The M2M Pathfinding Algorithm Based on the Idea of Granular Computing

Macro-to-micro (M2M) model is an implementation model that inherits the GrC idea and extends it to some additional highly desirable characteristics. In this paper we introduce an effective pathfinding algorithm based on the M2M model. This algorithm takes O(n) time to preprocess, constructing the M2...

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Published in:Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02 Vol. 2; pp. 533 - 540
Main Authors: Wan, Haifeng, Zhang, Yingpeng, Luo, Shengzhou, Liu, Ruijie, Ye, Wensheng
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 15.09.2009
IEEE
Series:ACM Conferences
Subjects:
Computing methodologies > Artificial intelligence > Search methodologies
Computing methodologies > Artificial intelligence > Search methodologies > Discrete space search
Computing methodologies > Artificial intelligence > Search methodologies > Game tree search
Conferences
Costs
Data structures
Heuristic algorithms
Intelligent agent
Iterative algorithms
Mathematics of computing > Discrete mathematics > Graph theory > Paths and connectivity problems
Multiagent systems
Navigation
Power system modeling
Real time systems
Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Routing and network design problems
ISBN:0769538010, 9780769538013
Online Access:Get full text
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Abstract Macro-to-micro (M2M) model is an implementation model that inherits the GrC idea and extends it to some additional highly desirable characteristics. In this paper we introduce an effective pathfinding algorithm based on the M2M model. This algorithm takes O(n) time to preprocess, constructing the M2M data structure. Such hierarchical structure occupies O(n) bit memory space and can be updated in O(1) expected time to handle changes. Although the resulting path is not always the shortest one, it can make a trade-off between accuracy and time cost by adjusting a parameter - range value to satisfy various applications. At last, we will discuss the advantages of the M2M pathfinding algorithm (M2M-PF) and demonstrate the academic and applied prospect of M2M model.
AbstractList Macro-to-micro (M2M) model is an implementation model that inherits the GrC idea and extends it to some additional highly desirable characteristics. In this paper we introduce an effective pathfinding algorithm based on the M2M model. This algorithm takes O(n) time to preprocess, constructing the M2M data structure. Such hierarchical structure occupies O(n) bit memory space and can be updated in O(1) expected time to handle changes. Although the resulting path is not always the shortest one, it can make a trade-off between accuracy and time cost by adjusting a parameter - range value to satisfy various applications. At last, we will discuss the advantages of the M2M pathfinding algorithm (M2M-PF) and demonstrate the academic and applied prospect of M2M model.
Author Liu, Ruijie
Ye, Wensheng
Zhang, Yingpeng
Wan, Haifeng
Luo, Shengzhou
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Snippet Macro-to-micro (M2M) model is an implementation model that inherits the GrC idea and extends it to some additional highly desirable characteristics. In this...
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StartPage 533
SubjectTerms Computing methodologies -- Artificial intelligence -- Search methodologies
Computing methodologies -- Artificial intelligence -- Search methodologies -- Discrete space search
Computing methodologies -- Artificial intelligence -- Search methodologies -- Game tree search
Conferences
Costs
Data structures
Heuristic algorithms
Intelligent agent
Iterative algorithms
Mathematics of computing -- Discrete mathematics -- Graph theory -- Paths and connectivity problems
Multiagent systems
Navigation
Power system modeling
Real time systems
Theory of computation -- Design and analysis of algorithms -- Approximation algorithms analysis -- Routing and network design problems
Title The M2M Pathfinding Algorithm Based on the Idea of Granular Computing
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Volume 2
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