On Learning Action Costs from Input Plans

• URL: http://arxiv.org/abs/2408.10889v2
• Date: Mon, 2 Sep 2024 09:48:43 GMT
• Title: On Learning Action Costs from Input Plans
• Authors: Marianela Morales, Alberto Pozanco, Giuseppe Canonaco, Sriram Gopalakrishnan, Daniel Borrajo, Manuela Veloso,
• Abstract summary: We introduce a new problem: that of learning the costs of a set of actions such that a set of input plans are optimal under the resulting planning model. We present $LACFIPk$, an algorithm to learn action's costs from unlabeled input plans.
• Score: 8.68471096727195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most of the work on learning action models focus on learning the actions' dynamics from input plans. This allows us to specify the valid plans of a planning task. However, very little work focuses on learning action costs, which in turn allows us to rank the different plans. In this paper we introduce a new problem: that of learning the costs of a set of actions such that a set of input plans are optimal under the resulting planning model. To solve this problem we present $LACFIP^k$, an algorithm to learn action's costs from unlabeled input plans. We provide theoretical and empirical results showing how $LACFIP^k$ can successfully solve this task.

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