A Desktop Grid Computing Service for Connect6

Size: px
Start display at page:

Download "A Desktop Grid Computing Service for Connect6"

Transcription

1 A Desktop Grid Computing Service for Connect6 I-Chen Wu*, Chingping Chen*, Ping-Hung Lin*, Kuo-Chan Huang**, Lung- Ping Chen***, Der-Johng Sun* and Hsin-Yun Tsou* *Department of Computer Science, National Chiao Tung University, Taiwan **Department of Computer and Information Science, National Taichung University, Taiwan *Department of Computer Science, Dong Hai University, Taiwan April 2009

2 Outline Background Goals Architecture Experiment Conclusion

3 Background Connect6 NCTU6 Connect6Lib A Connect6 Editor

4 Connect6 [Wu and Huang, 2005] presented the first article about Connect6. Features of Connect6 Simplicity of Rules Fairness High Game Complexity Tournaments Connect6 Editor Connect6Lib Connect6 Program NCTU6

5 Simplicity of Rules Black (first) places one stone. Both alternately places two stones. The one who connects up to six wins.

6 Fairness Key: Balanced One player always has one more stone than the other after making each move. 1.B: #1, W: #0 2.B: #1, W: #2 3.B: #3, W: #2 4.B: #3, W: #4 5.B: #5, W: #4 6.B: #5, W: #6 7.B: #7, W: #6

7 High Game Tree Complexity Game-Tree Complexity: [Herik, et al. 2002] [Wu and Huang, 2005] 1. Go: Shogi: Chinese Chess: Connect6: ~ Between Shogi & Chinese Chess. 5. Chess 6.

8 Connect6 Tournaments Tournaments: Human vs. Human NCTU-Cup Connect6 Open Tournaments (since 2006) Promote-to-Dan Connect6 Contests (since 2007) Russia Connect6 Open Tournament (since 2007) World Connect6 Open Tournament (since 2007) Computer vs. Computer: Computer Olympiad (since 2006) Chinese Computer Games Conferences-CCGC (since 2007) Computer vs. Human Man-Machine Connect6 Contest (since 2008)

9 NCTU6 A Connect6 Program A program developed by the team at NCTU. led by I-Chen Wu. Records: 2006: The 11th Computer Olympiad Won the gold 2008: The 13th Computer Olympiad Won the gold 2007: Go Champion Jun-Shing Chou ( 棋王周俊勳 ) NCTU6 won Chou. 2008: Go Champion Jun-Shing Chou ( 棋王周俊勳 ) NCTU6 won Chou again. 2008: The First Annual Man-Machine Connect6 Contest Won 11 among 12 games against top Connect6 players in Taiwan

10 NCTU6 Play Move Path Move Messages

11 NCTU6-Verifier Modified from NCTU6 [Wu and Lin,2009] Verify whether one player wins in a position. If not winning, Find the defensive moves. Listed as (11,11)(5,5), etc as the right Figure. Verifier Defensive moves

12 Connect6Lib An Connect6 Editor Modified from RenLib by [C.P. Chen 2009]. RenLib is an editor for Renju (professional version of five-in-a-row).

13 Connect6Lib An Connect6 Editor Features: Move Tree Comments Programs Hint Comment Hints Programs Move Tree

14 More Openings for Connect6 In order to promote Connect6, Need to develop more openings and puzzles. Reason: Lack of openings and puzzles, since Connect6 is a very young game. But, how? Have some Connect6 experts: Use NCTU6 to generate good moves. Use NCTU6-Verifer to generate all defensive moves. Implement an automatic opening generating system.

15 Integration of Connect6Lib, NCTU6 and NCTU6-Verifier Add NCTU6 into Connect6Lib. Add NCTU6-Verifier into Connect6Lib. Problem: Both takes huge amount of times. NCTU6: takes about 60 sec per move. NCTU6-Verifier: takes 1sec ~ 10hr per verification.

16 NCTU6 in Connect6Lib 1 Click ab button 2 Position: ;B[JJ];W[JH];W[JL] ;B[KK];B[II];W[HH] ;W[IK] NCTU6 Return good moves: ;B[KM];B[LL] ;B[KM];B[KJ] ;B[KJ];B[HJ] 3 4 Generate these moves.

17 Generate defensive moves base on results NCTU6-Verifier in Connect6Lib 1 click cw button 2 Position format ;B[JJ];W[JH];W[JL] ;B[KK];B[II];W[HH] ;W[IK];B[KM];B[LL] 4 Results for all defensive moves: ;W[MM];B[KI] ;B[MM];B[NJ] ;B[NN];B[NJ] ;B[NN];B[KN] 3 NCTU6- Verifier

18 Outline Background Goals Architecture Experiment Conclusion

19 Our Goals Parallelize jobs NCTU6 (takes about 1 min.) NCTU6-Verifier. (takes about 1 sec to 10 hr.) on idle desktops. Why not use the idle cycles of all desktops to help Connect6 experts speed up. For example, while finding all defensive moves, try some good moves in other positions.

