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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>
12 lines
430 B
Python
12 lines
430 B
Python
"""World model and simulation for AGI.
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Provides causal state-transition prediction from learned execution history,
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rollout simulation, and uncertainty estimation.
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"""
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from fusionagi.world_model.base import SimpleWorldModel, WorldModel
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from fusionagi.world_model.causal import CausalWorldModel
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from fusionagi.world_model.rollout import run_rollout
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__all__ = ["CausalWorldModel", "SimpleWorldModel", "WorldModel", "run_rollout"]
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