Initial commit: add .gitignore and README

fusionagi/world_model/__init__.py

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+"""World model and simulation for AGI."""
+
+from fusionagi.world_model.base import WorldModel, SimpleWorldModel
+from fusionagi.world_model.rollout import run_rollout
+
+__all__ = ["WorldModel", "SimpleWorldModel", "run_rollout"]

fusionagi/world_model/base.py

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+"""World model: causal state transitions for AGI."""
+
+from typing import Any, Protocol
+
+from fusionagi.schemas.plan import Plan
+from fusionagi.schemas.world_model import StateTransition, UncertaintyInfo
+
+
+class WorldModel(Protocol):
+    """Protocol for causal model of environment: how actions change state."""
+
+    def predict(self, state: dict[str, Any], action: str, action_args: dict[str, Any]) -> StateTransition:
+        """Predict result of action in state."""
+        ...
+
+    def uncertainty(self, state: dict[str, Any], action: str) -> UncertaintyInfo:
+        """Return uncertainty/risk for action in state."""
+        ...
+
+
+class SimpleWorldModel:
+    """
+    Minimal world model: state is a dict; actions are recorded but
+    prediction returns placeholder. Replace with real causal model.
+    """
+
+    def __init__(self) -> None:
+        self._transitions: list[StateTransition] = []
+
+    def predict(self, state: dict[str, Any], action: str, action_args: dict[str, Any]) -> StateTransition:
+        """Return placeholder transition (state unchanged)."""
+        return StateTransition(
+            from_state=dict(state),
+            action=action,
+            action_args=dict(action_args),
+            to_state=dict(state),
+            confidence=0.5,
+        )
+
+    def uncertainty(self, state: dict[str, Any], action: str) -> UncertaintyInfo:
+        return UncertaintyInfo(confidence=0.5, risk_level="medium", rationale="SimpleWorldModel placeholder")

fusionagi/world_model/rollout.py

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+"""Rollouts: simulate plan before executing."""
+
+from typing import Any, Callable, Protocol
+
+from fusionagi.schemas.plan import Plan
+from fusionagi.schemas.world_model import StateTransition
+from fusionagi._logger import logger
+
+
+class WorldModelLike(Protocol):
+    def predict(self, state: dict[str, Any], action: str, action_args: dict[str, Any]) -> StateTransition: ...
+
+
+def run_rollout(
+    plan: Plan,
+    initial_state: dict[str, Any],
+    world_model: WorldModelLike,
+    step_action_fn: Callable[[str, dict], str] | None = None,
+) -> tuple[bool, list[StateTransition], dict[str, Any]]:
+    """
+    Simulate plan in world model. Returns (success, transitions, final_state).
+    step_action_fn(step_id, step_dict) -> action name for prediction.
+    """
+    state = dict(initial_state)
+    transitions: list[StateTransition] = []
+    for step in plan.steps:
+        action = step.tool_name or "unknown"
+        action_args = step.tool_args or {}
+        if step_action_fn:
+            action = step_action_fn(step.id, step.model_dump())
+        trans = world_model.predict(state, action, action_args)
+        transitions.append(trans)
+        state = dict(trans.to_state)
+        if trans.confidence < 0.3:
+            logger.warning("Rollout low confidence", extra={"step_id": step.id, "confidence": trans.confidence})
+            return False, transitions, state
+    logger.info("Rollout completed", extra={"steps": len(transitions)})
+    return True, transitions, state