Finding causal theories quickly enough - building a responsive Apperception Engine
Year: 2023
Type: article
Abstract: I describe the implementation of a responsive Apperception Engine, based on the original design described in “Making sense of sensory input” by Richard Evans et al, for use in a distributed cognitive architecture targeting simple robots. An Apperception Engine applies symbolic machine learning to produce causal theories from short sequences of observations. The causal theories are small, human-readable logic programs that model an environment’s latent generative processes and predict incoming observations. Finding these causal theories quickly is a difficult problem due to the size of the search space. I built an Apperception Engine that can infer good enough causal theories quickly enough to be useful to a robot actively engaging with its environment.
Author: Cloutier, Jean-François
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6967420041
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