Goal

Goal

"A goal is not always meant to be reached. Often it serves simply as something to aim at." — Bruce Lee

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What Is It?

A Goal is a quest objective that drives the adventure forward. It defines what the player is trying to achieve and when they've succeeded (or failed).

Goals create narrative tension — the gap between current state and desired state.

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Schema Mechanism Connection

From Gary Drescher's theory:

Context → Action → Result

Goals define the desired Result that motivates Action. The adventure is a laboratory for learning causal chains that achieve goals.

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Goal Properties

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Completion Conditions

Natural language conditions that compile to JS/​PY:

complete_when: "player has the treasure"
complete_when_js: "(ctx) => ctx.player.inventory.includes('treasure')"

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Rewards

Goals can grant various rewards:

reward:
  buff: "Hero's Glory"           # Temporary effect
  item: "champion-medal"         # Object
  unlock: "secret-room"          # New access
  narrative: "The kingdom celebrates!"  # Story beat

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Progress Tracking

For multi-step goals:

progress:
  collected: 3
  needed: 5

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Goal Hierarchies

Goals can nest:

parent: defeat-dragon
children:
  - find-magic-sword
  - learn-dragon-weakness
  - reach-dragon-lair

Complete all children to complete the parent.

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Failure Conditions

Goals can fail:

fail_when: "time reaches midnight AND prisoner is not freed"
fail_message: "You were too late."

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Examples

Simple Quest

goal:
  id: find-treasure
  name: "Find the Treasure"
  complete_when: "player has the treasure"

Timed Challenge

goal:
  id: rescue-mission
  name: "Save the Prisoner"
  complete_when: "prisoner is freed"
  fail_when: "10 turns pass without rescue"
  priority: urgent

Collection Quest

goal:
  id: gather-keys
  name: "Collect All Keys"
  complete_when: "player has 5 enchanted keys"
  progress:
    collected: 2
    needed: 5

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Related Skills

• adventure — Contains active goals
• hero-story — Narrative structure
• needs — Internal motivations

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Protocol Symbol

SCHEMA-MECHANISM — Goals drive causal learning