Research in neuroevolution-that is, evolving artificial neural networks (ANNs) through evolutionary algorithms-is inspired by the evolution of biological brains, which can contain trillions of connections. Yet while neuroevolution has produced successful results, the scale of natural brains remains far beyond reach. This article presents a method called hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) that aims to narrow this gap. HyperNEAT employs an indirect encoding called connective compositional pattern-producing networks (CPPNs) that can produce connectivity patterns with symmetries and repeating motifs by interpreting spatial patterns generated within a hypercube as connectivity patterns in a lower-dimensional space. This approach can exploit the geometry of the task by mapping its regularities onto the topology of the network, thereby shifting problem difficulty away from dimensionality to the underlying problem structure. Furthermore, connective CPPNs can represent the same connectivity pattern at any resolution, allowing ANNs to scale to new numbers of inputs and outputs without further evolution. HyperNEAT is demonstrated through visual discrimination and food-gathering tasks, including successful visual discrimination networks containing over eight million connections. The main conclusion is that the ability to explore the space of regular connectivity patterns opens up a new class of complex high-dimensional tasks to neuroevolution.

For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others' images. Through this process of branching from other images, and through continually increasing image complexity made possible by the underlying neuroevolution of augmenting topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC system. This paper discusses not only the strengths of the Picbreeder approach, but its challenges and shortcomings as well, in the hope that lessons learned will inform the design of future CIE systems.

The impact of game content on the player experience is potentially more critical in casual games than in competitive games because of the diminished role of strategic or tactical diversions. Interestingly, until now procedural content generation (PCG) has nevertheless been investigated almost exclusively in the context of competitive, skills-based gaming. This paper therefore opens a new direction for PCG by placing it at the center of an entirely casual flower-breeding game platform called Petalz. That way, the behavior of players and their reactions to different game mechanics in a casual environment driven by PCG can be investigated. In particular, players in Petalz can (1) trade their discoveries in a global marketplace, (2) respond to an incentive system that awards diversity, and (3) generate real-world threedimensional replicas of their evolved flowers. With over 1,900 registered online users and 38,646 unique evolved flowers, Petalz showcases the potential for PCG to enable these kinds of casual game mechanics, thus paving the way for continued innovation with PCG in casual gaming.

The robustness of animal behavior is unmatched by current machines, which often falter when exposed to unforeseen conditions. While animals are notably reactive to changes in their environment, machines often follow finely-tuned yet inflexible plans. Thus instead of the traditional approach of training such machines over many different unpredictable scenarios in detailed simulations (which is the most intuitive approach to inducing robustness), this work proposes to train machines to be reactive to their environment. The idea is that robustness may result not from detailed internal models or finely-tuned control policies but from cautious exploratory behavior. Supporting this hypothesis, robots trained to navigate mazes with a reactive disposition prove more robust than those trained over many trials yet not rewarded for reactive behavior in both simulated tests and when embodied in real robots. The conclusion is that robustness may neither require an accurate model nor finely calibrated behavior.

Abstract-Multirobot domains are a challenge for learning algorithms because they require robots to learn to cooperate to achieve a common goal. The challenge only becomes greater when robots must perform heterogeneous tasks to reach that goal. Multiagent HyperNEAT is a neuroevolutionary method (i.e. a method that evolves neural networks) that has proven successful in several cooperative multiagent domains by exploiting the concept of policy geometry, which means the policies of team members are learned as a function of how they relate to each other based on canonical starting positions. This paper extends the multiagent HyperNEAT algorithm by introducing situational policy geometry, which allows each agent to encode multiple policies that can be switched depending on the agent's state. This concept is demonstrated both in simulation and in real Khepera III robots in a patrol and return task, where robots must cooperate to cover an area and return home when called. Robot teams that are trained with situational policy geometry are compared to teams that are not and shown to find solutions more consistently that are also able to transfer to the real world.