relationship between the scaffold and a generated accompaniment. The GUI helps users evolve these functions by importing and developing their music through a breeding process akin to animal breeding, called interactive evolutionary computation. Some resulting pieces are indistinguishable from completely human-composed pieces.
Algorithmically Flexible Style Composition Through Multi-Objective Fitness Functions
Authors: Murray Skyler , Ventura Dan
Published: October 2022

Creating a musical fitness function is largely subjective and can be critically affected by the designer's biases. Previous attempts to create such functions for use in genetic algorithms lack scope or are prejudiced to a certain genre of music. They also are limited to producing music strictly in the style determined by the programmer. We show in this paper that musical feature extractors, which avoid the challenges of qualitative judgment, enable creation of a multi-objective function for direct music production. The main result is that the multi-objective fitness function enables creation of music with varying identifiable styles. To demonstrate this, we use three different multi-objective fitness functions to create three distinct sets of musical melodies. We then evaluate the distinctness of these sets using three different approaches: a set of traditional computational clustering metrics; a survey of non-musicians; and analysis by three trained musicians.
The Melody Triangle: Exploring Pattern and Predictability in Music
Authors: Ekeus Henrik , Abdallah Samer , Plumbley Mark , McOwan Peter
Published: October 2022

The Melody Triangle is an interface for the discovery of melodic materials, where the input – positions within a triangle – directly map to information theoretic properties of the output. A model of human expectation and surprise in the perception of music, information dynamics, is used to ‘map out’ a musical generative system’s parameter space. This enables a user to explore the possibilities afforded by a generative algorithm, in this case Markov chains, not by directly selecting parameters, but by specifying the subjective predictability of the output sequence. We describe some of the relevant ideas from information dynamics and how the Melody Triangle is defined in terms of these. We describe its incarnation as a screen based performance tool and compositional aid for the generation of musical textures; the users control at the abstract level of randomness and predictability, and some pilot studies carried out with it. We also briefly outline a multi-user installation, where collabo- ration in a performative setting provides a playful yet informative way to explore expectation and surprise in music, and a forthcoming mobile phone version of the Melody Triangle.
‘Xa-lan’: Algorithmic Generation of Expressive Music Scores Based on Signal Analysis and Graphical Transformations
Authors: Rodriguez Mauricio
Published: October 2022

Xa-lan is a computer program written in Common-LISP to generate music scores with a high level of notational/symbolic expressivity. Generation is driven by audio-analysis of melodic profiles. Once a melodic contour is input to the software, graphic transformations of the original profile stochastically control the different notational elements of the score. The Xa-lan routines display their final output using the ‘Expressive Notation Package’ of PWGL, a LISP-based visual composition environment. A full range of traditional and non-conventional music notation elements can be algorithmically generated with Xa-lan, retrieving to the user a ‘ready-to-play’ or fully ex-pressive music score.
Mezzo: An Adaptive, Real-Time Composition Program for Game Soundtracks
Authors: Brown Daniel
Published: October 2022

Mezzo is a computer program designed that procedurally writes Romantic-Era style music in real-time to accompany computer games. Leitmotivs are associated with game characters and elements, and mapped into various musical forms.  These forms are distinguished by different amounts of harmonic tension and formal regularity, which lets them musically convey various states of markedness which correspond to states in the game story. Because the program is not currently attached to any game or game engine, “virtual” gameplays were been used to explore the capabilities of the program; that is, videos of various game traces were used as proxy examples.  For each game trace, Leitmotivs were input to be associated with characters and game elements, and a set of ‘cues’ was written, consisting of a set of time points at which a new set of game data would be passed to Mezzo to reflect the action of the game trace.  Examples of music composed for one such game trace, a scene from Red Dead Redemption, are given to illustrate the various ways the program maps Leitmotivs into different levels of musical markedness that correspond with the game state.
A Collaborative Puzzle Game to Study Situated Dialog
Authors: Danise Andrew , Striegnitz Kristina
Published: October 2022

