Abstract
This paper describes an automatic process for combining patterns and features, to guide a search process and reason about it. It is based on the functionality that a human brain might have, which is a highly distributed network of simple neuronal components that can apply some level of matching and cross-referencing over retrieved patterns. The process uses memory in a dynamic way and it is a directed process that is interested in self-images. The paper gives one example of the process, using computer chess as a case study. The second half of the paper then presents a formal language for describing the global pattern sequences and transitions. These pattern ensembles are created from the same techniques that the search and prediction processes require and they define an outer framework that patterns can evolve into. They can also be created automatically, resulting in further functionality for the generic cognitive model. The paper also adds further detail to the cognitive model by putting different types of hierarchical structure into context.
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URL
http://arxiv.org/abs/1803.01690