Action Model Learning with Guarantees
CoRR(2024)
Abstract
This paper studies the problem of action model learning with full
observability. Following the learning by search paradigm by Mitchell, we
develop a theory for action model learning based on version spaces that
interprets the task as search for hypothesis that are consistent with the
learning examples. Our theoretical findings are instantiated in an online
algorithm that maintains a compact representation of all solutions of the
problem. Among these range of solutions, we bring attention to actions models
approximating the actual transition system from below (sound models) and from
above (complete models). We show how to manipulate the output of our learning
algorithm to build deterministic and non-deterministic formulations of the
sound and complete models and prove that, given enough examples, both
formulations converge into the very same true model. Our experiments reveal
their usefulness over a range of planning domains.
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