Development of Functional Requirements for Cognitive Motivated Machines.

摘要

Machine Intelligence, and all of its associated fields and specialties, is a wide and complex area actively researched in laboratories around the world. This work aims to address some of the critical problems inherent in such research, from the most basic neural network structures, to handling of information, to higher level cognitive processes. All of these components and more are needed to construct a functioning intelligent machine. However, creating and implementing machine intelligence is easier said than done, especially when working from the ground up as many researchers have attempted. Instead, it is proposed that the problem be approached from both bottom-up and top-down level design paradigms, so that the two approaches will benefit from and support one another. To clarify, my research looks at both low level learning, and high level cognitive models and attempts to work toward a middle ground where the two approaches are combined into a single cognitive system. Specifically, this work covers the development of the Motivated Learning Embodied Cognition (MLECOG) model, and the associated components required for it to function. These consist of the Motivated Learning approach, various types of memory, action monitoring, visual and mental saccades, focus of attention, attention switching, planning, etc. Additionally, some elements needed for processing sensory data will be briefly examined because they are relevant to the eventual creation of a full cognitive model with proper sensory/motor I/O. The development of the Motivated Learning cognitive architecture is covered from its initial beginnings as a simple Motivated Learning algorithm to its advancement to a more complex architecture and eventually the proposed MLECOG model. The objective of this research is to show that a cognitive architecture that uses motivated learning principles is feasible, and to provide a path toward its development.