However, Prior research has investigated the benefits and costs of even when reviewers self-report as having the highest level double-anonymous review (DAR, also known as double-blind of expertise in their field, their guess accuracy is no better review) in comparison to single-anonymous review (SAR) and than those who are self-reported as less knowledgeable [17]. Several review papers have attempted to Increased editor burden in handling conflict of interest, author compile experimental results in peer review research both burden in anonymizing the manuscript, and reviewer burden broadly and in engineering and computer science specifically in navigating prior work by others and by the authors are also [1-4]. This document summarizes prior research in peer review cited as costs to DAR. that may inform decisions about the format of peer review in Despite these challenges, numerous robotics conferences the field of robotics and makes some recommendations for have already made the shift to DAR, including RSS and a potential next steps for robotics publications. Furthermore, top machine learning conferences such as NeurIPS and CoRL have II. The presence of gender bias and effect of DAR on such bias is a common concern in research into peer review but Based on the current literature, we find that the evidence the conclusions are varied. Many studies do conclude that in support of double-anonymous review is not sufficient to gender can disadvantage authors, particularly women [5, 6] conclusively recommend for implementation in robotics conferences and that DAR can reduce this bias [7].

The Bureau of Multiversal Arbitration is an unusual workplace. Maude Fletcher's alright, though she needs to learn how to turn off caps lock in the company chat. But trying to deal with Byron G Snodgrass is like handling an energetic poodle, and Phil is a bit stiff. Byron G Snodgrass is an energetic poodle. A peace lily, I think.

The Bureau of Multiversal Arbitration is an unusual workplace. Maude Fletcher's alright, though she needs to learn how to turn off caps lock in the company chat. But trying to deal with Byron G Snodgrass is like handling an energetic poodle, and Phil is a bit stiff. Byron G Snodgrass is an energetic poodle. A peace lily, I think.

Melinda Snodgrass is the novelist and screenwriter best known for her classic Star Trek: The Next Generation script "The Measure of a Man." Her latest novel, Lucifer's War, pits an unlikely band of heroes against a horde of Lovecraftian monsters that have been spreading fear and ignorance throughout human history. "It's unbelievable now, the kind of nonsense people are accepting, that's being pushed on them by social media," Snodgrass says in Episode 529 of the Geek's Guide to the Galaxy podcast. "I really wanted to make a stand for science and rationality, as opposed to magic and superstition." The book is set in Snodgrass' home state of New Mexico, a place where science and superstition clash in a particularly striking way. "It's a very weird place, where you have Los Alamos laboratory, Sandia laboratories, high-tech, high-energy centers," Snodgrass says, "Some of the finest scientific minds in the world come here to lecture and study and commune with each other, and then on the other side you have people who will balance your aura and sell you a crystal to deal with your cancer."

We present tile2tile, an approach for style transfer between levels of tile-based platformer games. Our method involves training models that translate levels from a lower-resolution sketch representation based on tile affordances to the original tile representation for a given game. This enables these models, which we refer to as filters, to translate level sketches into the style of a specific game. Moreover, by converting a level of one game into sketch form and then translating the resulting sketch into the tiles of another game, we obtain a method of style transfer between two games. We use Markov random fields and autoencoders for learning the game filters and apply them to demonstrate style transfer between levels of Super Mario Bros, Kid Icarus, Mega Man and Metroid.

In this paper we outline a method of procedurally generating maps using Markov Chains. Our method attempts to learn what makes a "good" map from a set of given human-authored maps, and then uses those learned patterns to generate new maps. We present an empirical evaluation using the game "Super Mario Bros.," showing encouraging results.

Procedural content generation (PCG) has become a popular research topic in recent years, but not much work has been done in terms of generalized content generators,that is, methods that can generate content for a wide variety of games without requiring hand-tuning. Probabilistic approaches are a promising avenue for creating more general content generators, and specificially map generators. I am interested in exploring probabilistic techniques that could lead to generalized procedural level generators.

Statistical models, such as Markov Chains, have been recently studied in the context of procedural content generation (PCG). These models can capture statistical regularities of a set of training data and use them to sample new content. However, these techniques assume the existence of sufficient training data with which to train the models. In this paper we study the setting in which we might not have enough training data from the target domain, but we have ample training data from another, similar domain. We propose an algorithm to discover a mapping between domains, so that out-of-domain training data can be used to train the statistical model. Specifically, we apply this to two-dimensional level generation, and experiment with three classic video games: Super Mario Bros., Kid Icarus and Kid Kool.

Procedural content generation via machine learning (PCGML) has been growing in recent years. However, many PCGML approaches are only explored in the context of linear platforming games, and focused on modeling structural level information. Previously, we developed a multi-layer level representation, where each layer is designed to capture specific level information. In this paper, we apply our multi-layer approach to Lode Runner, a game with non-linear paths and complex actions. We test our approach by generating levels for Lode Runner with a constrained multi-dimensional Markov chain (MdMC) approach that ensures playability and a standard MdMC sampling approach. We compare the levels sampled when using multi-layer representation against those sampled using the single-layer representation; we compare using both the constrained sampling algorithm and the standard sampling algorithm.

The exploration of Procedural Content Generation via Machine Learning (PCGML) has been growing in recent years. However, while the number of PCGML techniques and methods for evaluating PCG techniques have been increasing, little work has been done in determining how the quality and quantity of the training data provided to these techniques effects the models or the output. Therefore, little is known about how much training data would actually be needed to deploy certain PCGML techniques in practice. In this paper we explore this question by studying the quality and diversity of the output of two well-known PCGML techniques (multi-dimensional Markov chains and Long Short-term Memory Recurrent Neural Networks) in generating Super Mario Bros. levels while varying the amount and quality of the training data.