The GRT planning system: Backward heuristic construction in forward state-space planning

被引:0
|
作者
Refanidis, Ioannis [1 ]
Vlahavas, Ioannis [1 ]
机构
[1] Aristotle University, Dept. of Informatics, 54006 Thessaloniki, Greece
关键词
Competition - Computational complexity - Problem solving;
D O I
暂无
中图分类号
学科分类号
摘要
This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, it estimates the distance between each fact and the goals of the problem, in a backward direction. Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on a best-first basis. The paper presents the benefits from the adoption of opposite directions between the preprocessing and the search phases, discusses some difficulties that arise in the pre-processing phase and introduces techniques to cope with them. Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem. Finally, a method of overcoming local optimal states, based on domain axioms, is proposed. According to it, difficult problems are decomposed into easier sub-problems that have to be solved sequentially. The performance results from various domains, including those of the recent planning competitions, show that GRT is among the fastest planners. © 2001 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.
引用
收藏
页码:115 / 161
相关论文
共 50 条
  • [31] Forward looking SAR imaging with the state-space model
    Zhao Guanhua
    Fu Yaowen
    Li Xiang
    PROCEEDINGS OF 2012 IEEE 11TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP) VOLS 1-3, 2012, : 1773 - 1778
  • [32] Measure-adaptive state-space construction
    Obal, WD
    Sanders, WH
    PERFORMANCE EVALUATION, 2001, 44 (1-4) : 237 - 258
  • [33] A heuristic state-space approach to the functional design of mechanical systems
    Zhang, WY
    Tor, SB
    Britton, GA
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2002, 19 (04): : 235 - 244
  • [34] Heuristic allocation based on a dynamic programming state-space representation
    Dragut, AB
    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2002, 140 (1-2) : 257 - 273
  • [35] Heuristic mission planning approach for deep space explorer
    Institute of Deep Space Exploration Technology, Beijing Institute of Technology, Beijing
    100081, China
    不详
    100081, China
    Yuhang Xuebao, 5 (496-503):
  • [36] Autonomous state-space construction in pomdp with continuous observation space
    Inoue, K
    Ota, J
    Arai, T
    INTELLIGENT AUTONOMOUS VEHICLES 2001, 2002, : 245 - 250
  • [37] Interpretable State-Space Model of Urban Dynamics for Human-Machine Collaborative Transportation Planning
    Yu, Jiangbo
    Hyland, Michael F.
    TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2025, 192
  • [38] Online Gaussian Process State-space Model: Learning and Planning for Partially Observable Dynamical Systems
    Soon-Seo Park
    Young-Jin Park
    Youngjae Min
    Han-Lim Choi
    International Journal of Control, Automation and Systems, 2022, 20 : 601 - 617
  • [39] Online Gaussian Process State-space Model: Learning and Planning for Partially Observable Dynamical Systems
    Park, Soon-Seo
    Park, Young-Jin
    Min, Youngjae
    Choi, Han-Lim
    INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2022, 20 (02) : 601 - 617
  • [40] Forward and backward chain-code representation for motion planning of cars
    Shih, FY
    Wu, YT
    Chen, BLC
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2004, 18 (08) : 1437 - 1451