This paper describes a prototype of a two-player collaborative 2D puzzle game, designed to elicit task-oriented situated dialog. In this game players use a text-based chat to coordinate their actions in pushing a ball through a maze of obstacles. The game will be used to collect corpora of human-human interactions in this environment. The data will be used to study how language with actions are interleaved and influence each other in situated dialog. The ultimate goal is to build a computational model of these behaviors.
Location-Based Game Platform for Behavioral Data Collection in Disaster Rescue Scenarios
Authors: Frazier Spencer , Huang Chao , Chang Yu-Han , Maheswaran Rajiv
Published: October 2022

Location-based games are an emerging paradigm for training, simulation, entertainment, health and many other domains. In this paper, we consider the role of location-based games as a platform for data collection and analysis of human behavior. We also examine how human teams perform in a disaster scenario when such a scenario is mapped to a game environment conducted as a location-based augmented reality game. We use a pilot experiment to study human behavior between simulated disaster rescue teams and an integrated commander for the purpose of future research into improving exploitation of local tasks versus exploration of assigned objectives by disaster response teams. We show the results of our pilot experiment, analyze the effectiveness of this game as a data collection platform and then investigate how additional experiments may be conducted to formalize this problem further.
Research Summary
Authors: Baier Hendrik
Published: October 2022

Monte-Carlo Tree Search (MCTS) is an online planning algorithm that combines the ideas of best-first tree search and Monte-Carlo evaluation. Since MCTS is based on sampling, it does not require a transition function in explicit form, but only a generative model of the domain. Because it grows a highly selective search tree guided by its samples, it can handle huge search spaces with large branching factors. By using Monte-Carlo playouts, MCTS can take long-term rewards into account even with distant horizons. Combined with multi-armed bandit algorithms to trade off exploration and exploitation, MCTS has been shown to guarantee asymptotic convergence to the optimal policy, while providing approximations when stopped at any time. The relatively new MCTS approach has started a revolution in computer Go. Furthermore, it has achieved considerable success in domains as diverse as the games of Hex, Amazons, LOA, and Ms. Pacman; in General Game Playing, planning, and optimization. Whereas the focus of previous MCTS research has been on the practical application, current research begins to address the problem of understanding the nature, the underlying principles, of MCTS. A careful understanding of MCTS will lead to more effective search algorithms. Hence, my two interrelated research questions are: How can we formulate models that increase our understanding of how MCTS works? and How can we use the developed understanding to create effective search algorithms? This research summary describes the first steps I undertook in these directions, as well as my plans for future work.
Learning Human Motion Models
Authors: Tastan Bulent
Published: October 2022

My research is focused on using human navigation data ingames and simulation to learn motion models from trajectorydata. These motion models can be used to: 1) track the opponent’smovement during periods of network occlusion; 2)learn combat tactics by demonstration; 3) guide the planningprocess when the goal is to intercept the opponent. A trainingset of example motion trajectories is used to learn twotypes of parameterized models: 1) a second order dynamicalsteering model or 2) the reward vector for a Markov DecisionProcess. Candidate paths from the model serve as themotion model in a set of particle filters for predicting the opponent’slocation at different time horizons. Incorporating theproposed motion models into game bots allows them to customizestheir tactics for specific human players and functionas more capable teammates and adversaries.
Assistant Agents for Sequential Planning Problems
Authors: Macindoe Owen
Published: October 2022

The problem of optimal planning under uncertainty in collaborative multi-agent domains is known to be deeply intractable but still demands a solution. This thesis will explore principled approximation methods that yield tractable approaches to planning for AI assistants, which allow them to understand the intentions of humans and help them achieve their goals. AI assistants are ubiquitous in video games, mak- ing them attractive domains for applying these planning techniques. However, games are also challenging domains, typically having very large state spaces and long planning horizons. The approaches in this thesis will leverage recent advances in Monte-Carlo search, approximation of stochastic dynamics by deterministic dynamics, and hierarchical action representation, to handle domains that are too complex for existing state of the art planners. These planning techniques will be demonstrated across a range of video game domains.
Toward a Computational Model of Character Personality for Planning-Based Narrative Generation
Authors: Bahamon Julio
Published: October 2022

