ABIL uses abductive reasoning with a supplied knowledge base to learn symbolic perception from raw observations and then trains task-specific behavior policies, yielding better data efficiency and generalization in long-horizon planning tasks.
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Learning for Long-Horizon Planning via Neuro-Symbolic Abductive Imitation
ABIL uses abductive reasoning with a supplied knowledge base to learn symbolic perception from raw observations and then trains task-specific behavior policies, yielding better data efficiency and generalization in long-horizon planning tasks.