Challenge in Digital Games: Towards Developing a Measurement Tool

Challenge is arguably the most important experience that players seek in digital games. However, without a measure of how challenged players feel during the act of play, it is hard to design games that are neither too easy nor too hard and, therefore, truly enjoyable. Especially in industry, challenge is dominantly assessed by means of manual play testing in ad-hoc trials. The aim of this research is to create a more systematic, complete, and reliable instrument to evaluate the level of players' experienced challenge in games in the form of a questionnaire. This paper presents the key results from an extensive literature survey which will inform further development. We survey definitions of challenge, challenge types, and their relation to player experience based on the observations of game designers. We furthermore draw from empirical findings in a diverse range of fields such as game studies, human-computer interaction (HCI) and artificial intelligence (AI)

Accelerating Empowerment Computation with UCT Tree Search

Models of intrinsic motivation present an important means to produce sensible behaviour in the absence of extrinsic rewards. Applications in video games are varied, and range from intrinsically motivated general game-playing agents to non-player characters such as companions and enemies. The information-theoretic quantity of Empowerment is a particularly promising candidate motivation to produce believable, generic and robust behaviour. However, while it can be used in the absence of external reward functions that would need to be crafted and learned, empowerment is computationally expensive. In this paper, we propose a modified UCT tree search method to mitigate empowerment's computational complexity in discrete and deterministic scenarios. We demonstrate how to modify a Monte-Carlo Search Tree with UCT to realise empowerment maximisation, and discuss three additional modifications that facilitate better sampling. We evaluate the approach both quantitatively, by analysing how close our approach gets to the baseline of exhaustive empowerment computation with varying amounts of computational resources, and qualitatively, by analysing the resulting behaviour in a Minecraft-like scenario

Measuring perceived challenge in digital games: Development & validation of the challenge originating from recent gameplay interaction scale (CORGIS)

Challenge is a key element of digital games, but a clear conceptualisation and operationalisation of this player experience were long missing. This made it hard for game researchers to measure this experience in different video games across different skill sets and impeded the synthesis of challenge-related games research. To overcome this, we introduce a systematic, extensive, and reliable instrument to evaluate the level of players’ perceived challenge in digital games. We conceptualise challenge based on a survey of related literature in games user research, design and AI, as well as interviews with researchers and players. Exploratory factor analysis (N=394) highlights four components of experienced challenge: performative, emotional, cognitive and decision-making challenge. Refinement of the items allowed us to devise the Challenge Originating from Recent Gameplay Interaction Scale (CORGIS), which has been further validated in a study with nearly 1000 players. The questionnaire exhibits good construct validity for use by both game developers and researchers to quantify players’ challenge experiences

Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop

Active inference is an ambitious theory that treats perception, inference, and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g., different environments or agent morphologies. In the literature, paradigms that share this independence have been summarized under the notion of intrinsic motivations. In general and in contrast to active inference, these models of motivation come without a commitment to particular inference and action selection mechanisms. In this article, we study if the inference and action selection machinery of active inference can also be used by alternatives to the originally included intrinsic motivation. The perception-action loop explicitly relates inference and action selection to the environment and agent memory, and is consequently used as foundation for our analysis. We reconstruct the active inference approach, locate the original formulation within, and show how alternative intrinsic motivations can be used while keeping many of the original features intact. Furthermore, we illustrate the connection to universal reinforcement learning by means of our formalism. Active inference research may profit from comparisons of the dynamics induced by alternative intrinsic motivations. Research on intrinsic motivations may profit from an additional way to implement intrinsically motivated agents that also share the biological plausibility of active inference

Predicting Player Experience Without the Player. An Exploratory Study

A key challenge of procedural content generation (PCG) is to evoke a certain player experience (PX), when we have no direct control over the content which gives rise to that experience. We argue that neither the rigorous methods to assess PX in HCI, nor specialised methods in PCG are sufficient, because they rely on a human in the loop. We propose to address this shortcoming by means of computational models of intrinsic motivation and AI game-playing agents. We hypothesise that our approach could be used to automatically predict PX across games and content types without relying on a human player or designer. We conduct an exploratory study in level generation based on empowerment, a specific model of intrinsic motivation. Based on a thematic analysis, we find that empowerment can be used to create levels with qualitatively different PX. We relate the identified experiences to established theories of PX in HCI and game design, and discuss next steps