IEICE TRANSACTIONS on Information
Dynamic Macro-Based Heuristic Planning through Action Relationship Analysis
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.2 pp.363-371
- Publication Date
- 2015/02/01
- Publicized
- 2014/10/23
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7170
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Zhuo JIANG
Chongqing University
Junhao WEN
Chongqing University
Jun ZENG
Chongqing University
Yihao ZHANG
Chongqing University
Xibin WANG
Chongqing University
Sachio HIROKAWA
Kyushu University
Keyword
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Zhuo JIANG, Junhao WEN, Jun ZENG, Yihao ZHANG, Xibin WANG, Sachio HIROKAWA, "Dynamic Macro-Based Heuristic Planning through Action Relationship Analysis" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 2, pp. 363-371, February 2015, doi: 10.1587/transinf.2014EDP7170.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7170/_p
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@ARTICLE{e98-d_2_363,
author={Zhuo JIANG, Junhao WEN, Jun ZENG, Yihao ZHANG, Xibin WANG, Sachio HIROKAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Dynamic Macro-Based Heuristic Planning through Action Relationship Analysis},
year={2015},
volume={E98-D},
number={2},
pages={363-371},
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.},
keywords={},
doi={10.1587/transinf.2014EDP7170},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Dynamic Macro-Based Heuristic Planning through Action Relationship Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 363
EP - 371
AU - Zhuo JIANG
AU - Junhao WEN
AU - Jun ZENG
AU - Yihao ZHANG
AU - Xibin WANG
AU - Sachio HIROKAWA
PY - 2015
DO - 10.1587/transinf.2014EDP7170
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2015
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.
ER -
English
