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.

This paper presents a tool geared towards the collaboration of a human and an artificial designer for the creation of game content. The framework combines procedural content generation using stochastic search with user input in the form of an initial goal statement as well as preference of generated results. Feedback from industry experts in a pilot user experiment showcased the limitations of this approach and the protocol chosen for evaluating the authoring tool. The limitations are discussed with respect to the suitability of interactive evolution for creative design and the design of experimental protocols for evaluating authoring tools for games.

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.

Spatiotemporal reasoning is a fundamental contributor to effective problem solving. In an effort to design better problem-solving agents, we examined and evaluated the strategies that humans use to solve Tower Defense puzzles, a complex and popular class of real-time strategy games. A consistent and unexpected finding was that humans frequently treated time and space as equivalent. Players stated temporal goals but solved spatial problems. An analysis of human data and computer simulations showed that re-representing temporal problems as spatial problems was beneficial, but treating the two separately can lead to higher scores. The work presented here holds several possibilities for level designers and others who design and analyze maps and spatial arrangements for domains requiring strategic reasoning.

In this paper we introduce a prototype of ’meta-score’, a novel visual editor in PWGL, aimed at defining the structural, temporal and procedural properties of a musical composition. Meta-score is a music notation editor, thus, the score can be created manually by inputting the information using a GUI. However, meta-score extends the concept of a musical score so that the musical content can be defined not only manually but also procedurally. The composition is defined by placing scores (hence the name meta-score) on a timeline, creating dependencies between the objects, and defining the compositional processes associated with them. Meta-score presents the users with a three-stage compositional process beginning from the sketching of the overall structure along with the associated harmonic, rhythmic and melodic material; continuing with the procedural description of the composition and ending with the automatic production of the performance score. In this paper, we describe the present state of meta-score.

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.

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.

Many crowdsourcing systems have a contribution model that is shallow but massively parallel, with contributors rarely processing or iterating upon the work of others. Few systems, even those crowdsourcing creativity or artistic talent, are designed to allow deep chains where the ideas of one individual feed into and directly inspire another individual. To explore the ways in which creative ideas arise and evolve under the influence of specific artifacts created by others, we examine patterns from over 50,000 sketches created and uploaded with Sketch-a-bit, a collaborative mobile drawing application in which each sketch is directly prompted by a previous sketch. In this paper, we report results from two analyses of content created in the system's first two years of deployment. First, we apply qualitative coding to survey the range of effort and creativity in user actions (including actions ranging from unintentioned scribbles to subtly inspired reimaginations of source material through the unexpected preparation of blank canvases for others). Second, we perform an exploratory analysis of large-scale behaviors manifest in chains or trees of sketches (such as open-ended conversations and structured gameplay). The intent of this work is to describe an iterative model of collaborative creativity and to demonstrate a range of remixing behaviors that can be expected to arise in unrestricted, anonymous collaborative creativity applications.

Computational models for goal recognition hold great 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, have been the subjects of extensive investigation in the AI community. However, relatively little effort has been undertaken to examine goal recognition in interactive narrative. In this paper, we propose a research agenda to improve the accuracy of goal recognition models for interactive narratives using explicit representations of narrative structure inspired by the natural language processing community. We describe a particular category of narrative representations, narrative schemas, that we anticipate will effectively capture patterns of player behavior in interactive narratives and improve the accuracy of goal recognition models.

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.