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.

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.

Search is a recognized technique for procedural content generation and game design, and it has been used successfully as part of commercial and academic games. In this context, search has almost always referred to selective search, as opposed to larger brute-force searches. The argument against brute-force search is that the state spaces of the games are almost always too large to be amenable for brute-force search. We believe, however, that brute-force search should not be too quickly dismissed. State spaces with trillions or tens of trillions states can now be exhaustively searched with relative ease, and growth in parallelism and computational power is expected to continue to scale this trend. We believe that this, combined with appropriate abstraction, will allow exhaustive search to be applied to many problems once thought to be prohibitively large. We explore this argument in the context of a game called "Fling!," available for mobile devices, showing a system for interactively designing and analyzing puzzles.

We identify an open problem in game design assistance and automation: the development of reusable gameplay trace samplers. Inside many sophisticated content generators and design tools is a component that samples interesting and plausible sequences of player actions. Details and summary properties of these samples are used to assess generated content and to inform designers. As the development of this component is technically involved (sometimes comparable to making a second implementation of a game's mechanics), design tools often either make use of entirely custom, game-specific samplers or make due without the ability to sample interesting traces at all. This severely limits the population who could benefit from automation to those who are motivated to develop it for themselves. We propose the development of reusable samplers to ease the development of future design automation tools. This paper reviews several systems that demonstrate the availability of technology required by these samplers and the range of applications they may serve. It also sketches how future samplers might be architected. This proposal identifies one way for technical research to make progress on design automation challenges without making problematic assumptions about the nature of player behavior or designer intent. Filling in this missing infrastructure, we claim, will make the use of artificial intelligence in the design process more accessible and thus accelerate game design projects.

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.

Player classification allows for considerable improvements on both game analytics and game adaptivity. With this paper we aim at reversing the ad-hoc tendency in player classification methods, by proposing an approach to player classification that can be integrated across different games and genres and is particularly suited to be used by game designers. This paper describes our generic method of interaction-based player classification, which consists of three components: (i) intercepting player interactions, (ii) finding player types through fuzzy cluster analysis and (iii) classification using Hidden Markov Models (HMM). To showcase our method we developed Blindmaze, a simple web-based hidden maze game publicly available, featuring a bounded set of interactions. All data collected from a game is interaction-based, requiring minimal implementation effort from the game developers. It is concluded that our method makes player classification even more available by making it generic and re-usable across different games.

“Game AI” usually brings to mind control of opponents and other characters. We are interested in a different way that AI can intersect games: during the design process. How can retrieval, inference, knowledge representation, learning, and search loosen the bottlenecks in the game design process? How can AI be put to use in ideation, prototyping, feedback, visualization, synthesis and verification of designed artifacts (puzzles, missions, maps, mechanics, stories ...)? How can AI provide assistance to game designers and/or share the creative responsibilities in design?

In this paper we outline a method of procedurally generating maps using Markov Chains. Our method attempts to learn what makes a "good" map from a set of given human-authored maps, and then uses those learned patterns to generate new maps. We present an empirical evaluation using the game "Super Mario Bros.," showing encouraging results.

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.

Non-player character companions in video games should cooperate with players, understand them, and follow their lead during gameplay. In current games, however, companions tend to exhibit mainly static behaviours, and rarely live up to player expectations. In general, our work is aimed at improving this situation, developing both techniques and tools which allow companion NPCs to behave more appropriately, respecting player preferences and offering a more immersive gameplay for players.