Artificial Life最新文献

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.

Reproduction, development, and individual interactions are vital yet complex natural processes. Tierra (an ALife model proposed by Thomas Ray) and cellular automata, which can manage these aspects in a complex manner, are significantly limited in their ability to express morphology and behavior. In contrast, the virtual creatures proposed by Karl Sims have a considerably higher degree of freedom in terms of morphology and behavior. However, they also exhibit a limited capacity for processes like reproduction, development, and individual interactions. In addition, they employ genetic algorithms, which can result in a loss of biological diversity, as their implementation necessitates predefining a fitness function. Contrarily, the evolution of natural life is determined by mutation and natural selection, rather than by a human-defined fitness function. This study carefully extracts the characteristics of these models to propose a new Artificial Life model that can simulate reproduction, development, and individual interactions while exhibiting a high expressive power for morphology and behavior. The model is based on the concept of incorporating Tierra and cellular automata mechanisms into a cell that moves freely in 3-D space. In this model, no predefined fitness function or form that qualifies as a living creature exists. In other words, this approach can be rephrased as searching for persistent patterns, which is similar to the approach of Conway's Game of Life. The primary objective of this study was to conduct a proof-of-concept demonstration to showcase the capabilities of this model. Guideless simulation by the proposed model using mutation and natural selection resulted in the formation of two types of creatures-dumbbell shaped and reticulated. These creatures exhibit intriguing features, exploiting the degrees of freedom inherent to the proposed model. Particularly noteworthy is their unique method of reproduction, which bears a striking resemblance to that of real organisms. These results reinforce the potential of this approach in modeling intricate processes observed in actual organisms and its ability to generate virtual creatures with intriguing ecologies.

It is very important to model the behavior of protocells as basic lifelike artificial organisms more and more accurately from the level of genomes to the level of populations. A better understanding of basic protocell communities may help us in describing more complex ecological systems accurately. In this article, we propose a new comprehensive, bilevel mathematical model of a community of three protocell species (one generalist and two specialists). The aim is to achieve a model that is as basic/fundamental as possible while already displaying mutation, selection, and complex population dynamics phenomena (like competitive exclusion and keystone species). At the microlevel of genetic codes, the protocells and their mutations are modeled with Turing machines (TMs). The specialists arise from the generalist by means of mutation. Then the species are put into a common habitat, where, at the macrolevel of populations, they have to compete for the available nutrients, a part of which they themselves can produce. Because of different kinds of mutations, the running times of the species as TMs (algorithms) are different. This feature is passed on to the macrolevel as different reproduction times. At the macrolevel, a discrete-time dynamic model describes the competition. The model displays complex lifelike behavior known from population ecology, including the so-called competitive exclusion principle and the effect of keystone species. In future works, the bilevel model will have a good chance of serving as a simple and useful tool for studying more lifelike phenomena (like evolution) in their pure/abstract form.

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.

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.