Authoring narrative content for interactive digital media can be both difficult and time consuming.The research proposed here aims at enhancing the capabilities of content creators through the development of a computational model that improves the quality of automatically generated stories, potentially decreasing the burden placed on the author. The quality and believability of a story can be significantly enhanced by the presence of compelling characters. To achieve this objective, I aim to develop a choice-based computational model that facilitates the automatic generation of narrative that includes characters that are made more compelling by the presence of distinct personality characteristics.
Representing and Generating Maps of Large-Scale Virtual Environments for Intelligent Mobile Agents
Authors: Samperi Katrina
Published: October 2022

This paper looks at the challenges and solutions for intelligent mobile agents existing in virtual environments. Representing and using a map of these very large-scale, dynamic environments is a key challenge in providing an autonomous agent for large, online worlds. We look at a method for improving the generation of probabilistic roadmaps by observing and using the movements of other avatars in an environment. We also look at methods for segmenting and scaling the map in very large environments. Finally we extend this to look at generating and updating maps in dynamic environments.
Viewpoints AI: Demonstration
Authors: Jacob Mikhail , Coisne Gaëtan , Gupta Akshay , Sysoev Ivan , Verma Gaurav , Magerko Brian
Published: October 2022

This article describes a technical approach to movement-based interactions between a human interactor and an intelligent agent based on the theatrical Viewpoints movement framework. The Viewpoints AI system features procedural gesture interpretation using shallow semantics and deep aesthetics analysis from the Viewpoints framework. The installation creates a liminal virtual / real space for the human and AI to interact by the use of digital projection for the AI visualization and shadow play to represent the human. Observations from a recent public demonstration of the system and future directions of work are also discussed.
Sponsors
Authors: Sukthankar Gita
Published: October 2022

List of sponsors of the Ninth Artificial Intelligence and Interactive Digital Entertainment Conference.
Towards a Player Age Model
Authors: Tekofsky Shoshannah , Spronck Pieter , Goudbeek Martijn , Broersen Jan
Published: October 2022

This paper proposes a Player Age (PA) model with the potential to be generalized to many different games. The model offers insight into the relationship between age and play style. Game developers can use the PA model to gain a better understanding of their target audience, and to optimize adaptive game features (i.e., AI, targeted marketing). In order to become generically applicable, the PA model is based on the literature on life-span developments in physiology and psychology. The PA model states that player age is a linear function of four factors: Speed of Play (-), Performance (-), Preference (+/-), and Time Played (+/-). The model is validated on a data set from Battlefield 3 (FPS). It explains 33.7% of the variance in age (range: 12-65 years) with a standard error of 6.743. To determine the generic quality of the PA model, future work will validate it on games of other genres.
Data-Driven Personalized Drama Management
Authors: Yu Hong , Riedl Mark
Published: October 2022

A drama manager is an omniscient background agent responsible for guiding players through the story space and delivering an enjoyable and coherent experience. Most previous drama managers only consider the designer's intent. We present a drama manager that uses data-driven techniques to model players and provides personalized guidance in the story space without removing player agency. In order to guide players' experiences, our drama manager manipulates the story space to maximize the probability of the players making choices intended by the drama manager. Our system is evaluated on an interactive storytelling game. Results show that our drama manager can significantly increase the likelihood of the drama manager's desired story continuation.
Decision Making Styles as Deviation from Rational Action: A Super Mario Case Study
Authors: Holmgård Christoffer , Togelius Julian , Yannakakis Georgios
Published: October 2022

In this paper we describe a method of modeling play styles as deviations from approximations of game theoretically rational actions. These deviations are interpreted as containing information about player skill and player decision making style. We hypothesize that this information is useful for differentiating between players and for understanding why human player behavior is attributed intentionality which we argue is a prerequisite for believability. To investigate these hypotheses we describe an experiment comparing 400 games in the Mario AI Benchmark testbed, played by humans, with equivalent games played by an approximately game theoretically rationally playing AI agent. The player actions’ deviations from the rational agent’s actions are subjected to feature extraction, and the resulting features are used to cluster play sessions into expressions of different play styles. We discuss how these styles differ, and how believable agent behavior might be approached by using these styles as an outset for a planning agent. Finally, we discuss the implications of making assumptions about rational game play and the problematic aspects of inferring player intentions from behavior.
Reinforcement Learning for Spatial Reasoning in Strategy Games
Authors: Leece Michael , Jhala Arnav
Published: October 2022

