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.

A bottleneck in interactive storytelling is the authorial burden of writing narrative units, and connecting them to the interactive narrative structure. To address this problem, we present a hybrid approach that combines AI planning and evolutionary optimization in order to generated new plan operators representing possible story actions, within the framework of a planning-based interactive narrative system. We focus our work on inventing plan operators that are useful for contributing to suspenseful interactive stories, using suspense metrics that have been proposed in the literature. We devise an encoding scheme for converting a plan operator into a genetic-algorithm chromosome and vice versa, respecting constraints that are needed for an operator to be well-formed. We discuss the performance of the system, and several examples from preliminary experiments carried out to evaluate the evolved operators.

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.

This paper discusses emerging creative practices that involve interacting with generative computational systems, and the effect of such cybernetic interactions on our conceptions of creativity and agency. As computing systems have become more powerful in recent years, real time interaction with intelligent computational processes and models has emerged as a basis for innovative creative practices. Examples of these practices include interactive digital media installations, generative art works, live coding performances, virtual theatre, interactive cinema, and adaptive processes in computer games. In these types of activities computational systems have assumed a significant level of agency, or autonomy, that provoke questions about shared authorship and originality that are redefining our relationship with technologies and prompting new questions about human capabilities, values and the meaning of productive activities.

Mezzo is a computer program designed that procedurally writes Romantic-Era style music in real-time to accompany computer games. Leitmotivs are associated with game characters and elements, and mapped into various musical forms.  These forms are distinguished by different amounts of harmonic tension and formal regularity, which lets them musically convey various states of markedness which correspond to states in the game story. Because the program is not currently attached to any game or game engine, “virtual” gameplays were been used to explore the capabilities of the program; that is, videos of various game traces were used as proxy examples.  For each game trace, Leitmotivs were input to be associated with characters and game elements, and a set of ‘cues’ was written, consisting of a set of time points at which a new set of game data would be passed to Mezzo to reflect the action of the game trace.  Examples of music composed for one such game trace, a scene from Red Dead Redemption , are given to illustrate the various ways the program maps Leitmotivs into different levels of musical markedness that correspond with the game state.

Manual image annotation is a tedious and time-consuming task, while automated methods are error prone and limited in their results. Human computation, and especially games with a purpose, have shown potential to create high quality annotations by "hiding the complexity" of the actual annotation task and employing the "wisdom of the crowds". In this demo paper we present two games with a single purpose: finding regions in images that correspond to given terms. We discuss approach, implementation, and preliminary results of our work and give an outlook to immediate future work.

The problem of optimal planning under uncertainty in collaborative multi-agent domains is known to be deeply intractable but still demands a solution. This thesis will explore principled approximation methods that yield tractable approaches to planning for AI assistants, which allow them to understand the intentions of humans and help them achieve their goals. AI assistants are ubiquitous in video games, mak- ing them attractive domains for applying these planning techniques. However, games are also challenging domains, typically having very large state spaces and long planning horizons. The approaches in this thesis will leverage recent advances in Monte-Carlo search, approximation of stochastic dynamics by deterministic dynamics, and hierarchical action representation, to handle domains that are too complex for existing state of the art planners. These planning techniques will be demonstrated across a range of video game domains.

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.

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.

In this paper, we present adaptive quests, an extension to the dominant quest model that guides and motivates gameplay in MMORPG shared worlds. The standard model has proven effective, but is significantly incompatible with the desire for player driven change in the world. We present an incremental step to increasing player impact, discuss the problems it creates with the quest model, and show how adaptive quests can help reconcile the two. We present simulation experiments supporting not only that adaptive quests help mitigate those problems, but that they can actually improve them over the standard model.