Player-AI Interaction: What Neural Network Games Reveal About AI as Play

The advent of artificial intelligence (AI) and machine learning (ML) bring human-AI interaction to the forefront of HCI research. This paper argues that games are an ideal domain for studying and experimenting with how humans interact with AI. Through a systematic survey of neural network games (n = 38), we identified the dominant interaction metaphors and AI interaction patterns in these games. In addition, we applied existing human-AI interaction guidelines to further shed light on player-AI interaction in the context of AI-infused systems. Our core finding is that AI as play can expand current notions of human-AI interaction, which are predominantly productivity-based. In particular, our work suggests that game and UX designers should consider flow to structure the learning curve of human-AI interaction, incorporate discovery-based learning to play around with the AI and observe the consequences, and offer users an invitation to play to explore new forms of human-AI interaction

Interactive Evolution and Exploration within Latent Level-Design Space of Generative Adversarial Networks

Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such Latent Variable Evolution (LVE) has recently been applied to game levels. However, it is hard for objective scores to capture level features that are appealing to players. Therefore, this paper introduces a tool for interactive LVE of tile-based levels for games. The tool also allows for direct exploration of the latent dimensions, and allows users to play discovered levels. The tool works for a variety of GAN models trained for both Super Mario Bros. and The Legend of Zelda, and is easily generalizable to other games. A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels. User feedback also indicates how this system could eventually grow into a commercial design tool, with the addition of a few enhancements.Comment: GECCO 202

iNNk: A Multi-Player Game to Deceive a Neural Network

This paper presents iNNK, a multiplayer drawing game where human players team up against an NN. The players need to successfully communicate a secret code word to each other through drawings, without being deciphered by the NN. With this game, we aim to foster a playful environment where players can, in a small way, go from passive consumers of NN applications to creative thinkers and critical challengers

Möglichkeitsdenken. Utopie und Dystopie in der Gegenwart

Utopien denken Möglichkeiten von Zukunft. Mit Beginn der historischen Moderne, in der die Erwartung an die Zukunft die Erfahrung der Vergangenheit übersteigt, entstehen in der je aktuellen Gegenwart Entwürfe, die Utopien genannt werden können. Die Temporalisierung der Erfahrung macht Projektionen in die Zukunft möglich (Reinhart Koselleck). Diese sind nie eindeutig. Sie liefern mehrdeutige Wunsch- und Schreckbilder auch in eigentümlichen Verschränkungen. Die Einsicht in diese Dialektik nimmt mit dem Grad der Selbstreferentialität von Zukunftsentwürfen zu; Utopie und Dystopie bedingen sich wechselseitig. – Gegenwärtig leben wir mit außerordentlich unsicheren Zukunftsperspektiven. Haben Utopien nur in Dystopien überlebt? Nach dem Ende des Utopismus-Verdachts am Beginn der 90er Jahre geht es heute um eine Bestandsaufnahme von Zukunftspotentialen, um Diskussionen von Denkformen des Hypothetisch-Möglichen. Bietet die Tradition des utopischen Denkens Anknüpfungspunkte für aktuelle, positiv oder negativ konnotierte Zukunftsbeschreibungen? Wunsch- oder Warnbilder sind noch immer jenem utopischen Impuls verpflichtet, der den Blick aus der Gegenwart in die Zukunft richten will. Die Frage nach der Zukunft utopischen Denkens stellt somit in den Möglichkeiten temporalen, visionären und konjunktivischen Denkens zugleich die Frage nach dem Ort des Gesellschaftlichen und der Gesellschaft heute – und damit die Frage nach der Verbindlichkeit von Tradition, und das heißt auch: nach Traditionen des Utopischen

Approximating Context-Sensitive Program Information

Static program analysis is in general more precise if it is sensitive to execution contexts (execution paths). In this paper we propose χ-terms as a mean to capture and manipulate context-sensitive program information in a data-flow analysis. We introduce finite k-approximation and loop approximation that limit the size of the context-sensitive information. These approximated χ-terms form a lattice with a finite depth, thus guaranteeing every data-flow analysis to reach a fixed point.

A Framework for Memory Efficient Context-Sensitive Program Analysis

Static program analysis is in general more precise if it is sensitive to execution contexts (execution paths). But then it is also more expensive in terms of memory consumption. For languages with conditions and iterations, the number of contexts grows exponentially with the program size. This problem is not just a theoretical issue. Several papers evaluating inter-procedural context-sensitive data-flow analysis report severe memory problems, and the path-explosion problem is a major issue in program verification and model checking. In this paper we propose χ-terms as a means to capture and manipulate context-sensitive program information in a data-flow analysis. χ-terms are implemented as directed acyclic graphs without any redundant subgraphs. We introduce the k-approximation and the l-loop-approximation that limit the size of the context-sensitive information at the cost of analysis precision. We prove that every context-insensitive data-flow analysis has a corresponding k, l-approximated context-sensitive analysis, and that these analyses are sound and guaranteed to reach a fixed point. We also present detailed algorithms outlining a compact, redundancy-free, and DAG-based implementation of χ-terms