Model-Lite Case-Based Planning

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 to a state-of-the-art case-based planner that depends on complete domain models.