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.

Around the same time, a system called Player-Specific From Shakespeare's "Romeo and Juliet" to George Lucas' Stories via Automatically Generated Events (PaSSAGE) "Star Wars" to BioWare's "Jade Empire" to campfire stories (Thue et al. 2007) was proposed, which used AI techniques to baseball commentary, story-telling is a fundamental to model the player as he/she experiences a narrative-rich part of entertainment. A strong narrative resonates with our video game. Such a continuously updated player model was minds, hearts and souls and keeps us engaged. We remember used to dynamically adapt the story, tailoring it to the current the stories of our childhood and retell them to our own player. Unlike, ASD, PaSSAGE did not have any automation children. Story-telling has delighted and saddened the human at the design stage and relied on a human designer to race since the beginning of time and shows no signs of foresee all possible ways of a player breaking the story and slowing down. But can it be improved with technology?

Layered Statechart-based AI shows considerable promise by being a highly modular, reusable, and designer friendly approach to game AI. Here we demonstrate the viability of this approach by replicating the functionality of a full-featured and commercial-scale behaviour tree AI within a non-commercial game framework. As well as demonstrating that layered Statecharts are both usable and amply expressive, our experience highlights the value of several, previously unidentified design considerations, such as sensor patterns, the necessity of subsumption, and the utility of orthogonal regions. These observations point towards simplified, higher-level AI construction techniques that can reduce the complexity of AI design and further enhance reuse.

Adapting game content to a particular player's needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficultyto keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase ordecrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimizethe player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.

Player metrics are an invaluable resource for game designers and QA analysts who wish to understand players, monitor and improve game play, and test design hypotheses. Usually such metrics are collected in a straightforward manner by passively recording players; however, such an approach has several potential drawbacks. First, passive recording might fail to record metrics which correspond to an infrequent player behavior. Secondly, passive recording can be a costly, laborious, and memory intensive process, even with the aid of tools. In this paper, we explore the potential for an active approach to player metric collection which strives to collect data more efficiently, and thus with less cost. We use an online, iterative approach which models the relationship between player metrics and in-game situations probabilistically using a Markov Decision Process (MDP) and solves it for the best game configurations to run. To analyze the benefits and limitations of this approach, we implemented a system, called GAMELAB, for recording player metrics in Second Life.

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.

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 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."

In this paper, we survey techniques used in intelligent tutoring systems (ITSs) to model student knowledge. The three techniques that we review in detail are knowledge tracing, performance factor analysis, and matrix factorization. We also briefly cover other techniques that have been used. This review is meant to be a repository of knowledge for those who want to integrate these techniques into serious games. It is also meant to increase awareness and interest as to the techniques available that can be integrated into serious games.

The net result of this Because these approaches are not grounded in the core approach should be to either provide a viable alternative to processes that drive human action, the resultant outputs - classical artificial intelligence / machine learning (AI, ML) predictions of behavior, estimates of errors and the like - approaches or. Alternatively, to provide a more do not provide a robust basis for representing human users neurocognitively - inspired approach to developing these to the systems with which they are interacting.