Abstracts:An analysis of the language we use in scientific practice is critical to developing more rigorous and sound methodologies. This article argues that how certain methods of description are commonly employed in cognitive science risks obscuring important features of an agent’s cognition. We propose to make explicit a method of description whereby the concept of cognitive distinctions is the core principle. A model of referential communication is developed and analyzed as a platform to compare methods of description. We demonstrate that cognitive distinctions, realized in a graph theoretic formalism, better describe the behavior and perspective of a simple model agent than other, less systematic or natural language–dependent methods. We then consider how different descriptions relate to one another in the broader methodological framework of minimally cognitive behavior. Finally, we explore the consequences of, and challenges for, cognitive distinctions as a useful concept and method in the tool kit of cognitive scientists.

Abstracts:Robot controllers are often optimized for a single robot in a single environment. This approach proves brittle, as such a controller will often fail to produce sensible behavior for a new morphology or environment. In comparison, animal gaits are robust and versatile. By observing animals, and attempting to extract general principles of locomotion from their movement, we aim to design a single, decentralized controller applicable to diverse morphologies and environments. The controller implements the three components of (a) undulation, (b) peristalsis, and (c) leg motion, which we believe are the essential elements in most animal gaits. This work is a first step toward a general controller. Accordingly, the controller has been evaluated on a limited range of simulated centipede-like robot morphologies. The centipede is chosen as inspiration because it moves using both body contractions and legged locomotion. For a controller to work in qualitatively different settings, it must also be able to exhibit qualitatively different behaviors. We find that six different modes of locomotion emerge from our controller in response to environmental and morphological changes. We also find that different parts of the centipede model can exhibit different modes of locomotion, simultaneously, based on local morphological features. This controller can potentially aid in the design or evolution of robots, by quickly testing the potential of a morphology, or be used to get insights about underlying locomotion principles in the centipede.

Abstracts:This article presents Benefit Game 2.0, a multiscreen Artificial Life gameplay installation. Saccharina latissima, a seaweed species economically beneficial to humans but threatened by overexploitation, motivates the creation of this artwork. Technically, the authors create an underwater virtual ecosystem consisting of a seaweed swarm and symbiotic fungi, created using procedural content generation via machine learning and rule-based methods. Moreover, the work features a unique cybernetic loop structure, incorporating audience observation and game token interactions. This virtual system is also symbolically influenced in real time by indoor carbon dioxide measurements, serving as an artistic metaphor for the broader impacts of climate change. This integration with the physical game machine underscores the fragile relationship between human activities and the environment under severe global climate change and immerses the audience in the challenging balance between sustainability and profit seeking in this context.

Abstracts:The scientific fields of complexity, Artificial Life (ALife), and artificial intelligence (AI) share commonalities: historic, conceptual, methodological, and philosophical. Although their origins trace back to the 1940s birth of cybernetics, they were able to develop properly only as modern information technology became available. In this perspective, I offer a personal (and thus biased) account of the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and AI. Even when the trajectory of the article is influenced by my personal experience, the general questions posed (which outweigh the answers) will, I hope, be useful in aligning efforts in these fields toward overcoming—or accepting—their limits.

Abstracts:Epigenetic tracking (ET) is a model of development that is capable of generating diverse, arbitrary, complex three-dimensional cellular structures starting from a single cell. The generated structures have a level of complexity (in terms of the number of cells) comparable to multicellular biological organisms. In this article, we investigate the evolvability of the development of a complex structure inspired by the “French flag” problem: an “Italian Anubis” (a three-dimensional, doglike figure patterned in three colors). Genes during development are triggered in ET at specific developmental stages, and the fitness of individuals during simulated evolution is calculated after a certain stage. When this evaluation stage was allowed to evolve, genes that were triggered at later stages of development tended to be incorporated into the genome later during evolutionary runs. This suggests the emergence of the property of terminal addition in this system. When the principle of terminal addition was explicitly incorporated into ET, and was the sole mechanism for introducing morphological innovation, evolvability improved markedly, leading to the development of structures much more closely approximating the target at a much lower computational cost.

Abstracts:The NK fitness landscape is a well-known model with which to study evolutionary dynamics in landscapes of different ruggedness. However, the model is static, and genomes are typically small, allowing observations over only a short adaptive period. Here we introduce an extension to the model that allows the experimenter to set the velocity at which the landscape changes independently from other parameters, such as the ruggedness or the mutation rate. We find that, similar to the previously observed complexity catastrophe, where evolution comes to a halt when environments become too complex due to overly high degrees of epistasis, here the same phenomenon occurs when changes happen too rapidly. Our expanded model also preserves essential properties of the static NK landscape, allowing for proper comparisons between static and dynamic landscapes.

Abstracts:This letter presents the idea that neural backpropagation is exploiting dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward network are explored. This is contrasted with the equivalent standard layered approach to autoencoding. It is shown that such individualized processing is not detrimental and can improve network learning.