One of the major weaknesses of current real-time strategy (RTS) game agents is handling spatial reasoning at a high level. One challenge in developing spatial reasoning modules for RTS agents is to evaluate the ability of a given agent for this competency due to the inevitable confounding factors created by the complexity of these agents. We propose a simplified game that mimics spatial reasoning aspects of more complex games, while removing other complexities. Within this framework, we analyze the effectiveness of classical reinforcement learning for spatial management in order to build a detailed evaluative standard across a broad set of opponent strategies. We show that against a suite of opponents with fixed strategies, basic Q-learning is able to learn strategies to beat each. In addition, we demonstrate that performance against unseen strategies improves with prior training from other distinct strategies. We also test a modification of the basic algorithm to include multiple actors, to speed learning and increase scalability. Finally, we discuss the potential for knowledge transfer to more complex games with similar components.
Inferring Performer Skill from Aesthetic Quality Features in a Dance Game Gesture Corpus
Authors: Maraffi Christopher , Ishikawa Sascha , Jhala Arnav
Published: October 2022

In this paper, we describe experiments for inferring the artistic skill of performers by analyzing pose features in a gesture corpus. Poses were generated by having participants play a popular Kinect dance game in a markerless motion capture studio. Skeletal data was analyzed for features derived from both statistical analysis as well as arts and animation theory, and aesthetic metrics were designed to score pose features along three dimensions: balance,asymmetry, and readability. We applied our metrics to poses in a corpus of 10,080 annotated frames generated from 20 dance performances ranked according to the performing arts background of each participant. This work is the foundation of a computational performatology approach to quantifying artistic gesture in media by identifying aesthetic features that indicate figurative quality to viewers. The potential application of gesture analysis and feedback will be to inform the design of performative logics for virtual controlof avatars and non-player characters in videogames.
Designer Modeling for Personalized Game Content Creation Tools
Authors: Liapis Antonios , Yannakakis Georgios , Togelius Julian
Published: October 2022

With the growing use of automated content creation and computer-aided design tools in game development, there is potential for enhancing the design process through personalized interactions between the software and the game developer. This paper proposes designer modeling for capturing the designer's preferences, goals and processes from their interaction with a computer-aided design tool, and suggests methods and domains within game development where such a model can be applied. We describe how designer modeling could be integrated with current work on automated and mixed-initiative content creation, and envision future directions which focus on personalizing the processes to a designer's particular wishes.
Organizers
Authors: Liapis Antonios
Published: October 2022

List of organizers of the Artificial Intelligence and Game Aesthetics 2013 workshop held at AIIDE-13.
Organizers
Authors: Nelson Mark
Published: October 2022

List of organizers of the AI in the Game Design Process workshop held at the Ninth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.
Using Interactive Narrative in Health Interventions: A Drama Manager Approach
Authors: Yin Langxuan , Montfort Nick , Bickmore Timothy
Published: October 2022

In this work, we propose integrating a drama manager into a visual novel to guide both storytelling and health intervention. We discuss certain narrative structures that can be used to guide a visual-novel-based health intervention and can potentially benefit future drama manager research.
Integrating Formal Qualitative Analysis Techniques within a Procedural Narrative Generation System
Authors: Kybartas Ben , Verbrugge Clark
Published: October 2022

Qualitative analysis of procedurally generated narratives remains a difficult hurdle for most narrative generation tools. Typical analysis involves the use of human studies, rating the quality of the generated narratives against a given set of criteria, a costly and time consuming process. In this paper we integrate a set of features within the ReGEN system which aim to ensure narrative correctness and quality. Correct generation is ensured by performing an analysis of the preconditions and postconditions of each narrative event. Narrative quality is ensured by using an existing set of formal metrics which relate quality to the structure of the narrative to guide narrative generation. This quantitative approach provides an objective means of guaranteeing quality within narrative generation.
Modelling Character Knowledge in Plan-Based Interactive Narrative to Extend Accomodative Mediation
Authors: Robertson Justus , Young Robert
Published: October 2022

