Artificial Life最新文献

Kuhnian philosophy of science implies that progress in the study of open-ended evolution (OEE) would be accelerated if the OEE science community were to agree on some examples of striking success in OEE science. This article recounts the important role of scientific paradigms and scientific exemplars in creating the productivity of what Kuhn, in The Structure of Scientific Revolutions, calls "normal" science, and it describes how the study of OEE today would benefit from exhibiting more of the hallmarks of normal science. The article concludes by describing five proposed projects that would help create a consensus in the OEE community on some good examples of the scientific study of OEE.

Threshold models in which an individual's response to a particular state of the world depends on whether an associated measured value exceeds a given threshold are common in a variety of social learning and collective decision-making scenarios in both natural and artificial systems. If thresholds are heterogeneous across a population of agents, then graded population level responses can emerge in a context in which individual responses are discrete and limited. In this article, I propose a threshold-based model for social learning of shared quality categories. This is then combined with the voting model of fuzzy categories to allow individuals to learn membership functions from their peers, which can then be used for decision-making, including ranking a set of available options. I use agent-based simulation experiments to investigate variants of this model and compare them to an individual learning benchmark when applied to the ranking problem. These results show that a threshold-based approach combined with category-based voting across a social network provides an effective social mechanism for ranking that exploits emergent vagueness.

An embodied agent influences its environment and is influenced by it. We use the sensorimotor loop to model these interactions and quantify the information flows in the system by information-theoretic measures. This includes a measure for the interaction among the agent's body and its environment, often referred to as morphological computation. Additionally, we examine the controller complexity, which can be seen in the context of the integrated information theory of consciousness. Applying this framework to an experimental setting with simulated agents allows us to analyze the interaction between an agent and its environment, as well as the complexity of its controller. Previous research revealed that a morphology adapted well to a task can substantially reduce the required complexity of the controller. In this work, we observe that the agents first have to understand the relevant dynamics of the environment to interact well with their surroundings. Hence an increased controller complexity can facilitate a better interaction between an agent's body and its environment.

This work concerns the long-term collective excitability properties and the statistical analysis of the critical events displayed by a recently introduced spatiotemporal many-body model, proposed as a new paradigm for Artificial Life. Numerical simulations show that excitable collective structures emerge in the form of dynamic networks, created by bursts of spatiotemporal activity (avalanches) at the edge of a synchronization phase transition. The spatiotemporal dynamics is portraited by a movie and quantified by time varying collective parameters, showing that the dynamic networks undergo a "life cycle," made of self-creation, homeostasis, and self-destruction. The power spectra of the collective parameters show 1/f power law tails. The statistical properties of the avalanches, evaluated in terms of size and duration, show power laws with characteristic exponents in agreement with those values experimentally found in the neural networks literature. The mechanism underlying avalanches is argued in terms of local-to-collective excitability. The connections that link the present work to self-organized criticality, neural networks, and Artificial Life are discussed.