20 Parallelization of A scenario: Root Prove NCTU6-Verifier we win in Root. Parallelization NCTU6 NCTU6-Verifier NCTU6 NCTU6-Verifier

21 Job Priority Jobs: NCTU6-Verifier NCTU6 Job priority: Experts or opening generator use priorities to determine which job to run next. For example, For squares generated by NCTU6, choose the one with high chances to win. For circles generated by NCTU6-Verifier, choose the one with high chances not to win.

22 Abortion (Pruning) Abortion or called Pruning in Game Tree Search. A square wins other squares are aborted. A circle loses other circles are aborted. Root

23 Abortion (Pruning) NCTU6-Verifier: verify whether we win. Generate defensive moves by opponents. NCTU6: Generate our good moves. Root

24 Abortion (Pruning) Root Our good moves

25 Abortion (Pruning) Root NCTU6-Verifier: verify whether we win. Generate defensive moves by opponents.

26 Abortion (Pruning) Case1 Root Win

27 Abortion (Pruning) Case2 Root LoseLose Lose

28 Abortion (Pruning) Case2 Root Lose LoseLose Lose

29 Features of the Parallelism Highly dynamic Abortion and Priorities may greatly change the result including performance. There could be more than one winning move. Anomalous Speedups. [?] Similar to parallel alpha-beta search.

30 The Requirements of Desktop Grid Need fast response times. Reasons: Experts sometimes want to see the results as fast as possible. A result may cause huge pruning. Use push technology Use pull technology in most traditional desktops. Prefer to connection-oriented lines, such as TCP. Penetrate Firewalls/NATs. Some workers in our system may be inside Firewalls/NATs. Try not to overlap two jobs in a one-core desktop. Simply slow down the response time.

31 Outline Background Goals Architecture Experiment Conclusion

32 Current Architecture Our Desktop Grid System Job Result Result Connect6Lib is connected to desktops Result Job Job

33 Finite State Machine for a Job Put in waiting queue Dispatch to worker Finish Initial Waiting Running End Worker error occur User abort Abort User abort

34 TCP/IP communication Process communication Connect6Lib Create Request Info or Full Worker Monitor Server Manager Job Request Job Working Keep-Alive Messages Job Results Fork Process Daemon Fork Results Verifier Keep-Alive Messages Job end End

35 Current Policy Our Desktop Grid System As long as some desktops are free, dispatch jobs to them, even if these jobs have low priorities.

36 Potential Problem 1 NAT Penetration Our Desktop Grid System? NAT NAT port mapping is required.

37 Potential Problem 2 Job Overlapped Connect6Lib A Our Desktop Grid System Job Overlapped: Not the best scheduling. Job Connect6Lib B

38 Solution for These Potential Problems Connect6Lib A Our Desktop Grid System Dynamically dispatch jobs to desktops Allocated for A Broker Job Allocated for B Job Connect6Lib B

39 Solution for These Potential Problems Connect6Lib A Our Desktop Grid System Dynamically dispatch jobs to desktops Broker Job Allocated for A Job Allocated for B Connect6Lib B B only needs one job.

40 Ongoing Projects Centralize job distribution. Solve the potential problems. Decentralize job distribution. Make it scalable. Automate the job distribution of Connect6 jobs Use some algorithms like Proof Number Search. Distribute jobs without Connect6 experts.

41 Centralize job distribution User User User Broker Monitor Monitor Monitor Worker Worker Worker

42 Decentralize job distribution User User User Broker Broker Monitor Monitor Monitor Worker Worker Worker

43 Outline Background Goals Architecture Experiment Conclusion

44 Experiments A winning position for Black Used to test our desktop grid system.

45 Data for the Case #Cores: 28 # Jobs: #NCTU #NCTU6-Verifier 1220 #Aborted job 247 Averaged time per job: sec Average for NCTU sec Average for NCTU6-Verifier sec The time for the fastest job: sec The time for the slowest job: 19 hr, 42 min, 5 sec. Aggregate time: Total time: 584 hr, 35min, 51sec. 23 hr, 49min, 11sec

46 Outline Background Goals Architecture Experiment Conclusion

47 Conclusion In this paper, we are proposing a desktop grid system for Connect6 applications. Use push technology instead of pull technology as traditional desktop grid. Use broker to coordinate workers and clients. Currently, the implementation of broker is ongoing. Heavily use priorities as scheduling policy. Especially, avoid from overlapping two jobs in one core. Currently, this system already demonstrates the feasibility to speed the verification up greatly. This type of desktop grid systems can be applied to many other computation-bound AI applications.

