Constructing Symbolic Representations for High-Level Planning

被引：0
|
作者：
Konidaris, George [1 ]
Kaelbling, Leslie Pack [1 ]
Lozano-Perez, Tomas [1 ]
机构：
[1] MIT, Comp Sci & Artificial Intelligence Lab, 32 Vassar St, Cambridge, MA 02139 USA
来源：
PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2014年
基金：
美国国家科学基金会;
关键词：
MOTION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We consider the problem of constructing a symbolic description of a continuous, low-level environment for use in planning. We show that symbols that can represent the preconditions and effects of an agent's actions are both necessary and sufficient for high-level planning. This eliminates the symbol design problem when a representation must be constructed in advance, and in principle enables an agent to autonomously learn its own symbolic representations. The resulting representation can be converted into PDDL, a canonical high-level planning representation that enables very fast planning.
引用
收藏
页码：1932 / +
页数：9
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