Phys.org February 20, 2019
Complex behaviour emerges from interactions between objects produced by different generating mechanisms. Researchers in Sweden introduce a universal, unsupervised and parameter-free model-oriented approach, based on the seminal concept and the first principles of algorithmic probability, to decompose an observation into its most likely algorithmic generative models. They demonstrated its ability to deconvolve interacting mechanisms regardless of whether the resultant objects are bit strings, space–time evolution diagrams, images or networks. Although this is mostly a conceptual contribution and an algorithmic framework, they have provided numerical evidence evaluating the ability of the methods to extract models from data produced by discrete dynamical systems. The research could lead to a universal model of artificial intelligence…read more. Open Access TECHNICAL ARTTICLEÂ
Using algorithmic information theory, KAUST researchers have developed an approach for inferring the causal processes that give rise to a complex observed interaction. Credit: KAUST, Xavier Pita