A project to develop my neural model of learning into a general artificial intelligence system.
The project starts with a C+ program for the Arduino that models a simple nervous systems that controls a Roomba. This simple nervous system, consisting of about a dozen neurons, is able to demonstrate the following behavioral and learning capabilities: stimulus-response reflexes; "operant" behaviors, i.e., self generated goal directed behavior; Classical Conditioning (like Pavlov’s dogs); Operant conditioning i.e., learning based on rewards and punishments (per B.F. Skinner); secondary reinforcement (a neutral stimulus becomes a reward): behavior chaining (learning a sequence of behaviors). Further, it intrinsically models the affects (prototypical emotions) of pleasure and aversion and can be readily extended to model the affects of Silvan S. Tomkins' affect theory. These capabilities of this simple model strongly suggests that, with development, it could replicate the behavioral and learning capabilities of animals up to, and surpassing, human beings. This project is intended to develop an open source version of such an AI system.
This site contains the initial version of the program, instructions on how neurons function, my rules for synaptic modification, schematic representation of the presently modelled nervous system, and a map of "synaptic efficacies" mathamatically models the nervous system. The schematic and map may be found in the first issue.
The Matheta.com website provides much additonal information about the model as well as vidoes demonstrating various cabilities of the Roomba as controlled by the Arduino program.