Probabilistic movement modeling for intention inference in human-robot interaction.

Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.

Let's mix it up: interviews exploring the practical and technical challenges of interactive mixing in games

Game audio has come a long way since the simple electronic beeps of the early 1970s, when significant technical constraints governed the scope of creative possibilities. Recent years have witnessed technological advancements on an unprecedented scale; no sooner is one technology introduced than it is superseded by another, boasting a range of new refinements and enhanced performance

SimDialog: A visual game dialog editor

SimDialog is a visual editor for dialog in computer games. This paper presents the design of SimDialog, illustrating how script writers and non-programmers can easily create dialog for video games with complex branching structures and dynamic response characteristics. The system creates dialog as a directed graph. This allows for play using the dialog with a state-based cause and effect system that controls selection of non-player character responses and can provide a basic scoring mechanism for games

Playing with Identity. Authors, Narrators, Avatars, and Players in The Stanley Parable and The Beginner’s Guide

This article offers a comparative analysis of Davey Wreden’s The Stanley Parable (Wreden 2011 / Galactic Cafe 2013) and The Beginner’s Guide (Everything Unlimited Ltd. 2015) in order to explore the interrelation of authors, narrators, avatars, and players as four salient functions in the play with identity that videogames afford. Building on theories of collective and collaborative authorship, of narratives and narrators across media, and of the avatar-player relationship, the article reconstructs the similarities and differences between the way in which The Stanley Parable and The Beginner’s Guide position their players in relation to the two games’ avatars, narrators, and (main) author, while also underscoring how both The Stanley Parable and The Beginner’s Guide use metareferential strategies to undermine any overly rigid conceptualization of these functions and their interrelation

Design and Evaluation of a Probabilistic Music Projection Interface

We describe the design and evaluation of a probabilistic interface for music exploration and casual playlist generation. Predicted subjective features, such as mood and genre, inferred from low-level audio features create a 34- dimensional feature space. We use a nonlinear dimensionality reduction algorithm to create 2D music maps of tracks, and augment these with visualisations of probabilistic mappings of selected features and their uncertainty. We evaluated the system in a longitudinal trial in users’ homes over several weeks. Users said they had fun with the interface and liked the casual nature of the playlist generation. Users preferred to generate playlists from a local neighbourhood of the map, rather than from a trajectory, using neighbourhood selection more than three times more often than path selection. Probabilistic highlighting of subjective features led to more focused exploration in mouse activity logs, and 6 of 8 users said they preferred the probabilistic highlighting mode