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May 13, 2014, 06:38:19 PM |
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For one of my projects we used neural networks for to predict uptrends and downtrends, I have also used a number of data mining techniques such as time series clustering for predicting seismic events using GPS data.
A classifier could be trained to detect various events and conditions using volume history, price history, and other features such as keyword and sentiment analysis given discussion about the coin on social media sites like twitter.
Using this one can create a state/goal based architecture where a coin can learn to adapt its behavior to meet certain criteria such as stable growth. I know at least one coin did a very simple attempt at adjusting block rewards, but the behavior and adaptive mechanism was unable to learn and the coin did not do well. An adaptive coin would have learned that the behavior was ineffective and changed.
Cognitive architectures based on artificial neural networks generally use a back-propagation based neural network to adjust the likelihood they will engage in a behavior in the future based upon whether it got them close to or farther from meeting a goal. Parameters such as how long they wait before calculating this can be altered.
Behaviors:
A stabilization fund could be used to generate behaviors via buying and selling, staking and mining rates could be adjusted, the coin could be frozen on market in the case of a pump and dump if an api would allow that, and various other things.
I would use neural networks.
This would also apply to decentralized autonomous corporations. A DAC should be adaptive and capable of learning if we want them to be fully autonomous and decentralized. We can set the initial behaviors but allow for them to be turned on or off based upon learning.
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