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.

Location-based games are an emerging paradigm fortraining, simulation, entertainment, health and many other domains. In this paper, we consider the role of artificialagents in such games. We also examine how human teams perform when given the same game, playedin both a real environment with mobile devices and alsoin a virtual environment that replicates the real environment.We perform the first direct comparison of real andvirtual instantiations of the same location-based game.We show the similarities and differences in game playand then investigate how adding an advice-giving agentchanges the experience.

Artificial Intelligence (AI) and Personalization are both essential - How do we relate content (the factual knowledge aspects of all games, be they serious or entertainment contained, game mechanics) and context (experiences based. In this research the role of AI and Personalization is and activities) to pedagogical goals towards supporting however focused upon the context of Serious Games (SG) in pedagogically-driven design and development of SGs? particular. A concerted research direction is necessary in this From these two high-level questions we derived a more area so as to establish future benchmarks and metrics for the pragmatic approach to AI and Personalization based on: In effective use of AI and Personalization in serious games design what ways can personalization improve learning and adapt and will benefit relevant research communities in providing best to learner requirements?

The prevalence of virtual worlds presents an interesting The research questions we are looking to solve are: challenge for intelligent mobile agents. Online, very largescale, - How to represent maps of large scale, complex environments persistent virtual worlds such as Second Life (Linden Research Inc. 2012) and massively multi-player online games (MMOs) are becoming more popular. As these - How an agent can generate, update and use these maps worlds grow in size there is a challenge in providing intelligent - How can we utilise user-generated information to build agents that can generate and use maps of these environments and improve upon these maps without the need for hard-coding or pre-processing the map.

My research is focused on using human navigation data ingames and simulation to learn motion models from trajectorydata. These motion models can be used to: 1) track the opponent’smovement during periods of network occlusion; 2)learn combat tactics by demonstration; 3) guide the planningprocess when the goal is to intercept the opponent. A trainingset of example motion trajectories is used to learn twotypes of parameterized models: 1) a second order dynamicalsteering model or 2) the reward vector for a Markov DecisionProcess. Candidate paths from the model serve as themotion model in a set of particle filters for predicting the opponent’slocation at different time horizons. Incorporating theproposed motion models into game bots allows them to customizestheir tactics for specific human players and functionas more capable teammates and adversaries.

We address the problem of determining the structure of a set of plot points for an interactive narrative. To do so, we define a formal two-player game where a computer can play with an author to learn the structural representation of the story. This technique will allow for authors unfamiliar, or uncomfortable, with mathematical structures to create the inputs interactive narrative algorithms require. We include the underlying mathematical theory as a foundation of our approach, and characterize it's effectiveness through a series of simulation experiments. Results indicate there is promise in using formal games to aid in authoring interactive narrative structures.

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'.

In this paper, methods and models for the design of educational interventions and usable systems are presented and synthesized. The purpose is to suplliment the design process with educational considerations and discern design principles for the development of serious STEM games. This synthesis can contribute to the design of the next generation of technologically enhanced learning environments.

However, due to the small numbers of goals present in existing systems, goal management Autonomous AI systems should be aware of their own goals is a relatively simple affair. Hanheide et al. (2010) describe and be capable of independently formulating behaviour to a system similar in architecture to our own that manages address them. We would ideally like to provide an agent with just two goals, whereas the one discussed in this paper must a collection of competences that allow it to act in novel situations manage upwards of forty. As the number of goals increases, that may not be predictable at design-time. In particular, the potential for goal conflict grows. This leads to a requirement we are interested in the operation of AI systems in for more sophisticated management processes, such as complex, oversubscribed domains where there may exist a dynamic goal re-prioritisation, allowing agents to alter their variety of ways to address high-level goals by composing behaviour to meet changing operational requirements. In the behaviours to achieve a set of sub-goals taken from a larger oversubscribed problem domains we are interested in, encoding set. Our research focusses how such sub-goals might be chosen all possible operating strategies at design time may (i.e.

A common approach to interactive narrative involves imbuing the computer with all of the potential story pre-authored story experiences (e.g. as beats, plot points, planning operators, etc.). This has resulted in an accepted paradigm where stories are not created by or with the user; rather, the user is given piecemeal access to the story from the gatekeeper of story knowledge: the computer (e.g. as an AI drama manager). This article describes a formal process that provides for the equal co-creation of story-rich experiences, where neither the user nor computer is in a privileged position in an interactive narrative. It describes a new formal approach that acts as a first step for the real-time co-creation of narrative in games that rely on the negotiated shared mental model between a human actor and an AI improv agent.