In plan-based interactive narrative systems, user actions must be integrated with an ideal story structure in order to strike a reasonable balance between user agency and story quality. A process called mediation addresses this issue by selecting appropriate responses to possible detrimental user actions in a given interactive narrative plan. One way the algorithm mediates exceptional user activity is accommodation, the process of replanning future events to incorporate an unwanted action and accomplish the story's authorial goals. However, this approach is limited by an underlying assumption that the story world exists as an object outside the user's subjective experience. In this paper, we incorporate a model of user knowledge into plan-based mediation in order to widen the space of plans the algorithm may search during accommodation by allowing the process to replan past events that occur outside a user's imperfect knowledge of the story world.
CONTACT: A Multimodal Corpus for Studying Expressive Styles and Informing the Design of Individualized Virtual Narrators
Authors: Mbengue Cheikh , Gómez Jáuregui David , Martin Jean-Claude
Published: October 2022

Virtual agents used in storytelling applications should display consistent and natural multimodal expressions of emotions. In this paper, we describe the method that we defined to endow virtual narrators with individual gesture profiles. We explain how we collected a corpus of gestural behaviors displayed by different actors telling the same story. Videos were annotated both manually and automatically. Preliminary analyses are presented.
Ember, Toward Salience-Based Cinematic Generation
Authors: Cassell Bradley , Young R.
Published: October 2022

Automatic cinematic generation for virtual environments and games has shown to be capable of generating general purpose cinematics. There is initial work that approaches cinematic generation from the perspective of narrative instead of low level camera manipulation. In our work, we further extend this idea to take into consideration a model of user memory. Models of reader comprehension and memory have been developed to attempt to explain how people comprehend narratives. We propose using these models of narrative comprehension and memory to augment a system for cinematic generation so that it can produce a cinematic that can communicate character deliberation to the viewer by maintaining the salience of specific events.
A Call for Emotion Modeling in Interactive Storytelling
Authors: Poo Hernandez Sergio , Bulitko Vadim
Published: October 2022

Artificial Intelligence (AI) techniques are widely used in video games. Recently, AI planning methods have been applied to maintain plot consistency in the face of player’s agency over the narrative. Combined with an automatically populated player model, such AI experience managers can dynamically create a consistent narrative tailored to a specific player. These tools help game narrative designers achieve narrative goals while affording players a choice. On the other hand, they increase the number of feasible plot branches making it more difficult for the author to ensure that each branch carries the player along a desired emotion arc. In this paper we discuss the problem and call for an extension of experience managers with player emotion models. When successful, interactive narrative can be then automatically produced to satisfy authorial goals not only in terms of specific events but also in terms of emotions evoked in the player.
Preface
Authors: Cavazza Marc , Si Mei , Zook Alexander
Published: October 2022

The Intelligent Narrative Technologies workshop strives to be at the forefront of research on devel- oping and examining intelligent and adaptive narrative systems.
Composing with All Sound Using the FreeSound and Wordnik APIs
Authors: Merz Evan
Published: October 2022

In order to create algorithmic art using the wealth of documents available on the internet, artists must discover strategies for organizing those documents. In this paper I demonstrate techniques for organizing and collaging sounds from the user-contributed database at freesound.org. Sounds can be organized in a graph structure by exploiting aural similarity relationships provided by freesound.org, and lexical relationships provided by wordnik.com. Music can then be generated from these graphs in a variety of ways. In the final section, I elaborate on three pieces I've generated from sound graphs using cellular automata, and swarm intelligence.
Tracking Creative Musical Structure: The Hunt for the Intrinsically Motivated Generative Agent
Authors: Smith Benjamin
Published: October 2022

