Modeling Human Infant Learning in Embodied Artificial Entities to Produce Grounded Concepts

I present a system for concept development in an artificial entity. The concept development is designed around the foundations of human cognition while at the same time remaining grounded in the agent or robot’s own perception of its world

Grounded Concept Development Using Introspective Atoms

In this paper we present a system that uses its underlying physiology, a hierarchical memory and a collection of memory management algorithms to learn concepts as cases and to build higher level concepts from experiences represented as sequences of atoms. Using a memory structure that requires all base memories to be grounded in introspective atoms, the system builds a set of grounded concepts that must all be formed from and applied to this same set of atoms. All interaction the system has with its environment must be represented by the system itself and therefore, given a complete ability to perceive its own physiological and mental processes,can be modeled and recreated

Body-Based Reasoning Using a Feeling-Based Lexicon Mental Imagery and an Object-Oriented Metaphor Hierarchy

Our computer reasoning system uses a set of memory networks, a spatial simulator and an object-oriented hierarchy of body-based spatial metaphors to reason about abstract concepts. The metaphor hierarchy is based on the hierarchical nature of embodied actions, and the simulator is designed to model these actions. The system maps its input to a set of spatial metaphors at the most detailed level possible, and then uses modeling of the metaphorical concepts to reason about the original input