---
title: "Prioritized Goal Decomposition of Markov Decision Processes: Toward a Synthesis of Classical and Decision Theoretic Planning"
authors:
  - "Craig Boutilier"
  - "Ronen I. Brafman"
  - "Christopher W. Geib"
doi: ""
url: "https://ijcai.org/proceedings/1997/167"
pdf: "https://ijcai.org/proceedings/1997/0167.pdf"
venue: "Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence"
year: 1997
pages: "1156-1161"
track: "Planning and Scheduling"
keywords:
---

# Prioritized Goal Decomposition of Markov Decision Processes: Toward a Synthesis of Classical and Decision Theoretic Planning

Craig Boutilier, Ronen I. Brafman, Christopher W. Geib

*Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence*, pages 1156-1161.

## Abstract

We describe an approach to goal decomposition for a certain class of Markov decision processes (MOPs) . An abstraction mechanism is used to generate abstract MOPs associated with different objectives, and several methods for merging the policies for these different objectives are considered. In one technique, causal (least-commitment) structures are generated for abstract policies and plan merg ing techniques, exploiting the relaxation of policy commitments reflected in this structure, are used to piece the results into a single policy. Abstract value functions provide guidance if plan repair is needed. This work makes some first steps toward the synthesis of classical and decision theoretic planning methods .