Roxor128 wrote: Sun Jun 23, 2019 10:14 am
How you'd modify the weights to make it learn, I have no idea. I never managed to get my head around that aspect of neural networks.
Maybe you can have a set of value that represent the travel time of a train like iron ore. Your neural network could be oriented to call train from the location that is usually the fastest to answer the call, with the aim of adapting to traffic congestion.
You can see it as many experiments, each train arriving gives the system an new information , 1 time travel, and modify a weight that would be the average time travel from one spot.
Thus the system would learn to avoid certains areas and use others more often with time. Like if you suddenly congestion on portion of the rail network without noticing at first, the system will start giving bad travel times to the trains from that area and eventually adapt by stop calling them, reducing the congestion.
A more complex system would require that more combinators are build to store more information, because the average time travel from one spot is not very precise, you would want to make some lanes A B C D E and so on,several layers of neurons would be needed to keep track of the different open/close possibilty A is on while BCDE are off , A and B are on rest are off ect.
If you model each possibility at the same time the information each train gives is then "the traveltime from one spot when train comes from C when A and B are close and D and E open" for example.
The value that changes when a train arrives is still a timetravel that will be put in some sort of "average time travel per situation" table. but you give yourself the opportunity to have more precise understanding, by multiplicating the number of nodes you can apply the "weight" into a more specific situation.
The system would then know that when you use copper and iron at the same time you need to call trains from patches that are far away from each other so that the train don't cross because it would give longer time travels and the system could know it, but the system wouldn't mind using some of the iron that is near copper if you require iron and coal at the same time.
Even more would be to keep track of the open/close possibilty A is on while BCDE are off SINCE AT LEAST 3 minutes , A and B are on rest are off BUT "E" WAS CLOSED ONLY 10 SEC AGO ( so trains returning might still cause issue ). If you make different nodes for those situation, then the weight is still an average time travel , but it carries more value for the system.
That was one way i had in mind for application of neural entities, i am not sure it is correct, it is possible to implement that sort of logic with exisiting combinators because as far as i know circuits networks are Turing complete.
But could totally be made easier if you have a neuron entity that is a signle combinator compared to something like shown here
viewtopic.php?t=28641 which seems to act like one if understood what i saw at the time.
I understand the training part as feeding information to your system that will be organised and reused later. The neurons are in the brain but you need at least 1 eye of some sort.
The more precisely you wish to index this information, the more experiments you need to run for the datas to start making sense. The more neurons you need to model the more configuration. The bigger the brain, especially if you add an 1 ear to the 1 eye.
) entity, too. The possible rounding problems would be a part of the game.