dogworld

name

dogworld

description

Use to build agents/worlds in the Dogworld engine — belief-gated, abductive, soft-RNG agents whose beliefs become world-facts only if WARRANTED (else WISDOM-1), with catalysis/emergence as the computed "good" and signal-detection calibration. Triggers on "dogworld", "add an agent/perception/action", "warrant gate", "WISDOM penalty", "abductive world", "catalysis/RAF/fitness", "calibration/d-prime", "the owl/dog world".

Dogworld — how to build in it

Engine at the repo root. Read DESIGN.md for the architecture, then use this. Core is stdlib-only; default sampler is MockArbiter (no API). Never make the gate depend on a heuristic, and never let an agent grant itself warrant/reward (rule-two).

The one pattern: an agent is a class whose decorated methods self-reflect into tools+urges

from dogworld import Agent, action, perception, RngSpec, World, Stats, Engine, MockArbiter

class Owl(Agent):
    name = "owl"
    @action(rng=RngSpec(values={"p": 0.6}), closes="owl_hooted@{t}")   # closes a FOOD fact (spontaneous)
    def hoot(self): return "HOOOO"

class Dog(Agent):
    name = "dog"
    @perception(                       # a belief: fires by soft prior, abduces its warrant BACKWARD
        requires="owl_hooted@{t}",     # the fact the world must hold for the belief to be true
        abduces="near({self},owl)",    # reified ON WARRANT (effect -> cause)
        penalty="You thought you heard an owl. In fact, there was not one when you looked.",
        urge="You think you heard an owl (felt-strength {p}). Bark only if you trust it.",
        rng=RngSpec(values={"p": 0.7}))
    def bark(self): return "WOOF!"

world = World()
Engine(world, [Owl(), Dog()], stats=Stats(), arbiter=MockArbiter(seed=7)).run(12)

The gate adjudicates each fired perception: warranted + consistent -> execute, close the abduced fact (catalysis edge warrant -> abduced); unwarranted/inconsistent -> WISDOM -1 + the penalty (returned to the agent, option B). Templates use {self} and {t}.

The four things you can build

1. A cascade / catalysis chain — make one agent's perception requires another's abduces (master.investigate requires near(dog,owl)). dogworld.catalysis: cat(f) (downstream structure), fitness(agent), max_raf(reactions, food) (emergence = a self-sustaining set; collapses w/o food).
2. Calibration — dogworld.sdt: a Channel(d') emits a noisy percept; Detector(tau) decides; sweep tau -> interior optimum that matches optimal_threshold(...). Blind percept (d'=0) can't calibrate.
3. Contradiction — world.declare_functional("at", subject_arity=1) so at(dog,barn) blocks at(dog,field); the gate penalizes an abduction that contradicts a closed fact.
4. A real LLM arbiter — LLMArbiter(persona=...) (MiniMax via dogworld.llm, key in env) reasons over the urges + feedback. seams/cave_runtime.py runs it through cave-teams' gate(body, phi).

Invariants to preserve (or you break the thesis)

• Gate checks stay decidable (membership + declared conflicts). Soundness = prevention, not suppression.
• "good" is world-conferred: cat/fitness/calibration computed from real closures, never self-granted.
• Add a test (tests/) + update DESIGN.md in the same change. Run python tests/run_all.py (must pass).