In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is hence important to consider more solutions that decision-makers can compare and further explore based on additional criteria. Alongside the existing approaches of evolutionary diversity optimization, quality diversity, and multimodal optimization, this paper presents a fresh perspective on this challenge by considering the problem of identifying a fixed number of solutions with a pairwise distance above a specified threshold while maximizing their average quality. We obtain first insight into these objectives by performing a subset selection on the search trajectories of different well-established search heuristics, whether specifically designed with diversity in mind or not. We emphasize that the main goal of our work is not to present a new algorithm but to look at the problem in a more fundamental and theoretically tractable way by asking the question: What trade-off exists between the minimum distance within batches of solutions and the average quality of their fitness? These insights also provide us with a way of making general claims concerning the properties of optimization problems that shall be useful in turn for benchmarking algorithms of the approaches enumerated above. A possibly surprising outcome of our empirical study is the observation that naive uniform random sampling establishes a very strong baseline for our problem, hardly ever outperformed by the search trajectories of the considered heuristics. We interpret these results as a motivation to develop algorithms tailored to produce diverse solutions of high average quality.

Generative Adversarial Networks (GANs) are capable of generating convincing imitations of elements from a training set, but the distribution of elements in the training set affects to difficulty of properly training the GAN and the quality of the outputs it produces. This paper looks at six different GANs trained on different subsets of data from the game Lode Runner. The quality diversity algorithm MAP-Elites was used to explore the set of quality levels that could be produced by each GAN, where quality was defined as being beatable and having the longest solution path possible. Interestingly, a GAN trained on only 20 levels generated the largest set of diverse beatable levels while a GAN trained on 150 levels generated the smallest set of diverse beatable levels, thus challenging the notion that more is always better when training GANs.

This paper introduces a fully automatic method of mechanic illumination for general video game level generation. Using the Constrained MAP-Elites algorithm and the GVG-AI framework, this system generates the simplest tile based levels that contain specific sets of game mechanics and also satisfy playability constraints. We apply this method to illuminate mechanic space for $4$ different games in GVG-AI: Zelda, Solarfox, Plants, and RealPortals.

In today's world Artificial Intelligence (AI) and Machine Learning (ML) are buzz words in every industry. Come meet experts in this technology to demystify and understand AI and ML applications in healthcare. We will hear from several industry and community healthcare leaders about how they harnessed this technology to improve patient's lives. While we have made incredible strides in bringing data technology and AI into healthcare, we know that all of our work is for nothing unless patients can access the care they need. In honor of the patients we are helping everyday with our custom data solutions, we are asking for your help in supporting the American Cancer Society's Transportation and Lodging Program to ensure their patients can get to and from the treatment they need.

This essay is one of the five winners in the 2019 writing competition held by the Black Hole Initiative at Harvard University. "The Black Hole Initiative offers a unique environment for thinking about the topic of black holes more creatively and comprehensively," says BHI director, Avi Loeb. To add context to the exciting April 10 announcement that astronomers have observed a black hole for the first time, this week Nautilus is featuring all five winning essays. The way up and the way down are one and the same," says the Sage. Paradox has a way of plaguing thought.

Fittingly, the Black Hole Initiative (BHI) was founded 100 years after Karl Schwarzschild solved Einstein's equations for general relativity--a solution that described a black hole decades before the first astronomical evidence that they exist. As exotic structures of spacetime, black holes continue to fascinate astronomers, physicists, mathematicians, philosophers, and the general public, following on a century of research into their mysterious nature. The mission of the BHI is interdisciplinary and, to that end, we sponsor many events that create the environment to support interaction between researchers of different disciplines. Philosophers speak with mathematicians, physicists, and astronomers, theorists speak with observers and a series of scheduled events create the venue for people to regularly come together. As an example, for a problem we care about, consider the singularities at the centers of black holes, which mark the breakdown of Einstein's theory of gravity.