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 journalArticlepeer-review

    2 Citations (Scopus)


    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
    Issue number2
    Publication statusPublished - Feb 1 2015

    All Science Journal Classification (ASJC) codes

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


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