Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.

Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set. Thus DDFA features are inherently discriminative from the start even though they are trained without knowledge of the ultimate classification problem. Interestingly, DDFA also continues to add new features indefinitely (so it does not depend on a hidden layer size), is not based on minimizing error, and is inherently divergent instead of convergent, thereby providing a unique direction of research for unsupervised feature learning. In this paper the quality of its learned features is demonstrated on the MNIST dataset, where its performance confirms that indeed DDFA is a viable technique for learning useful features.

In contrast to the conventional role of evolution in evolutionary computation (EC) as an optimization algorithm, a new class of evolutionary algorithms has emerged in recent years that instead aim to accumulate as diverse a collection of discoveries as possible, yet where each variant in the collection is as fit as it can be. Often applied in both neuroevolution and morphological evolution, these new quality diversity (QD) algorithms are particularly well-suited to evolution's inherent strengths, thereby offering a promising niche for EC within the broader field of machine learning. However, because QD algorithms are so new, until now no comprehensive study has yet attempted to systematically elucidate their relative strengths and weaknesses under different conditions. Taking a first step in this direction, this paper introduces a new benchmark domain designed specifically to compare and contrast QD algorithms. It then shows how the degree of alignment between the measure of quality and the behavior characterization (which is an essential component of all QD algorithms to date) impacts the ultimate performance of different such algorithms. The hope is that this initial study will help to stimulate interest in QD and begin to unify the disparate ideas in the area.

This article presents a lightweight platform for evolving two-dimensional artificial creatures. The aim of providing such a platform is to reduce the barrier to entry for researchers interested in evolving creatures for artificial life experiments. In effect the novel platform, which is inspired by the Sodarace construction set, makes it easy to set up creative scenarios that test the abilities of Sodarace-like creatures made of masses and springs. In this way it allows the researcher to focus on evolutionary algorithms and dynamics. The new indirectly encoded Sodarace (IESoR) system introduced in this article extends the original Sodarace by enabling the evolution of significantly more complex and regular creature morphologies. These morphologies are themselves encoded by compositional pattern-producing networks (CPPNs), an indirect encoding previously shown effective at encoding regularities and symmetries in structure. The capability of this lightweight system to facilitate research in artificial life is then demonstrated through both walking and jumping domains, in which IESoR discovers a wide breadth of strategies through novelty search with local competition.

Natural evolution inspires the fields of evolutionary computation (EC) and artificial life (ALife). A prominent feature of natural evolution is that it effectively never ends. However, most EC and ALife experiments are only run for several days or weeks at a time. Once an experiment concludes, reproducing, observing, or extending the results often requires considerable effort. In contrast, some Collaborative Interactive Evolution (CIE) systems, e.g. Picbreeder, were designed to preserve results as potential stepping stones to build upon later while taking advantage of human insight to solve challenging problems. Traditionally, building long-running and open experiments similar to Picbreeder presents a complex and timeconsuming software challenge. To reduce this challenge and thereby remove the barrier to situating almost any experiment within an interactive online framework, this paper presents the initial prototype for Worldwide Infrastructure for Neuroevolution (WIN). Built in the model of Picbreeder, WIN is a modular library for significantly reducing the complexity of creating fully persistent, online, and interactive evolutionary platforms for any new or existing domain. WIN Online, the public interface for WIN, provides an online collection of domains built with WIN that lets novice and expert users browse and meaningfully contribute to ongoing experiments. Two example experiments in this paper demonstrate WIN’s potential to quickly bootstrap any evolutionary domain with online and interactive capabilities.

While the real-time focus of today's automated accompaniment generators can benefit instrumentalists and vocalists in their practice, improvisation, or performance, an opportunity remains specifically to assist novice composers. This paper introduces a novel such approach based on evolutionary computation called functional scaffolding for musical composition (FSMC), which helps the user explore potential accompaniments for existing musical pieces, or scaffolds. The key idea is to produce accompaniment as a function of the scaffold, thereby inheriting from its inherent style and texture. To implement this idea, accompaniments are represented by a special type of neural network called a compositional pattern producing network (CPPN), which produces harmonies by elaborating on and exploiting regularities in pitches and rhythms found in the scaffold. This paper focuses on how inexperienced composers can personalize accompaniments by first choosing any MIDI scaffold, then selecting which parts (e.g. the piano, guitar, or bass guitar) the CPPN can hear, and finally customizing and refining the computer-generated accompaniment through an interactive process of selection and mutation of CPPNs called interactive evolutionary computation (IEC). The potential of this approach is demonstrated by following the evolution of a specific accompaniment and studying whether listeners appreciate the results.