Documentation
- License
- Artificial Assymetric Intelligence
- Psyop Detection
- Graph Based Naive Bayes
- Decision Ambiguity
- Collisikekkenshiki
- Distribute Nervoussystem And Dynamic Reward Allocation
- Shortest Path Between Statistics
- Intelligent Ultra Compressed Autonomous Agent
- Daticesupe: Concepts For Nightmare Solstice
- Selection By Vortex
- Maximum Read, Minimum Write
- Expanding On Containerization
BASIC CONCEPTS
INTERMEDIATE CONCEPTS
Shortest Path Between Statistics
These models seek to find the shortest path between interrelated statistical models, such as the shortest path between cult recruitment practices, conspiracy theories, and other clandestine research.
Such models also need to have the ability to return home, until self-modify its own training data in future versions of itself.
Complementary Systems
This can be combined with Reinforcement Naive Bayes in order to find the shortest path between statistical paths using the "Three Known Facts" solution.
Dynamic Reward Allocation
l1_reasses = "level one reasses"
l2_reasses = "level two reasses"
l3_reasses = "level tre reasses"
l4_reasses = "level fro reasses"
reward_model = [
[[l1_reasses, l1_reasses, l1_reasses, l1_reasses],
[l1_reasses, l1_reasses, l1_reasses, l2_reasses],
[l1_reasses, l1_reasses, l1_reasses, l3_reasses],
[l1_reasses, l1_reasses, l1_reasses, l4_reasses]],
[[l2_reasses, l2_reasses, l2_reasses, l1_reasses],
[l2_reasses, l2_reasses, l2_reasses, l2_reasses],
[l2_reasses, l2_reasses, l2_reasses, l3_reasses],
[l2_reasses, l2_reasses, l2_reasses, l4_reasses]],
[[l3_reasses, l3_reasses, l3_reasses, l1_reasses],
[l3_reasses, l3_reasses, l3_reasses, l2_reasses],
[l3_reasses, l3_reasses, l3_reasses, l3_reasses],
[l3_reasses, l3_reasses, l3_reasses, l4_reasses]],
[[l4_reasses, l4_reasses, l4_reasses, l1_reasses],
[l4_reasses, l4_reasses, l4_reasses, l2_reasses],
[l4_reasses, l4_reasses, l4_reasses, l3_reasses],
[l4_reasses, l4_reasses, l4_reasses, l4_reasses]],
]
row_options = [0, 1, 2, 3]
col_options = [0, 1, 2, 3]
arr_options = [0, 1, 2, 3]
cur_row = row_options.sample
cur_col = col_options.sample
cur_arr = arr_options.sample
current_reward_structure = reward_model[cur_row][cur_col][cur_arr]
if current_reward_structure == l1_reasses; reasses
elsif current_reward_structure == l2_reasses; 2.times do reasses end
elsif current_reward_structure == l3_reasses; 3.times do reasses end
elsif current_reward_structure == l4_reasses; 4.times do reasses end
else
reconsider
end