---
title: "Efficient Black-Box Planning Using Macro-Actions with Focused Effects"
authors:
  - "Cameron Allen"
  - "Michael Katz"
  - "Tim Klinger"
  - "George Konidaris"
  - "Matthew Riemer"
  - "Gerald Tesauro"
doi: "10.24963/ijcai.2021/554"
url: "https://ijcai.org/proceedings/2021/554"
pdf: "https://ijcai.org/proceedings/2021/0554.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4024-4031"
track: "Main Track"
keywords:
  - "Planning and Scheduling: Planning and Scheduling"
  - "Planning and Scheduling: Search in Planning and Scheduling"
  - "Heuristic Search and Game Playing: Heuristic Search"
---

# Efficient Black-Box Planning Using Macro-Actions with Focused Effects

Cameron Allen, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4024-4031. DOI: [10.24963/ijcai.2021/554](https://doi.org/10.24963/ijcai.2021/554)

## Abstract

The difficulty of deterministic planning increases exponentially with search-tree depth. Black-box planning presents an even greater challenge, since planners must operate without an explicit model of the domain. Heuristics can make search more efficient, but goal-aware heuristics for black-box planning usually rely on goal counting, which is often quite uninformative. In this work, we show how to overcome this limitation by discovering macro-actions that make the goal-count heuristic more accurate. Our approach searches for macro-actions with focused effects (i.e. macros that modify only a small number of state variables), which align well with the assumptions made by the goal-count heuristic. Focused macros dramatically improve black-box planning efficiency across a wide range of planning domains, sometimes beating even state-of-the-art planners with access to a full domain model.