445865e429
Some checks failed
- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available) - Integrate GPU deduplication into multi_agent/consensus_engine.py - Add semantic_search() method to memory/semantic_graph.py with GPU acceleration - Integrate GPU training into self_improvement/training.py AutoTrainer - Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names) - Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.) - Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint]) - Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds - Add type hints and docstrings to verification/outcome.py - Fix E402 import ordering in witness_agent.py - Fix F821 undefined names in vector_pgvector.py and native.py - Fix E741 ambiguous variable names in reflective.py and recommender.py All 276 tests pass. 0 ruff errors. 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
"""World model: causal state transitions for AGI."""
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from typing import Any, Protocol
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from fusionagi.schemas.world_model import StateTransition, UncertaintyInfo
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class WorldModel(Protocol):
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"""Protocol for causal model of environment: how actions change state."""
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def predict(self, state: dict[str, Any], action: str, action_args: dict[str, Any]) -> StateTransition:
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"""Predict result of action in state."""
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...
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def uncertainty(self, state: dict[str, Any], action: str) -> UncertaintyInfo:
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"""Return uncertainty/risk for action in state."""
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...
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class SimpleWorldModel:
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"""
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Minimal world model: state is a dict; actions are recorded but
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prediction returns placeholder. Replace with real causal model.
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"""
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def __init__(self) -> None:
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self._transitions: list[StateTransition] = []
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def predict(self, state: dict[str, Any], action: str, action_args: dict[str, Any]) -> StateTransition:
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"""Return placeholder transition (state unchanged)."""
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return StateTransition(
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from_state=dict(state),
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action=action,
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action_args=dict(action_args),
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to_state=dict(state),
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confidence=0.5,
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)
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def uncertainty(self, state: dict[str, Any], action: str) -> UncertaintyInfo:
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return UncertaintyInfo(confidence=0.5, risk_level="medium", rationale="SimpleWorldModel placeholder")
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