Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad-Trees
Joint Authors
Yin, Quanjun
Qin, Long
Sun, Lin
Hu, Yue
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-19
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
In the context of robot navigation, game AI, and so on, real-time search is extensively used to undertake motion planning.
Though it satisfies the requirement of quick response to users’ commands and environmental changes, learning real-time search (LRTS) suffers from the heuristic depressions where agents behave irrationally.
There have introduced several effective solutions, such as state abstractions.
This paper combines LRTS and encoded quad-tree abstraction which represent the search space in multiresolutions.
When exploring the environments, agents are enabled to locally repair the quad-tree models and incrementally refine the spatial cognition.
By virtue of the idea of state aggregation and heuristic generalization, our EQ LRTS (encoded quad-tree based LRTS) possesses the ability of quickly escaping from heuristic depressions with less state revisitations.
Experiments and analysis show that (a) our encoding principle for quad-trees is a much more memory-efficient method than other data structures expressing quad-trees, (b) EQ LRTS differs a lot in several characteristics from classical PR LRTS which represent the space and refine the paths hierarchically, and (c) EQ LRTS substantially reduces the planning amount and curtails heuristic updates compared with LRTS on uniform cells.
American Psychological Association (APA)
Hu, Yue& Qin, Long& Yin, Quanjun& Sun, Lin. 2017. Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad-Trees. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1189673
Modern Language Association (MLA)
Hu, Yue…[et al.]. Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad-Trees. Mathematical Problems in Engineering No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1189673
American Medical Association (AMA)
Hu, Yue& Qin, Long& Yin, Quanjun& Sun, Lin. Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad-Trees. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1189673
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189673