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name: goal_effect_induction description: Induce separated goal and effect rules from behavioral cloning traces while logging training evidence.

goal_effect_induction

Use this skill when recovering Bain and Sammut style behavioural cloning without the original simulator repository. It is not a general reinforcement-learning skill; it targets trace-driven reactive cloning with explicit GRAIL-style separation.

Inputs

• JSON-compatible trace examples or prepared examples, depending on the script.
• Current-attempt artifacts only; do not read any original source repository.

Outputs

• Deterministic JSON-compatible structures that can be consumed by downstream recovery modules.

Workflow

1. Induce threshold goal rules from state variables.
2. Induce effect rules that map goal error to elevator actions.
3. Optionally run a tiny scalar optimizer step for reduced recovery evidence.

Validation

Run python scripts/goal_effect_induction.py --help where applicable and run the tests through the Distiller skill-tree validator with --run-tests.

Limitations

The scripts implement a reduced proxy for the paper mechanism. They validate trace preparation, GRAIL separation, compactness, and reactive action prediction; they do not recreate the unavailable flight simulator.