Neural networks have been employed to learn, generalize, and generate musical pieces with a constrained notion of creativity. Yet, these computational models typically suffer from an inability to characterize and reproduce long-term dependencies indicative of musical structure. Hierarchical and deep learning models propose to remedy this deficiency, but remain to be adequately proven. We describe and examine a novel dynamic bayesian network model with the goal of learning and reproducing longer-term formal musical structures. Incorporating a computational model of intrinsic motivation and novelty, this hierarchical probabilistic model is able to generate pastiches based on exemplars.
The Musical Metacreation Weekend: Challenges Arising from the Live Presentation of Musically Metacreative Systems
Authors: Bown Oliver , Eigenfeldt Arne , Martin Aengus , Carey Benjamin , Pasquier Philippe
Published: October 2022

This paper discusses the approach to curation of the MuMe Weekend (held in Sydney, Australia, 2013) and the experience the authors gained from hosting the event. We identify open challenges arising from this applied demonstration of musically metacreative systems. Identified challenges are not technical, but rather methodological, concerning pragmatic aspects of presenting and collaboratively innovating musically metacreative work. The paper presents our approach to curating the event, the categories of performances offered — and responses to these — issues to do with the selection and presentation of the work, and issues to do with the evaluation by audience and performers.
Methods for the Flexible Parameterisation of Musical Material in Ableton Live
Authors: Bown Oliver , Britton Sam
Published: October 2022

We present a set of tools that were developed for an exper- imental music project in which an album was created with 1,000 distinct variations and released as a digital limited edi- tion. The tools enable the parameterisation of musical com- positions created in the popular music software Ableton Live such that timelines of musical elements can be composed as keyframes and interpolated between.
Jazz Drum Machine: A Novel Interface for Data-Driven Remixing of Music
Authors: Lacker Ben
Published: October 2022

Jazz Drum Machine is a web application written in Javascript that uses the Echo Nest Remix API to intelligently remix music. An input song is segmented into discrete musical events, which are then grouped according to pitch. Users are presented with an interface that allows them to select the number of segments from each group that will be heard in the final mix. The final mix is generated in real time and preserves rhythmic features extracted from the original song while reordering segments based on user input.
The Human, the Mechanical, and the Spaces in Between: Explorations in Human-Robotic Musical Improvisation
Authors: Barton Scott
Published: October 2022

HARMI (Human and Robotic Musical Improvisation) is a software and hardware system that enables musical robots to improvise with human performers. The goal of the system is not to replicate human musicians, but rather to explore the novel kinds of musical expression that machines can produce. At the same time, the system seeks to create spaces where humans and robots can communicate with each other in a common language. To help achieve the former, ideas from contemporary compositional practice and music theory were used to shape the system’s expressive capabilities. In regard to the latter, research from the field of cognitive psychology was incorporated to enable communication, interaction, and understanding between human and robotic performers. The system was partly developed in conjunction with a residency at High Concept Laboratories in Chicago, IL, where a group of human improvisers performed with the robotic instruments. The system represents an approach to the question of how humans and robots can interact and improvise in musical contexts. This approach purports to highlight the unique expressive spaces of humans, the unique expressive spaces of machines, and the shared spaces between the two.
Human-Computer Co-Creativity: Blending Human and Computational Creativity
Authors: Davis Nicholas
Published: October 2022

This paper describes a thesis exploring how computer programs can collaborate as equals in the artistic creative process. The proposed system, CoCo Sketch, encodes some rudimentary stylistic rules of abstract sketching and music theory to contribute supplemental lines and music while the user sketches. We describe a three-part research method that includes defining rudimentary stylistic rules for abstract line drawing, exploring the interaction design for artistic improvisation with a computer, and evaluating how CoCo Sketch affects the artistic creative process. We report on the initial results of early investigations into artistic style that describe cognitive, perceptual, and behavioral processes used in abstract artists making.
CHIME: Causal Human-in-the-Loop Model Explanations
Authors: Biswas Shreyan , Corti Lorenzo , Buijsman Stefan , Yang Jie
Published: October 2022

