{"title":"Automatic Orchestration for Automatic Composition","authors":"Eliot Handelman, Andie J. Sigler, Davide Donna","doi":"10.1609/aiide.v8i4.12562","DOIUrl":"https://doi.org/10.1609/aiide.v8i4.12562","url":null,"abstract":"\u0000 \u0000 The automatic orchestration problem is that of assigning instruments or sounds to the notes of an unorchestrated score. This is related to, but distinct from, problems of automatic expressive interpretation. A simple algorithm is described that successfully orchestrates scores based on analysis of one musical structure -- the \"Z-chain.\"\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127580573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Metaphor Computing","authors":"Dan-Yang Fu, M. Bishop","doi":"10.1609/aiide.v8i5.12578","DOIUrl":"https://doi.org/10.1609/aiide.v8i5.12578","url":null,"abstract":"\u0000 \u0000 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.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124233419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alok Baikadi, Jonathan P. Rowe, Bradford W. Mott, James C. Lester
{"title":"Improving Goal Recognition in Interactive Narratives with Models of Narrative Discovery Events","authors":"Alok Baikadi, Jonathan P. Rowe, Bradford W. Mott, James C. Lester","doi":"10.1609/aiide.v9i4.12635","DOIUrl":"https://doi.org/10.1609/aiide.v9i4.12635","url":null,"abstract":"\u0000 \u0000 Computational models of goal recognition hold considerable promise for enhancing the capabilities of drama managers and director agents for interactive narratives. The problem of goal recognition, and its more general form plan recognition, has been the subject of extensive investigation in the AI community. However, there have been relatively few empirical investigations of goal recognition models in the intelligent narrative technologies community to date, and little is known about how computational models of interactive narrative can inform goal recognition. In this paper, we investigate a novel goal recognition model based on Markov Logic Networks (MLNs) that leverages narrative discovery events to enrich its representation of narrative state. An empirical evaluation shows that the enriched model outperforms a prior state-of-the-art MLN model in terms of accuracy, convergence rate, and the point of convergence.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127011412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Creative Partnerships with Technology: How Creativity Is Enhanced Through Interactions with Generative Computational Systems","authors":"Andrew R. Brown","doi":"10.1609/aiide.v8i4.12555","DOIUrl":"https://doi.org/10.1609/aiide.v8i4.12555","url":null,"abstract":"\u0000 \u0000 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.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130729059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher Anderson, Arne Eigenfeldt, Philippe Pasquier
{"title":"The Generative Electronic Dance Music Algorithmic System (GEDMAS)","authors":"Christopher Anderson, Arne Eigenfeldt, Philippe Pasquier","doi":"10.1609/aiide.v9i5.12649","DOIUrl":"https://doi.org/10.1609/aiide.v9i5.12649","url":null,"abstract":"\u0000 \u0000 The Generative Electronic Dance Music Algorithmic System (GEDMAS) is a generative music system that composes full Electronic Dance Music (EDM) compositions. The compositions are based on a corpus of transcribed musical data collected through a process of detailed human transcription. This corpus data is used to analyze genre-specific characteristics associated with EDM styles. GEDMAS uses probabilistic and first order Markov chain models to generate song form structures, chord progressions, melodies and rhythms. The system is integrated with Ableton Live, and allows its user to select one or several songs from the corpus, and generate a 16 tracks/parts composition in a few clicks.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132126920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automatic Harmonization Using a Hidden Semi-Markov Model","authors":"Ryan Groves","doi":"10.1609/aiide.v9i5.12654","DOIUrl":"https://doi.org/10.1609/aiide.v9i5.12654","url":null,"abstract":"\u0000 \u0000 Hidden Markov Models have been used frequently in the audio domain to identify underlying musical structure. Much less work has been done in the purely symbolic realm. Recently, a substantial amount of expert-labelled symbolic musical data has been injected into the research community. The new availability of data allows for the application of machine learning models to purely symbolic tasks. Similarly, the continued expansion of the field of machine learning provides new perspectives and implementations of machine learning methods, which are powerful tools when approaching complex musical challenges. This research explores the use of an extended probabilistic model such as the Hidden Semi-Markov Model (HSMM) to approach the task of automatic harmonization. One distinct advantage of the HSMM is that it is able to automatically differentiate harmonic boundaries, through its inclusion of an extra parameter: duration. In this way, a melody can be harmonized automatically in the style of a particular corpus. In the case of this research, the corpus was in the style of Rock 'n' Roll.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132937209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Finding Image Regions with Human Computation and Games with a Purpose","authors":"M. Lux, Mario Guggenberger","doi":"10.1609/aiide.v8i5.12570","DOIUrl":"https://doi.org/10.1609/aiide.v8i5.12570","url":null,"abstract":"\u0000 \u0000 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.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131813139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
António Brisson, Gonçalo Pereira, Rui Prada, Ana Paiva, Sandy Louchart, N. Suttie, Theodore Lim, Ricardo Lopes, Rafael Bidarra, Francesco Bellotti, Milos Kravcik, Manuel Oliveira
{"title":"Artificial Intelligence and Personalization Opportunities for Serious Games","authors":"António Brisson, Gonçalo Pereira, Rui Prada, Ana Paiva, Sandy Louchart, N. Suttie, Theodore Lim, Ricardo Lopes, Rafael Bidarra, Francesco Bellotti, Milos Kravcik, Manuel Oliveira","doi":"10.1609/aiide.v8i5.12576","DOIUrl":"https://doi.org/10.1609/aiide.v8i5.12576","url":null,"abstract":"\u0000 \u0000 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.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129262093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Narrative Extraction, Processing and Generation for Interactive Fiction and Computer Games","authors":"Josep Valls-Vargas","doi":"10.1609/aiide.v9i6.12600","DOIUrl":"https://doi.org/10.1609/aiide.v9i6.12600","url":null,"abstract":"\u0000 \u0000 Often, computer games require meaningful stories and complex worlds in order to successfully engage players. Developing a high-quality story and rich characters can be one of the hardest tasks in the game development process. Narrative is a key element in building game worlds for interactive digital entertainment. I am particularly interested in computational narrative algorithms that can analyze stories, model narrative, and generate plots to be used in various forms and game domains.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124773709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"FreshJam: Suggesting Continuations of Melodic Fragments in a Specific Style","authors":"Tom Collins, Christian Coulon","doi":"10.1609/aiide.v8i4.12553","DOIUrl":"https://doi.org/10.1609/aiide.v8i4.12553","url":null,"abstract":"\u0000 \u0000 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.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128755419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}