AI Game Development: Synthetic Creatures with Learning and Reactive Behaviors
| [A] [B] [C] [D] [E] [F] [G] [H] [I] [J] [K] [L] [M] [N] [O] [P] [Q] [R] [S] [T] [U] [V] [W] [X] object-attribute-value paradigm 2nd KR (knowledge representation) 2nd objects behaviors collecting contraptions 2nd 3rd criteria 2nd 3rd contraptions items specification 2nd 3rd 4th 5th 6th 7th test beds 2nd obstacle avoidance containment 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th generalized obstacle avoidance 2nd Occam's razor odometers off-policy algorithms offline games learning systems offsetting modifiers on-policy algorithms OnCollision() function online forums AI specialists online games learning systems ontogenetic part animats operators fuzzy set theory 2nd genetic operators defensive actions 2nd 3rd 4th phenotype-specific operators optimal policies reinforcement learning 2nd optimal returns reinforcement learning 2nd 3rd optimization finite state machines 2nd 3rd 4th learning systems optimization phase (development) optimization techniques perceptrons 2nd 3rd 4th brute-force optimization 2nd 3rd local minima 2nd 3rd momentum 2nd 3rd numeric optimization simulated annealing 2nd 3rd 4th steepest descent optimization 2nd 3rd 4th 5th 6th training procedures 2nd 3rd 4th 5th 6th 7th 8th 9th weights 2nd 3rd 4th 5th 6th 7th 8th organization reactive architectures 2nd 3rd outcome output interpreting perceptrons activation function 2nd 3rd computing 2nd net sums 2nd 3rd 4th sketching output alphabet Mealy machines 2nd output function Mealy machines 2nd 3rd outputs perceptrons 2nd 3rd 4th 5th 6th 7th |