Model-Lite Case-Based Planning
Hankz Hankui Zhuo, Subbarao Kambhampati, and Tuan Nguyen

TL;DR
This paper introduces a novel case-based planning approach that combines incomplete domain models with a library of verified plans, reducing the need for complete domain specifications and improving planning in real-world scenarios.
Contribution
It proposes a new method that integrates generative planning on incomplete models with case libraries, enabling effective planning without full domain models.
Findings
Outperforms state-of-the-art case-based planners with complete models.
Effectively augments skeletal plans with relevant plan fragments.
Demonstrates practical applicability in real-world planning domains.
Abstract
There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider a novel solution for this challenge that combines generative planning on incomplete domain models with a library of plan cases that are known to be correct. While this was arguably the original motivation for case-based planning, most existing case-based planners assume (and depend on) from-scratch planners that work on complete domain models. In contrast, our approach views the plan generated with respect to the incomplete model as a "skeletal plan" and augments it with directed mining of plan fragments from library cases. We will present the details of our approach and present an empirical evaluation of our method in comparison…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
