A model for multimodal humanlike perception based on modular hierarchical symbolic information processing, knowledge integration, and learning

Automatic surveillance systems as well as autonomous robots are technical systems which would profit from the ability of humanlike perception for effective, efficient, and flexible operation. In this article, a model for humanlike perception is introduced based on hierarchical modular fusion of multi-sensory data, symbolic information processing, integration of knowledge and memory, and learning. The model is inspired by findings from neuroscience. Information from diverse sensors is transformed into symbolic representations and processed in parallel in a modular, hierarchical fashion. Higher-level symbolic information is gained by combination of lower-level symbols. Feedbacks from higher levels to lower levels are possible. Relations between symbols can be learned from examples. Stored knowledge influences the activation of symbols. The model and the underlying concepts are explained by means of a concrete example taken from building automation.