Interfacing the Network: An Embedded Approach to Network Instrument Creation

This paper discusses the design, construction, and development of a multi-site collaborative instrument, The Loop, developed by the JacksOn4 collective during 2009-10 and formally presented in Oslo at the arts.on.wires and NIME conferences in 2011. The development of this instrument is primarily a reaction to historical network performance that either attempts to present traditional acoustic practice in a distributed format or utilises the network as a conduit to shuttle acoustic and performance data amongst participant nodes. In both scenarios the network is an integral and indispensible part of the performance, however, the network is not perceived as an instrument, per se. The Loop is an attempt to create a single, distributed hybrid instrument retaining traditionally acoustic interfaces and resonant bodies that are mediated by the network. The embedding of the network into the body of the instrument raises many practical and theoretical discussions, which are explored in this paper through a reflection upon the notion of the distributed instrument and the way in which its design impacts the behaviour of the participants (performers and audiences); the mediation of musical expression across networks; the bi-directional relationship between instrument and design; as well as how the instrument assists in the realisation of the creators’ compositional and artistic goals

Mapping Instructions and Visual Observations to Actions with Reinforcement Learning

We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent's exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants.Comment: In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 201

Cyclical Flow: Spatial Synthesis Sound Toy as Multichannel Composition Tool

This paper outlines and discusses an interactive system designed as a playful ‘sound toy’ for spatial composition. Proposed models of composition and design in this context are discussed. The design, functionality and application of the software system is then outlined and summarised. The paper concludes with observations from use, and discussion of future developments

Designing constraints: composing and performing with digital musical systems

This paper investigates two central terms in Human Computer Interaction (HCI) – affordances and constraints – and studies their relevance to the design and understanding of digital musical systems. It argues that in the analysis of complex systems, such as new interfaces for musical expression (NIME), constraints are a more productive analytical tool than the common HCI usage of affordances. Constraints are seen as limitations enabling the musician to encapsulate a specific search space of both physical and compositional gestures, proscribing complexity in favor of a relatively simple set of rules that engender creativity. By exploring the design of three different digital musical systems, the paper defines constraints as a core attribute of mapping, whether in instruments or compositional systems. The paper describes the aspiration for designing constraints as twofold: to save time, as musical performance is typically a real-time process, and to minimize the performer’s cognitive load. Finally, it discusses skill and virtuosity in the realm of new musical interfaces for musical expression with regard to constraints

How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions

How much is 131 million US dollars? To help readers put such numbers in context, we propose a new task of automatically generating short descriptions known as perspectives, e.g. "$131 million is about the cost to employ everyone in Texas over a lunch period". First, we collect a dataset of numeric mentions in news articles, where each mention is labeled with a set of rated perspectives. We then propose a system to generate these descriptions consisting of two steps: formula construction and description generation. In construction, we compose formulae from numeric facts in a knowledge base and rank the resulting formulas based on familiarity, numeric proximity and semantic compatibility. In generation, we convert a formula into natural language using a sequence-to-sequence recurrent neural network. Our system obtains a 15.2% F1 improvement over a non-compositional baseline at formula construction and a 12.5 BLEU point improvement over a baseline description generation

Projective simulation for artificial intelligence

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes retaine