Autonomous aesthetic evaluation is the Holy Grail of generative music, and one of the great challenges of computational creativity. Unlike most other computational activities, there is no notion of optimality in evaluating creative output: there are subjective impressions involved, and framing obviously plays a big role. When developing metacreative systems, a purely objective fitness function is not available: the designer is thus faced with how much of their own aesthetic to include. Can a generative system be free of the designer’s bias? This paper presents a system that incorporates an aesthetic selection process that allows for both human-designed and non-human fitness functions.

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.

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.

The word autonomy is often used in the discussion of software-based music-generating systems. Whilst the term conveys a very clear concept — the sense of self-determination of a system — attempts to formalise autonomy are at an early stage, and the term is subject to a range of interpretations when practically applied. We consider how the evaluation of music-generating systems will be enhanced by a clearer understanding of autonomy and its application to music. We discuss existing definitions and approaches to quantifying autonomy and consider, through a series of examples, the information that is required in order to make precise formal judgements about autonomy, and the identification of relevant levels at which the principle of autonomy applies in music. We conclude that automated measures can supplement human evaluation of autonomy, but that (a) automated measures must be supported by sound reasoning about the features and timescales used in the measurement, and (b) they are improved by a having knowledge of the internal working of the system, rather than taking a black box approach. We consider multi-dimensional representations of system behaviour that may capture a richer sense of the notion of autonomy. Finally, we propose an approach to automatically probing music systems as a means of determining an autonomy `portrait'.

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.

Search-based procedural content generation methods allow video games to introduce new content continually, thereby engaging the player for a longer time while reducing the burden on developers. However, games so far have not explored the potential economic value of unique evolved artifacts. Building on this insight, this paper presents for the first time a Facebook game called Petalz in which players can share flowers they breed themselves with other players through a global marketplace. In particular, the market in this social game allows players to set the price of their evolved aesthetically-pleasing flowers in virtual currency. Furthermore, the transaction in which one player buys seeds from another creates a new social element that links the players in the transaction. The combination of unique user-generated content and social gaming in Petalz facilitates meaningful collaboration between users, positively influences the dynamics of the game, and opens new possibilities in digital entertainment.

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.

What features contribute to player enjoyment and player retentionhas been a popular research topic in video games research;however, the question of what causes players to quit agame has received little attention by comparison. In this paper,we examine 5 quantitative features of the game Scrabblesquein order to determine what behaviors are predictors ofa player prematurely ending a game session. We identified afeature transformation that notably improves prediction accuracy.We used a naive Bayes model to determine that there areseveral transformed feature sequences that are accurate predictorsof players terminating game sessions before the endof the game.We also identify several trends that exist in thesesequences to give a more general idea as to what behaviorsare characteristic early indicators of players quitting.

Serious games offer an opportunity for players to learn communication skills by practicing conversations with nonplaying characters (NPCs). To realize this potential, the player needs freedom of play to discover the relationships between its actions and their effects on the partner and the conversation. Scripting is currently the common approach to design in-game dialogue. Although scripting is a robust technique, the approach tends to produce deterministic conversations, allowing little control to the player. It is claimed that a Belief-Desire-Intention (BDI) approach to model the behavior of NPCs allows greater freedom to the player, and delivers better scalability and re-use of dialogues. This claim is evaluated by using BDI in the development of a sales-talk training game in the real-estate domain. It is concluded that BDI enables representative NPCs that respond appropriately and the game allows the player its freedom of choice to explore. The results also showed that BDI brings about new challenges to address, in order to further increase the quality of in-game dialogue.

Many computer games of all genres pit the player against a succession of increasingly difficult challenges such as combat with computer-controlled enemies and puzzles. Part of the fun of computer games is to master the skills necessary to complete the game. Challenge tailoring is the problem of matching the difficulty of skill-based events over the course of a game to a specific player's abilities. We present a tensor factorization approach to predicting player performance in skill-based computer games. Our tensor factorization approach is data-driven and can predict changes in players' skill mastery over time, allowing more accurate tailoring of challenges. We demonstrate the efficacy and scalability of tensor factorization models through an empirical study of human players in a simple role-playing combat game. We further find a significant correlation between these performance ratings and player subjective experiences of difficulty and discuss ways our model can be used to optimize player enjoyment.