Algorithmic Information Dynamics of Persistent Patterns and Colliding Particles in the Game of Life
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We demonstrate the way to apply and exploit the concept of algorithmic information dynamics in the characterization and classification of dynamic and persistent patterns, motifs and colliding particles in, without loss of generalization, Conway's Game of Life (GoL) cellular automaton as a case study. We analyze the distribution of prevailing motifs that occur in GoL from the perspective of algorithmic probability. We demonstrate how the tools introduced are an alternative to computable measures such as entropy and compression algorithms which are often nonsensitive to small changes and features of non-statistical nature in the study of evolving complex systems and their emergent structures.
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