Sub Conscious Learning System

I had a thought, today. It was for a type of learning which seemed quite natural, but I had not even imagined being able to implement, previously. It is a system for self-scoring. As you might know, in general evolutionary algorithms, a computer program is given a score based on how well it functions, and it's children carry on that score, and build onto it. Eventually, after many generations, it gets smarter and smarter. However, with this system, if forces the entire species to lean as a whole, rather then individually. This works perfectly fine if the algorithm is designed to only master a single task, even if it's complex. But what about more complex tasks?

How could an artificial intelligence agent be designed, so that it can make it's own choices. Score it's own actions and abilities. This is an important trait for agents who want to have any hope in thriving in a complex environment, where more then a single action must be scored. This is a critical base for higher level thinking machines. Machines that learn strategy, and can plan out actions ahead of time. Taking into consideration events which only they specifically have encountered, not their previous generations.

Here is my proposal. For this system, the original generation based learning algorithm is still in place. This judges actions solely around things such as survival of the individual, and expanding the population. But there is also another learning system in play, here. The second learning system is per agent, and effects only their brain. This is constantly updating, as well.

In this system, imagine we have an entire AI brain network. Neurons and all. Now, take a chunk of it. Say, 25% or so, and mark this as the subconscious. This part is unable to be edited by the agent. In addition, this chunk is close to the end or the network, and all linked together closely. Yes, there are many connections going in, and also several going out. Though, in the middle of these, there are "emotion" neurons. These release emotional responses based on their input. Positive input, results in a positive output. Negative input, results in a negative output. Highly positive input, results in a highly positive output. And so on. Now, these emotional responses are the "score" values for the agent. It wants these to be as high as possible. So it is constantly weighing in different neuron values for the rest of the brain to try to influence the emotions. Now, the main evolutionary algorithm still wants the brain to function a certain way, to accomplish it's overall tasks. Survive, and thrive.

When a new agent is born, it's default brain state is given to it by the evolutionary algorithm. It also decides which parts of the brain are editable, or not. (This network is also assuming the same setup as the one I described in my article "Smoother Node Map Learning".)

By doing it this way, the agent is allowed to learn and adapt to it's environment at it's own pace, while the main algorithm gives a general path for everyone to follow. Both algorithms fight each other, and also work together to achieve a goal.

As each agent is it's own brain, and learning path, each one will have it's own personality, goals, and so on. Each will behave in their own way, but still follow the same overall instincts given to them when they are born.

I am quite excited to see how this plays out, so I will be playing with the code for it. (Though most likely I'll just get lazy and never finish it, as is the case for most of these ideas.)