Deep Fusion of Computational and Symbolic Processing

Abstract

A Subsymbolic and Symbolic Model for Learning Sequential Decision Tasks Integration of Different Information Processing Methods Symbol Pattern Integration Using Multilinear Functions Design of Autonomously Learning Controllers Using FYNESSE Modeling for Dynamical Systems with Fuzzy Sequential Knowledge Hybrid Machine Learning Tools: INSS - A Neuro-Symbolic System for Constructive Machine Learning A Generic Architecture for Hybrid Intelligence Systems New Paradigm toward Deep Fusion of Computational and Symbolig Processing Fusion of Symbolic and Quantitative Processing by Conceptual Fuzzy Sets Novel Knowledge Representation (Area Representation) and the Implementation by Neural Network A Symbol Ground Problem of Gesture Motion through a Self-organizing Network of Time-varying Motion Images.