Bridging AGI Theory and Practice with Galois Connections

Multiple cognitive algorithms posited to play a key role in AGI (forward and backward chaining inference, clustering and concept formation, evolutionary and reinforcement learning, probabilistic programming, etc.) are given a common formulation as recursive discrete decision processes involving optimizing functions defined over metagraphs, in which the key decisions involve sampling from probability distributions over metagraphs and enacting sets of combinatory operations on selected sub-metagraphs. This forms a bridge between abstract conceptions of general intelligence founded on notions of algorithmic information and complex systems theory, and the practical design of multi-paradigm AGI systems.