SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model.

ABSTRACT: To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand their environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inferences easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environment and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, the connected modules are dependent on each other and their parameters must be optimized as a whole. Conventionally, the equations for parameter

SUBMITTER: Nakamura T 

PROVIDER: S-EPMC6028621 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature