Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods based on mutation and recombination of programs, or via neural language models. We propose a novel method that combines both approaches using a concept of a virtual neuro-genetic programmer: using evolutionary methods as an alternative to gradient descent for neural network training}, or scrum team. We demonstrate its ability to provide performant and explainable solutions for various OpenAI Gym tasks, as well as inject expert knowledge into the otherwise data-driven search for solutions.

Collaborative writing is essential for teams that create documents together. Creating documents in large-scale collaborations is a challenging task that requires an efficient workflow. The design of such a workflow has received comparatively little attention. Conventional solutions such as working on a single Microsoft Word document or a shared online document are still widely used. In this paper, we propose a new workflow consisting of a combination of the lightweight markup language AsciiDoc together with the state-of-the-art version control system Git. The proposed process makes use of well-established workflows in the field of software development that have grown over decades. We present a detailed comparison of the proposed markup + Git workflow to Word and Word for the Web as the most prominent examples for conventional approaches.We argue that the proposed approach provides significant benefits regarding scalability, flexibility, and structuring of most collaborative writing tasks, both in academia and industry.

Most state of the art decision systems based on Reinforcement Learning (RL) are data-driven black-box neural models, where it is often difficult to incorporate expert knowledge into the models or let experts review and validate the learned decision mechanisms. Knowledge-insertion and model review are important requirements in many applications involving human health and safety. One way to bridge the gap between data and knowledge driven systems is program synthesis: replacing a neural network that outputs decisions with a symbolic program generated by a neural network or by means of genetic programming. We propose a new programming language, BF++, designed specifically for automatic programming of agents in a Partially Observable Markov Decision Process (POMDP) setting and apply neural program synthesis to solve standard OpenAI Gym benchmarks.