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] tactical behaviors capabilities customizing 2nd 3rd 4th 5th strategic decision making 2nd subsumption architecture 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th target selection shooting targets distances rescaling environment layouts 2nd fight progression projecting selecting 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th 13th 14th 15th 16th 17th 18th 19th 20th senses status 2nd training zones weapon selection criteria 2nd 3rd 4th 5th evaluation 2nd 3rd 4th 5th 6th 7th 8th 9th methodology 2nd tasks teaching animats temporal-differnce learning reinforcement learning 2nd 3rd 4th terminal states test beds 2nd testing adaptive behaviors 2nd conditions testing phase (development) testing set theoretical analysis problem solving 2nd 3rd 4th Thief time continuous events 2nd 3rd discrete events 2nd 3rd timing restrcitions weapon selection 2nd topologies feed forward topologies MLPs (multilayer perceptrons) 2nd connections 2nd hidden units 2nd 3rd 4th middle layers tournament selection (fitness) tracking projectiles 2nd training animats 2nd solutions training algorithms batch algorithms 2nd incremental algorithms 2nd MLPs (multilayer perceptrons) training interfaces incremental learning interface training procedures perceptron weight optimization algorithms data sets 2nd delta rule 2nd perception training 2nd 3rd training sets training zones strategic decision making 2nd weapons 2nd 3rd trajectories projectiles transition arrays finite state machines 2nd transition function finite state automata 2nd 3rd Transport Tycoon transversals DTs (decision trees) 2nd 3rd 4th 5th tree induction algorithm 2nd trees arrays lists trial and error animat learning animats Turn primitive Turn() function |