Observing Artificial Life
An Abstract Algorithmic Framework
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Establishing the presence of "life-like emergent phenomena" is a major goal of simulation based artificial life (ALife) studies. This book presents an abstract algorithmic framework towards this goal. Presented framework is used to study observational processes by which time-varying entities are identified and their behaviour is observed to uncover higher-level emergent phenomena. Observations are formalized as algorithmic transformations from the underlying dynamic structure of the model universe to a set of abstract components needed to establish the presence of emergent phenomena. Observabi...
Establishing the presence of "life-like emergent phenomena" is a major goal of simulation based artificial life (ALife) studies. This book presents an abstract algorithmic framework towards this goal. Presented framework is used to study observational processes by which time-varying entities are identified and their behaviour is observed to uncover higher-level emergent phenomena. Observations are formalized as algorithmic transformations from the underlying dynamic structure of the model universe to a set of abstract components needed to establish the presence of emergent phenomena. Observability of evolutionary dynamics is used as a defining characteristic to infer the emergence of life in any ALife model. Computational complexity theoretic analysis of the problem of observability provides insights for an algorithmic realization of the presented framework for an automated discovery of life-like phenomena during model simulations. Anyone interested in the field of ALife and its operational processes would find this work useful. It should be of special interest to researchers, who aim to programmatically discover emergent properties in their models using simulations based studies.
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