Dynamic macro-based heuristic planning through action relationship analysis

Zhuo Jiang, Junhao Wen, Jun Zeng, Yihao Zhang, Xibin Wang, Sachio Hirokawa

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The success of heuristic search in AI planning largely depends on the design of the heuristic. On the other hand, previous experience contains potential domain information that can assist the planning process. In this context, we have studied dynamic macro-based heuristic planning through action relationship analysis. We present an approach for analyzing the action relationship and design an algorithm that learns macros in solved cases. We then propose a dynamic macro-based heuristic that appropriately reuses the macros rather than immediately assigning them to domains. The above ideas are incorporated into a working planning system called Dynamic Macro-based Fast Forward planner. Finally, we evaluate our method in a series of experiments. Our method effectively optimizes planning since it reduces the result length by an average of 10% relative to the FF, in a time-economic manner. The efficiency is especially improved when invoking an action consumes time.

Original languageEnglish
Pages (from-to)363-371
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE98D
Issue number2
DOIs
Publication statusPublished - Feb 1 2015

Fingerprint

Macros
Planning
Dynamical systems
Economics
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Cite this

Dynamic macro-based heuristic planning through action relationship analysis. / Jiang, Zhuo; Wen, Junhao; Zeng, Jun; Zhang, Yihao; Wang, Xibin; Hirokawa, Sachio.

In: IEICE Transactions on Information and Systems, Vol. E98D, No. 2, 01.02.2015, p. 363-371.

Research output: Contribution to journalArticle

Jiang, Zhuo ; Wen, Junhao ; Zeng, Jun ; Zhang, Yihao ; Wang, Xibin ; Hirokawa, Sachio. / Dynamic macro-based heuristic planning through action relationship analysis. In: IEICE Transactions on Information and Systems. 2015 ; Vol. E98D, No. 2. pp. 363-371.
@article{dd500f41c2924fdd8462ded21bc0342d,
title = "Dynamic macro-based heuristic planning through action relationship analysis",
abstract = "The success of heuristic search in AI planning largely depends on the design of the heuristic. On the other hand, previous experience contains potential domain information that can assist the planning process. In this context, we have studied dynamic macro-based heuristic planning through action relationship analysis. We present an approach for analyzing the action relationship and design an algorithm that learns macros in solved cases. We then propose a dynamic macro-based heuristic that appropriately reuses the macros rather than immediately assigning them to domains. The above ideas are incorporated into a working planning system called Dynamic Macro-based Fast Forward planner. Finally, we evaluate our method in a series of experiments. Our method effectively optimizes planning since it reduces the result length by an average of 10{\%} relative to the FF, in a time-economic manner. The efficiency is especially improved when invoking an action consumes time.",
author = "Zhuo Jiang and Junhao Wen and Jun Zeng and Yihao Zhang and Xibin Wang and Sachio Hirokawa",
year = "2015",
month = "2",
day = "1",
doi = "10.1587/transinf.2014EDP7170",
language = "English",
volume = "E98D",
pages = "363--371",
journal = "IEICE Transactions on Information and Systems",
issn = "0916-8532",
publisher = "一般社団法人電子情報通信学会",
number = "2",

}

TY - JOUR

T1 - Dynamic macro-based heuristic planning through action relationship analysis

AU - Jiang, Zhuo

AU - Wen, Junhao

AU - Zeng, Jun

AU - Zhang, Yihao

AU - Wang, Xibin

AU - Hirokawa, Sachio

PY - 2015/2/1

Y1 - 2015/2/1

N2 - The success of heuristic search in AI planning largely depends on the design of the heuristic. On the other hand, previous experience contains potential domain information that can assist the planning process. In this context, we have studied dynamic macro-based heuristic planning through action relationship analysis. We present an approach for analyzing the action relationship and design an algorithm that learns macros in solved cases. We then propose a dynamic macro-based heuristic that appropriately reuses the macros rather than immediately assigning them to domains. The above ideas are incorporated into a working planning system called Dynamic Macro-based Fast Forward planner. Finally, we evaluate our method in a series of experiments. Our method effectively optimizes planning since it reduces the result length by an average of 10% relative to the FF, in a time-economic manner. The efficiency is especially improved when invoking an action consumes time.

AB - The success of heuristic search in AI planning largely depends on the design of the heuristic. On the other hand, previous experience contains potential domain information that can assist the planning process. In this context, we have studied dynamic macro-based heuristic planning through action relationship analysis. We present an approach for analyzing the action relationship and design an algorithm that learns macros in solved cases. We then propose a dynamic macro-based heuristic that appropriately reuses the macros rather than immediately assigning them to domains. The above ideas are incorporated into a working planning system called Dynamic Macro-based Fast Forward planner. Finally, we evaluate our method in a series of experiments. Our method effectively optimizes planning since it reduces the result length by an average of 10% relative to the FF, in a time-economic manner. The efficiency is especially improved when invoking an action consumes time.

UR - http://www.scopus.com/inward/record.url?scp=84922359237&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84922359237&partnerID=8YFLogxK

U2 - 10.1587/transinf.2014EDP7170

DO - 10.1587/transinf.2014EDP7170

M3 - Article

AN - SCOPUS:84922359237

VL - E98D

SP - 363

EP - 371

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

SN - 0916-8532

IS - 2

ER -