---
title: "Refining Incomplete Planning Domain Models Through Plan Traces"
authors:
  - "Hankz Hankui Zhuo"
  - "Tuan Anh Nguyen"
  - "Subbarao Kambhampati"
doi: ""
url: "https://ijcai.org/proceedings/2013/360"
pdf: "https://ijcai.org/proceedings/2013/0360.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "2451-2457"
track: ""
keywords:
---

# Refining Incomplete Planning Domain Models Through Plan Traces

Hankz Hankui Zhuo, Tuan Anh Nguyen, Subbarao Kambhampati

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 2451-2457.

## Abstract

Most existing work on learning planning models assumes that the entire model needs to be learned from scratch. A more realistic situation is that the planning agent has an incomplete model which it needs to refine through learning. In this paper we propose and evaluate a method for doing this. Our method takes as input an incomplete model (with missing preconditions and effects in the actions), as well as a set of plan traces that are known to be correct. It outputs a refined model that not only captures additional precondition/effect knowledge about the given actions, but also macro actions. We use a MAX-SAT framework for learning, where the constraints are derived from the executability of the given plan traces, as well as the preconditions/effects of the given incomplete model. Unlike traditional macro-action learners which use macros to increase the efficiency of planning (in the context of a complete model), our motivation for learning macros is to increase the accuracy (robustness) of the plans generated with the refined model. We demonstrate the effectiveness of our approach through a systematic empirical evaluation.