Creature learning

Owns a creature's learned decision parameters: weights linking perceived situations or objects to actions. After the creature acts, a reward or punishment signal (a player stroke or slap, or a change in an internal drive) adjusts the weights for that situation-action pair, so its future selection probability rises or falls and generalizes to similar situations. Creatures (1996) uses about 1,000 neurons in 9 lobes; a 16-cell decision layer picks the strongest-firing action; drive reduction releases a reward chemical and drive increase a punishment chemical, which reinforce excitatory or inhibitory synapses, so norns learn to eat when hungry but not when full. In Black & White (2001) the creature follows a belief-desire-intention model with desires as simplified perceptrons and opinions as decision trees; the player strokes or slaps it after actions and uses leashes to direct its attention. Inputs: player feedback, drives, perceived objects. Outputs: changed action probabilities.

The player becomes a parent or trainer. Each reward is a bet about what the creature will generalize, and personality emerges from the history of training. Success feels like understanding; failure feels like a stubborn pet, and players blame themselves for bad habits.

• learning rate and how quickly old associations fade
• credit assignment window that decides which recent action receives the feedback
• granularity of the situation representation (object types, desires, contexts), which decides what generalizes
• feedback channels such as stroke and slap, food, demonstration, and attention leashes
• innate drives and biases the learning starts from

• Credit assignment errors attach feedback to the wrong action or object, and players cannot tell why the creature learned something.
• Learning too slowly makes training feel ignored; too quickly lets one slap erase hours of shaping.
• Generalization surprises, such as a creature learning to eat villagers, read as bugs rather than lessons.
• Emergent learned behavior varies by player, which makes testing and balancing expensive; Black & White was later called overrated for underusing its creature.

• Creatures from the same start diverge into distinct personalities, which drives players to share and compare them.
• Learned behavior acts on the world, so training choices reshape villages, alignment, and appearance in Black & White.
• Learning from internal drives without player input produces self-taught habits and odd local optima.

• Can modern reinforcement learning or language-conditioned policies give creatures learning that generalizes the way players intend, with explainable credit assignment?
• Does exposing a creature's learned associations improve attachment or destroy the illusion of a mind?