Hierarchical, Symbolic, Language, and Multi-Agent Planning › Worked example: language-conditioned goals

def ground_instruction(text, state):
    """Map fixed token patterns to goals; state appears only in error text."""
    punctuation = str.maketrans("", "", ".,!?;:")
    tokens = set(text.lower().translate(punctuation).split())
    if tokens & {"not", "never", "don't", "dont"}:
        raise ValueError("Negated instructions are outside this toy grammar.")
    if {"key", "goal"}.issubset(tokens) and tokens & {"get", "reach"}:
        return frozenset({"has_key", "at_goal"})
    if "key" in tokens and tokens & {"get", "take"}:
        return frozenset({"has_key"})
    if "door" in tokens and tokens & {"unlock", "open"}:
        return frozenset({"door_open"})
    if "goal" in tokens and tokens & {"reach", "go"}:
        return frozenset({"at_goal"})
    raise ValueError(f"Could not ground instruction in state {state}: {text!r}")


INSTRUCTIONS = [
    "Get the key.",
    "Unlock the door.",
    "Reach the goal.",
    "Get the key, then reach the goal.",
]
grounded_goals = {
    text: ground_instruction(text, INITIAL_ENV) for text in INSTRUCTIONS
}

rejected = []
for text in ["Open the window.", "Unlock the phone.", "Do not open the door."]:
    try:
        ground_instruction(text, INITIAL_ENV)
    except ValueError:
        rejected.append(text)
assert len(rejected) == 3
assert ground_instruction("Get the key.", INITIAL_ENV) == ground_instruction(
    "Get the key.", manual_final
)