Explaining the behaviour of Artificial Intelligence models has become a necessity. Their opaqueness and fragility are not tolerable in high-stakes domains especially. Although considerable progress is being made in the field of Explainable Artificial Intelligence, scholars have demonstrated limits and flaws of existing approaches: explanations requiring further interpretation, non-standardised explanatory format, and overall fragility. In light of this fragmentation, we turn to the field of philosophy of science to understand what constitutes a good explanation, that is, a generalisation that covers both the actual outcome and, possibly multiple, counterfactual outcomes. Inspired by this, we propose CHIME: a human-in-the-loop, post-hoc approach focused on creating such explanations by establishing the causal features in the input. We first elicit people's cognitive abilities to understand what parts of the input the model might be attending to. Then, through Causal Discovery we uncover the underlying causal graph relating the different concepts. Finally, with such a structure, we compute the causal effects different concepts have towards a model's outcome. We evaluate the Fidelity, Coherence, and Accuracy of the explanations obtained with CHIME with respect to two state-of-the-art Computer Vision models trained on real-world image data sets. We found evidence that the explanations reflect the causal concepts tied to a model's prediction, both in terms of causal strength and accuracy.
Eliciting and Learning with Soft Labels from Every Annotator
Authors: Collins Katherine M. , Bhatt Umang , Weller Adrian
Published: October 2022

The labels used to train machine learning (ML) models are of paramount importance. Typically for ML classification tasks, datasets contain hard labels, yet learning using soft labels has been shown to yield benefits for model generalization, robustness, and calibration. Earlier work found success in forming soft labels from multiple annotators' hard labels; however, this approach may not converge to the best labels and necessitates many annotators, which can be expensive and inefficient. We focus on efficiently eliciting soft labels from individual annotators. We collect and release a dataset of soft labels (which we call CIFAR-10S) over the CIFAR-10 test set via a crowdsourcing study (N=248). We demonstrate that learning with our labels achieves comparable model performance to prior approaches while requiring far fewer annotators -- albeit with significant temporal costs per elicitation. Our elicitation methodology therefore shows nuanced promise in enabling practitioners to enjoy the benefits of improved model performance and reliability with fewer annotators, and serves as a guide for future dataset curators on the benefits of leveraging richer information, such as categorical uncertainty, from individual annotators.
Cobi: Community-Informed Conference Scheduling
Authors: Kim Juho , Zhang Haoqi , André Paul , Chilton Lydia , Bhardwaj Anant , Karger David , Dow Steven , Miller Robert
Published: October 2022

Creating a schedule for a large multi-track conference requires considering the preferences and constraints of organizers, authors, and attendees. Traditionally, a few dedicated organizers manage the size and complexity of the schedule with limited information and coverage. Cobi presents an alternative approach to conference scheduling by engaging the entire community to take active roles in the planning process. It consists of a collection of crowdsourcing applications that elicit preferences and constraints from the community, and software that enable organizers and other community members to take informed actions based on collected information.
Wanted: More Nails for the Hammer — An Investigation Into the Application of Human Computation
Authors: Brem Elizabeth , Bick Tyler , Schriner Andrew , Oerther Daniel
Published: October 2022

Researchers studying human computation and crowdsourcing know it as a useful tool for solving data analysis problems. Outside of this community, however, we ask the question ‘how many people with data analysis problems know how to use human computation and crowdsourcing?’ In this paper, we observe trends in academic papers on the topic of human computation and crowdsourcing, conduct a survey, and examine the growth of the human computation and crowdsourcing market by researching recent market reports in order to test the hypothesis that human computation and crowdsourcing is not a widely used tool despite its usefulness for problem solving.
Frenzy: A Platform for Friendsourcing
Authors: Chilton Lydia , Cordeiro Felicia , Weld Daniel , Landay James
Published: October 2022

Although micro-task crowdwork was popularized by the Mechanical Turk (MTurk) labor-market platform, it is useful in many other contexts and communities as well. Unfortunately, several MTurk design choices, such as worker anonymity and isolation, are problematic in other environments. This paper introduces Frenzy, a platform for friendsourcing. Frenzy helps people who are closely connected in a community complete crowdsourcing tasks on a platform that affords more user control and freedom than traditional crowdsourcing platforms. Our evaluation of Frenzy shows that friendworkers can find meaningful work to do, effectively use discussion to reduce uncertainty, and finish the task. Most importantly, they are happy with the quality of the output and indicate a desire to use Frenzy for their own tasks such as: creating wedding albums, coding social science data, and creating sessions of accepted conference papers.
Disco: Workshop on Human and Machine Learning in Games
Authors: Krause Markus , Bry François , Georgescu Mihai
Published: October 2022

