Forming Sphere with Self-Learning Particle-Bots.
7 years ago
Neuroscience based machine intelligence models by Gary Gaulin, contact: GarySGaulin@gmail.com
(1) ALPHA CLASS
While Alpha Rodney does exhibit some interesting behavioral characteristics, one really has to stretch the definition of intelligence to make it fit an Alpha-Class machine. The Intelligence is there, of course, but it operates on such a primitive level that little of significance comes from it. ....the essence of an Alpha-Class machine is its purely reflexive and, for the most part, random behavior. Alpha Rodney will behave much as a little one-cell creature that struggles to survive in its drop-of-water world. The machine will blunder around the room, working its way out of menacing tight spots, and hoping to stumble, quite accidentally, into the battery charger.
In summary, an Alpha-Class machine is highly adaptive to changes in its environment. It displays a rather flat and low learning curve, but there is virtually no change in the curve when the environment is altered.
(2) BETA CLASS
A Beta-Class machine uses the Alpha-Class mechanisms, but extends them to include some memory - memory of responses that worked successfully in the past.
The main-memory system is something quite different from the program memory you have been using. The program memory is the storage place for Rodney’s basic operating programs-programs that are somewhat analogous to intuition or the subconscious in higher-level animals. The main memory is the seat of Rodney’s knowledge and, in the case of Bete-Class machines, this means knowledge that is grained only by direct experience with the environment. A Beta-Class machine still relies on Alpha-like random responses in the early going but after experiencing some life and problem solving, knowledge in the main memory becomes dominant over the more primitive Alpha-Class reflex actions.
A Beta-Class machine demonstrates a rising learning curve that eventually passes the scoring level of the best Alpha-Class machine. If the environment is static, the score eventually rises toward perfection. Change the environment, however, and a Beta-Class machine suffers for a while, the learning curve drops down to the chance level. However, the learning curve gradually rises toward perfection as the Beta-Class machine establishes a new pattern of behavior. Its adaptive process requires some time and experience to show itself, but the end result is a more efficient machine.
(3) GAMMA CLASS
A Gamma-Class robot includes the reflex and memory features of the two lower-order machines, but it also has the ability to generalize whatever it learns through direct experience. Once a Gamma-Class robot meets and solves a particular problem, it not only remembers the solution, but generalizes that solution into a variety of similar situations not yet encountered. Such a robot need not encounter every possible situation before discovering what it is suppose to do; rather, it generalizes its first-hand responses, thereby making it possible to deal with the unexpected elements of its life more effectively.
A Gamma-Class machine is less upset by changes and recovers faster than the Beta-Class mechanism. This is due to its ability to anticipate changes.