MONTE-CARLO TREE SEARCH WITH HEURISTIC KNOWLEDGE: A NOVEL WAY IN SOLVING CAPTURING AND LIFE AND DEATH PROBLEMS IN GO

Search for this publication on Google Scholar
Zhang, P. (2010). MONTE-CARLO TREE SEARCH WITH HEURISTIC KNOWLEDGE: A NOVEL WAY IN SOLVING CAPTURING AND LIFE AND DEATH PROBLEMS IN GO. Unc Charlotte Electronic Theses And Dissertations.
Download PDF

Analytics

96 views ◎
62 downloads ⇓


Abstract

Monte-Carlo (MC) tree search is a new research field. Its effectiveness in searching large state spaces, such as the Go game tree, is well recognized in the computer Go community. Go domain-specific heuristics and techniques as well as domain-independent heuristics and techniques are systematically investigated in the context of the MC tree search in this dissertation. The search extensions based on these heuristics and techniques can significantly improve the effectiveness and efficiency of the MC tree search.Two major areas of investigation are addressed in this dissertation research: I. The identification and use of the effective heuristic knowledge in guiding the MC simulations, II. The extension of the MC tree search algorithm with heuristics. Go, the most challenging board game to the machine, serves as the test bed. The effectiveness of the MC tree search extensions is demonstrated through the performances of Go tactic problem solvers using these techniques.The main contributions of this dissertation include:1. A heuristics based Monte-Carlo tactic tree search framework is proposed to extend the standard Monte-Carlo tree search.2. Knowledge based heuristics are systematically investigated to improve the Monte-Carlo tactic tree search.3. Pattern learning is demonstrated as effective in improving the Monte-Carlo tactic tree search.4. Domain knowledge independent tree search enhancements are shown as effective in improving the Monte-Carlo tactic tree search performances.5. A strong Go Tactic solver based on proposed algorithms outperforms traditional game tree search algorithms.The techniques developed in this dissertation research can benefit other game domains and application fields.

Details
Author

Zhang, Peigang

Title

MONTE-CARLO TREE SEARCH WITH HEURISTIC KNOWLEDGE: A NOVEL WAY IN SOLVING CAPTURING AND LIFE AND DEATH PROBLEMS IN GO

Physical Description

1 online resource (88 pages) : PDF

Date

2010

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Monte-Carlo (MC) tree search is a new research field. Its effectiveness in searching large state spaces, such as the Go game tree, is well recognized in the computer Go community. Go domain-specific heuristics and techniques as well as domain-independent heuristics and techniques are systematically investigated in the context of the MC tree search in this dissertation. The search extensions based on these heuristics and techniques can significantly improve the effectiveness and efficiency of the MC tree search.Two major areas of investigation are addressed in this dissertation research: I. The identification and use of the effective heuristic knowledge in guiding the MC simulations, II. The extension of the MC tree search algorithm with heuristics. Go, the most challenging board game to the machine, serves as the test bed. The effectiveness of the MC tree search extensions is demonstrated through the performances of Go tactic problem solvers using these techniques.The main contributions of this dissertation include:1. A heuristics based Monte-Carlo tactic tree search framework is proposed to extend the standard Monte-Carlo tree search.2. Knowledge based heuristics are systematically investigated to improve the Monte-Carlo tactic tree search.3. Pattern learning is demonstrated as effective in improving the Monte-Carlo tactic tree search.4. Domain knowledge independent tree search enhancements are shown as effective in improving the Monte-Carlo tactic tree search performances.5. A strong Go Tactic solver based on proposed algorithms outperforms traditional game tree search algorithms.The techniques developed in this dissertation research can benefit other game domains and application fields.

Genre

doctoral dissertations

Subjects--Topics

Computer science
Artificial intelligence

Degree

Ph.D.

Keywords

Capturing
Computer Go
Heuristic Knowledge
Life and Death
Monte-Carlo Tree Search
Tactic Search

Subject Area

Information Technology

Advisor(s)

Chen, Keh-Hsun

Committee Members

Xiao, Jing
Ras, Zbigniew
Wu, Xintao
Dmochowski, Jacek

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2010.

Rights Statement

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). For additional information, see http://rightsstatements.org/page/InC/1.0/.

Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Zhang_uncc_0694D_10090

Permalink

http://hdl.handle.net/20.500.13093/etd:1585