Exploiting the playfulness of games has been extremely successful in bringing humans “in the loop” to solve com­plex computational tasks that would otherwise be hardly tractable. Although many proposals and systems after this paradigm have been developed, deployed, and tested, the relationship between play and human computation still de­serves more investigations. Most work in human computa­tion focuses on the ability for the machine to exploit, or learn from, humans. The workshop has a slightly different focus: the exploration of extending “I learn” (“disco” in Latin) to machines and humans alike. Games hold tremen­dous potential for discovery related to human and machine computation because of the intrinsic relation between play and learning. Extending and building upon the focus of past workshops on games and human computation Disco aims at exploring the intersection of entertainment, learning and human computation.
Preface
Authors: Hartman Björn , Horvitz Eric
Published: October 2022

The HCOMP conference was created as a venue for exchanging ideas and developments on principles, experiments, and implementations of systems that rely on programmatic access to human intellect to perform some aspect of computation, or where human perception, knowledge, reasoning, or coordinated physical activities contributes to the operation of larger systems and applications. Topics covered in the evolving discipline of human computation and crowdsourcing include efforts in human-computer interaction (HCI), computer-supported collaborative work (CSCW), cognitive psychology, organizational behavior, economics, information retrieval, databases, computer systems, optimization, and multiple areas of artificial intelligence (AI), such as vision, speech, robotics, machine learning, mechanism and market design, and planning. The links to AI are as strong as those to HCI, CSCW, and economics; human computation promises to play an important role in research on principles of artificial intelligence as well as in the engineering of systems that can take advantage of the (changing) complementarities of human and machine intellect.
nEmesis: Which Restaurants Should You Avoid Today?
Authors: Sadilek Adam , Brennan Sean , Kautz Henry , Silenzio Vincent
Published: October 2022

Computational approaches to health monitoring and epidemiology continue to evolve rapidly. We present an end-to-end system, nEmesis, that automatically identifies restaurants posing public health risks. Leveraging a language model of Twitter users' online communication, nEmesis finds individuals who are likely suffering from a foodborne illness. People's visits to restaurants are modeled by matching GPS data embedded in the messages with restaurant addresses. As a result, we can assign each venue a "health score" based on the proportion of customers that fell ill shortly after visiting it. Statistical analysis reveals that our inferred health score correlates (r = 0.30) with the official inspection data from the Department of Health and Mental Hygiene (DOHMH). We investigate the joint associations of multiple factors mined from online data with the DOHMH violation scores and find that over 23% of variance can be explained by our factors. We demonstrate that readily accessible online data can be used to detect cases of foodborne illness in a timely manner. This approach offers an inexpensive way to enhance current methods to monitor food safety (e.g., adaptive inspections) and identify potentially problematic venues in near-real time.
Crowdsourcing Spatial Phenomena Using Trust-Based Heteroskedastic Gaussian Processes
Authors: Venanzi Matteo , Rogers Alex , Jennings Nicholas
Published: October 2022

Many crowdsourcing applications require spatial data modelling to make sense of location-based observations provided by multiple users. In this context, we propose a new spatial function modelling approach to address the problem of fusing multiple spatial observations reported by possibly untrustworthy users in the domains of participatory sensing and crowdsourcing applications. Specifically, we use a heteroskedastic Gaussian process model to incorporate user trust modelling into Bayesian spatial regression. In particular, by training the model with the reports gathered from the crowd, we are able to estimate the spatial function at any location of interest and also learn the level of trustworthiness of each user. We show that our method outperforms other standard homoskedastic and heteroskedastic Gaussian processes by up to 23% on a crowdsourced radiation dataset collected during the 2011 Fukushima earthquake in Japan. We also show that our method is able to improve the quality of spatial predictions on synthetic data by up to 70% and is robust in settings of up to 30% presence of untrustworthy users within the crowd.