Let’s assume that we have a solution for Diverse perceptron Nerual Network from Tools in RL. , i.e., we have an Agent that can play chess by using this machine. We can see that machine $M$ is the part of Agent Which is outside in the world of the machine, which is the part of the world now it is inside Agent (part of agent). ( The agent I am talking about Is model-based ) since M is outside, we reflect M in Agent’s model....

Turing machine with a coin laws and wills this is same as predictability and randomness Randomness to Evolution to RL Randomness and lack of knowledge Free will to Randomness Proof of Randomness Resource/randomness and predictability

Black swan theory - Wikipedia Theory that unexpected events have major impacts, often rationalized in hindsight. How can we trust induction hypothesis? Where is the place for induction hypothesis in the logic?