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Choice → Consequence → Learning: - ConsequenceEngine tracks every decision point with alternatives, risk/reward estimates, and actual outcomes - Consequences feed into AdaptiveEthics for experience-based learning - FusionAGILoop now wires ethics + consequences into task lifecycle Causal World Model: - CausalWorldModel learns state-transition patterns from execution history - Predicts outcomes based on observed action→effect patterns - Uncertainty estimates decrease as more evidence accumulates Metacognition: - assess_head_outputs() evaluates reasoning quality from head outputs - Detects knowledge gaps, measures head agreement, identifies uncertainty - Actively recommends whether to seek more information Interpretability: - ReasoningTracer captures full prompt→answer reasoning traces - Each step records stage, component, input/output, timing - explain() generates human-readable reasoning explanations Claim Verification: - ClaimVerifier cross-checks claims for evidence, consistency, grounding - Flags high-confidence claims lacking evidence support - Detects contradictions between claims from different heads 325 tests passing, 0 ruff errors, 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
"""Tests for the causal world model."""
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from fusionagi.world_model import CausalWorldModel
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class TestCausalWorldModel:
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"""Test learned causal state-transition prediction."""
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def test_predict_unknown_action(self) -> None:
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wm = CausalWorldModel()
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result = wm.predict({"x": 1}, "unknown", {})
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assert result.confidence == 0.3
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assert result.to_state == {"x": 1}
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def test_observe_and_predict(self) -> None:
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wm = CausalWorldModel()
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wm.observe(
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from_state={"count": 0},
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action="increment",
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action_args={},
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to_state={"count": 1},
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success=True,
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)
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result = wm.predict({"count": 5}, "increment", {})
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assert result.confidence > 0.3
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assert "count" in result.to_state
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def test_multiple_observations_increase_confidence(self) -> None:
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wm = CausalWorldModel()
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for i in range(10):
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wm.observe({"s": i}, "act", {}, {"s": i + 1}, success=True)
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result = wm.predict({"s": 100}, "act", {})
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assert result.confidence > 0.7
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def test_uncertainty_no_observations(self) -> None:
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wm = CausalWorldModel()
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info = wm.uncertainty({}, "unknown_action")
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assert info.risk_level == "high"
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assert info.confidence == 0.3
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def test_uncertainty_with_observations(self) -> None:
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wm = CausalWorldModel()
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for i in range(10):
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wm.observe({}, "safe_action", {}, {}, success=True)
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info = wm.uncertainty({}, "safe_action")
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assert info.risk_level in ("low", "medium")
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assert info.confidence > 0.5
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def test_failed_observations_lower_confidence(self) -> None:
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wm = CausalWorldModel()
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for i in range(5):
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wm.observe({}, "risky", {}, {}, success=False)
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info = wm.uncertainty({}, "risky")
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assert info.risk_level == "high"
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def test_known_actions(self) -> None:
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wm = CausalWorldModel()
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wm.observe({}, "act_a", {}, {}, success=True)
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wm.observe({}, "act_b", {}, {}, success=True)
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assert "act_a" in wm.known_actions
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assert "act_b" in wm.known_actions
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def test_get_summary(self) -> None:
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wm = CausalWorldModel()
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wm.observe({}, "x", {}, {"result": 1}, success=True)
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wm.observe({}, "x", {}, {"result": 2}, success=True)
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summary = wm.get_summary()
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assert summary["total_observations"] == 2
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assert summary["known_patterns"] >= 1
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