We make a first step towards terrain-adaptive PCGML in Minecraft by introducing an automated system to create “volume-to-volume” datasets suitable for machine learning by leveraging handwritten black-box Minecraft settlement generation algorithms. Using this system, we create ten terrain-adaptive Minecraft ML datasets - including ones based on the currently best-performing algorithm submitted to the Generative Design in Minecraft (GDMC) competition. Finally, we train and qualitatively evaluate various GAN-based volume-to-volume models on all ten of our datasets. Although we do not obtain good results in all cases, we demonstrate that terrain-adaptive PCGML in Minecraft is indeed feasible.

Game content has long been created using procedural generation. However, many of these systems are currently designed in an ad-hoc manner, and there is a lack of knowledge around the design criteria that lead to generators producing the most successful results. In this study, we conduct a qualitative examination of the comments left by judges for the 2018–2020Generative Design in Minecraftcompetition. Using abductive thematic analysis, we identify the core design criteria that contribute to a generator that creates “good” content – here defined as interesting or engaging. By performing this study, we have identified that the core design criteria that create and interesting settlement are usability of the settlement environment, the thematic coherence within the settlement, and an anchoring in real-world simulacra.

The mastery of skills, such as balancing an inverted pendulum, implies a very accurate control of movements to achieve the task goals. Traditional accounts of skilled action control that focus on either routinization or perceptual control make opposite predictions about the ways we achieve mastery. The notion of routinization emphasizes the decrease of the variance of our actions, whereas the notion of perceptual control emphasizes the decrease of the variance of the states we visit, but not of the actions we execute. Here, we studied how participants managed control tasks of varying levels of difficulty, which consisted of controlling inverted pendulums of different lengths. We used information-theoretic measures to compare the predictions of alternative accounts that focus on routinization and perceptual control, respectively. Our results indicate that the successful performance of the control task strongly correlates with the decrease of state variability and the increase of action variability. As postulated by perceptual control theory, the mastery of skilled pendulum control consists in achieving stable control of goals by flexible means.

The GDMC AI settlement generation challenge is a procedural content generation (PCG) competition about producing an algorithm that can create a settlement in the game Minecraft. In contrast to the majority of AI competitions, the GDMC entries are evaluated by human experts on several criteria such as adaptability, functionality, evocative narrative, and visual aesthetics – all of which represent challenges to state-of-the-art PCG systems. This paper contains a collection of written experiences with this competition, by participants, judges, organizers and advisors. We asked people to reflect both on the artifacts themselves, and on the competition in general. The aim of this paper is to offer a shareable and edited collection of experiences and qualitative feedback which have the potential to push forward PCG and computational creativity, but would be lost once the individual assessments are compressed to scalar ratings. We reflect upon organizational issues for AI competitions, and discuss the future of the GDMC competition.

Procedural content generation for games is a growing trend in both research and industry, even though there is no consensus on how good content looks, nor how to automatically evaluate it. A number of metrics have been developed in the past, usually focused on the artifact as a whole, and mostly lacking grounding in human experience. In this study, we develop a new set of automated metrics, motivated by ideas from architecture, namely isovists, which have a track record of capturing the human experience of space. These metrics can be computed for a specific game state, from the player’s perspective, and take into account their embodiment in the game world. We show how to apply those metrics to the 3d blockworld of Minecraft. We use a dataset of generated settlements from the GDMC Settlement Generation Challenge in Minecraft and establish several rank-based correlations between the isovist properties and the rating human judges gave those settlements. We also produce a range of heat maps that demonstrate the location-based applicability of the approach, which allows for the development of those metrics as measures for a game experience at a specific time and space.

The papers in this special section focus on user experiences of artificial intelligence in games.

There are a range of metrics that can be applied to the artifacts produced by procedural content generation, and several of them come with qualitative claims. In this paper, we adapt a range of existing PCG metrics to generated Minecraft settlements, develop a few new metrics inspired by PCG literature, and compare the resulting measurements to existing human evaluations. The aim is to analyze how those metrics capture human evaluation scores in different categories, how the metrics generalize to another game domain, and how metrics deal with more complex artifacts. We provide an exploratory look at a variety of metrics and provide an information gain and several correlation analyses. We found some relationships between human scores and metrics counting specific elements, measuring the diversity of blocks and measuring the presence of crafting materials for the present complex blocks.

This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the abilities of different game-playing agents. This approach can be used to make agent testing more efficient. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of the effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways.

Live Action Role-Playing (LARP) games and similar experiences are becoming a popular game genre. Here, we discuss how artificial intelligence techniques, particularly those commonly used in AI for Games, could be applied to LARP. We discuss the specific properties of LARP that make it a surprisingly suitable application field, and provide a brief overview of some existing approaches. We then outline several directions where utilizing AI seems beneficial, by both making LARPs easier to organize, and by enhancing the player experience with elements not possible without AI.

A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans [1]. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions.

At the 2017 Artificial and Computational Intelligence in Games meeting at Dagstuhl, Julian Togelius asked how to make spaces where every way of filling in the details yielded a good game. This study examines the possibility of enriching search spaces so that they contain very high rates of interesting objects, specifically game elements. While we do not answer the full challenge of finding good games throughout the space, this study highlights a number of potential avenues. These include naturally rich spaces, a simple technique for modifying a representation to search only rich parts of a larger search space, and representations that are highly expressive and so exhibit highly restricted and consequently enriched search spaces. We treat the creation of plausible road systems, useful graphics, highly expressive room placement for maps, generation of cavern-like maps, and combinatorial puzzle spaces.

Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the do what’s possible representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.

From the beginning of the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. This prompted a shift in research focus towards electronic games, which provide unique new challenges. As is often the case with AI research, these results are liable to be exaggerated or mis-represented by either authors or third parties. The extent to which these game benchmarks constitute "fair" competition between human and AI is also a matter of debate. In this paper, we review statements made by reseachers and third parties in the general media and academic publications about these game benchmark results. We analyze what a fair competition would look like and suggest a taxonomy of dimensions to frame the debate of fairness in game contests between humans and machines. Eventually, we argue that there is no completely fair way to compare human and AI performance on a game.

This paper presents a method for generating floor plans for structures in Minecraft (Mojang 2009). Given a 3D space, it will auto-generate a building to fill that space using a combination of constrained growth and cellular automata. The result is a series of organic-looking buildings complete with rooms, windows, and doors connecting them. The method is applied to the Generative Design in Minecraft (GDMC) competition [24] to auto-generate buildings in Minecraft, and the results are discussed.

Being able to measure time, whether directly or indirectly, is a significant advantage for an organism. It allows for timely reaction to regular or predicted events, reducing the pressure for fast processing of sensory input. Thus, clocks are ubiquitous in biology. In the present article, we consider minimal abstract pure clocks in different configurations and investigate their characteristic dynamics. We are especially interested in optimally time-resolving clocks. Among these, we find fundamentally diametral clock characteristics, such as oscillatory behavior for purely local time measurement or decay-based clocks measuring time periods on a scale global to the problem. We include also sets of independent clocks ( clock bags), sequential cascades of clocks, and composite clocks with controlled dependence. Clock cascades show a condensation effect, and the composite clock shows various regimes of markedly different dynamics.

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map---ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.

We introduce several deck of cards and dice models that can be used to represent stochastic outcomes in tabletop games. We analyze these using a toy game introduced as a Micro Combat game. By simulating the outcome of the game with these different models we can analyze them in terms of their salience, disparity, fairness and obfuscation. We expect this analysis to help designers choose the method that best suits their intended experience.

Christian Guckelsberger and Christoph Salge, 'Does Empowerment Maximisation Allow for Enactive Artificial Agents?' in Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (Alife 2016), Cancun, Mexico, 4-8 July 2016. Carlos Gershenson, Tom Froese, Jesus M. Siqueiros, Wendy Aguilar, Eduardo J. Izquierdo and Hiroki Sayama eds., ISBN 9780262339360. Published by MIT Press.

The number of mechanical degrees of freedom (DoFs) within rehabilitation robots directly influences the scope of the movements that a subject can perform when training walking. Currently, gait rehabilitation robots have a limited number of mechanical DoFs, as a consequence this limits the movements these robots can make possible. In this paper, the novel gait rehabilitation system CORBYS is presented which consists of the mobile platform and a powered orthosis which is attached to the platform. The CORBYS powered orthosis has 16 DoFs enabling more physiological movements, making it a state-of-the-art gait rehabilitation robotic system. With the sufficient number of DoFs to enable natural-like walking, the CORBYS robotic system enables the integration of the “learning gait by therapist demonstration” paradigm. This paper presents the fully integrated functional CORBYS gait rehabilitation system, with the focus on the implementation aspects which enable generation of the reference gait trajectory through learning by therapist demonstration, and the use of the generated trajectory in the robotic therapy session. The results of the initial evaluation of the robotic system obtained in tests with a selected patient are given in the paper.

We are interested in designing artificial universes for artificial agents. We view artificial agents as networks of highlevel processes on top of of a low-level detailed-description system. We require that the high-level processes have some intrinsic explanatory power and we introduce an extension of informational closure namely interaction closure to capture this. Then we derive a method to design artificial universes in the form of finite Markov chains which exhibit high-level processes that satisfy the property of interaction closure. We also investigate control or information transfer which we see as an building block for networks representing artificial agents

Integrating information gained by observing others via Social Bayesian Learning can be beneficial for an agent’s performance, but can also enable population wide information cascades that perpetuate false beliefs through the agent population. We show how agents can influence the observation network by changing their probability of observing others, and demonstrate the existence of a population-wide equilibrium, where the advantages and disadvantages of the Social Bayesian update are balanced. We also use the formalism of relevant information to illustrate how negative information cascades are characterized by processing increasing amounts of non-relevant information

The objective of the CORBYS project is to design and implement a robot control architecture that allows the integration of high-level cognitive control modules, such as a semantically-driven self-awareness module and a cognitive framework for anticipation of, and synergy with, human behaviour based on biologically-inspired information-theoretic principles. CORBYS aims to provide a generic control architecture to benefit a wide range of applications where robots work in synergy with humans, ranging from mobile robots such as robotic followers to gait rehabilitation robots. The behaviour of the two demonstrators, used for validating this architecture, will each be driven by a combination of task specific algorithms and generic cognitive algorithms. In this paper we focus on the generic algorithms based on information theory.