Model-Lite Case-Based Planning
  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.
