Tom Thumb Algorithm and von Neumann Universal Constructor

This article describes the addition to the von Neumann cellular automaton of the Tom Thumb Algorithm, a mechanism developed for the self-replication of multi-processor systems. Except for the cell construction process, every functionality of the original CA has been preserved in our new system. Moreover, the Tom Thumb Algorithm now allows the replication of any structure within the von Neumann environment, whatever its number of cells may be

Línea formativa de inteligencia artificial en la facultad de informática de la UPV-EHU

Con objeto de ampliar la capacitación de sus licenciados en el área de IA ésta facultad ofrece una línea de optatividad que reúne 37 créditos en 7 asignaturas fundamentales. Estas asignaturas se centran en torno a: Sistemas Basados en el Conocimiento, Procesamiento del Lenguaje Natural, Métodos Probabilísticos de la IA, Redes Neuronales e Inferencia Estadística

Heredity, Complexity, and Surprise: Embedded Self-Replication and Evolution in CA

Abstract. This paper reviews the history of embedded, evolvable selfreplicating structures implemented as cellular automata systems. We relate recent advances in this field to the concept of the evolutionary growth of complexity, a term introduced by McMullin to describe the central idea contained in von Neumann's self-reproducing automata theory. We show that conditions for such growth are in principle satisfied by universal constructors, yet that in practice much simpler replicators may satisfy scaled-down -yet equally relevant -versions thereof. Examples of such evolvable self-replicators are described and discussed, and future challenges identified

Enhancing the diversity of self-replicating structures using active self-adapting mechanisms

Numerous varieties of life forms have filled the earth throughout evolution. Evolution consists of two processes: self-replication and interaction with the physical environment and other living things around it. Initiated by von Neumann et al. studies on self-replication in cellular automata have attracted much attention, which aim to explore the logical mechanism underlying the replication of living things. In nature, competition is a common and spontaneous resource to drive self-replications, whereas most cellular-automaton-based models merely focus on some self-protection mechanisms that may deprive the rights of other artificial life (loops) to live. Especially, Huang et al. designed a self-adaptive, self-replicating model using a greedy selection mechanism, which can increase the ability of loops to survive through an occasionally abandoning part of their own structural information, for the sake of adapting to the restricted environment. Though this passive adaptation can improve diversity, it is always limited by the loop’s original structure and is unable to evolve or mutate new genes in a way that is consistent with the adaptive evolution of natural life. Furthermore, it is essential to implement more complex self-adaptive evolutionary mechanisms not at the cost of increasing the complexity of cellular automata. To this end, this article proposes new self-adaptive mechanisms, which can change the information of structural genes and actively adapt to the environment when the arm of a self-replicating loop encounters obstacles, thereby increasing the chance of replication. Meanwhile, our mechanisms can also actively add a proper orientation to the current construction arm for the sake of breaking through the deadlock situation. Our new mechanisms enable active self-adaptations in comparison with the passive mechanism in the work of Huang et al. which is achieved by including a few rules without increasing the number of cell states as compared to the latter. Experiments demonstrate that this active self-adaptability can bring more diversity than the previous mechanism, whereby it may facilitate the emergence of various levels in self-replicating structures