48 References [1]Renlib, [2] BOINC, [3] XtremWeb, [4] Foster, I., Kesselman, C., The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann Publishers, Inc., [5] The globus project, [6] Global Grid Forum, [7] Condor, [8] Volunteer at Home, [9] I-Chen Wu and Ping-Hung, Lin, "NCTU6-Lite Wins Connect6 Tournament", to appear in ICGA Journal (SCI), [10] I-Chen Wu and Shi-Jim, Yen, "X6 Wins Tournament", ICGA Journal, September [11] I-Chen Wu and Shi-Jim, Yen, "NCTU6 Wins Connect6 Tournament", ICGA Journal, September [12] I-Chen Wu and Ping-Hung Lin, Threat-based Proof Search For Connect Games, in preparation, [13] I-Chen Wu, Dei-Yen Huang and Hsiu-Chen Chang, "Connect6", ICGA Journal, Vol. 28, No. 4, pp , December [14] I-Chen Wu, and Dei-Yen Huang, "A New Family of k-in-a-row Games", the 11th Advances in Computer Games Conference (ACG'11), Taipei, Taiwan, September *15+ C.P. Chen, ConnectLib, private communication, [16] Taiwan Connect6 Association, [17] Herik, H. J. van den, Uiterwijk, J.W.H.M., Rijswijck, J.V. (2002). Games solved: Now and in the future. Artificial Intelligence, Vol. 134, pp

Design and Implementation of Magic Chess

Design and Implementation of Magic Chess Design and Implementation of Magic Chess Wen-Chih Chen 1, Shi-Jim Yen 2, Jr-Chang Chen 3, and Ching-Nung Lin 2 Abstract: Chinese dark chess is a stochastic game which is modified to a single-player puzzle

More information

On Drawn K-In-A-Row Games

On Drawn K-In-A-Row Games On Drawn K-In-A-Row Games Sheng-Hao Chiang, I-Chen Wu 2 and Ping-Hung Lin 2 National Experimental High School at Hsinchu Science Park, Hsinchu, Taiwan jiang555@ms37.hinet.net 2 Department of Computer Science,

More information

Ageneralized family of -in-a-row games, named Connect

Ageneralized family of -in-a-row games, named Connect IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, VOL 2, NO 3, SEPTEMBER 2010 191 Relevance-Zone-Oriented Proof Search for Connect6 I-Chen Wu, Member, IEEE, and Ping-Hung Lin Abstract Wu

More information

Analysis of Computational Agents for Connect-k Games. Michael Levin, Jeff Deitch, Gabe Emerson, and Erik Shimshock.

Analysis of Computational Agents for Connect-k Games. Michael Levin, Jeff Deitch, Gabe Emerson, and Erik Shimshock. Analysis of Computational Agents for Connect-k Games. Michael Levin, Jeff Deitch, Gabe Emerson, and Erik Shimshock. Department of Computer Science and Engineering University of Minnesota, Minneapolis.

More information

Five-In-Row with Local Evaluation and Beam Search

Five-In-Row with Local Evaluation and Beam Search Five-In-Row with Local Evaluation and Beam Search Jiun-Hung Chen and Adrienne X. Wang jhchen@cs axwang@cs Abstract This report provides a brief overview of the game of five-in-row, also known as Go-Moku,

More information

Virtual Global Search: Application to 9x9 Go

Virtual Global Search: Application to 9x9 Go Virtual Global Search: Application to 9x9 Go Tristan Cazenave LIASD Dept. Informatique Université Paris 8, 93526, Saint-Denis, France cazenave@ai.univ-paris8.fr Abstract. Monte-Carlo simulations can be

More information

Games solved: Now and in the future

Games solved: Now and in the future Games solved: Now and in the future by H. J. van den Herik, J. W. H. M. Uiterwijk, and J. van Rijswijck Tsan-sheng Hsu tshsu@iis.sinica.edu.tw http://www.iis.sinica.edu.tw/~tshsu 1 Abstract Which game

More information

Outline. Game Playing. Game Problems. Game Problems. Types of games Playing a perfect game. Playing an imperfect game

Outline. Game Playing. Game Problems. Game Problems. Types of games Playing a perfect game. Playing an imperfect game Outline Game Playing ECE457 Applied Artificial Intelligence Fall 2007 Lecture #5 Types of games Playing a perfect game Minimax search Alpha-beta pruning Playing an imperfect game Real-time Imperfect information

More information

CS221 Project Final Report Gomoku Game Agent

CS221 Project Final Report Gomoku Game Agent CS221 Project Final Report Gomoku Game Agent Qiao Tan qtan@stanford.edu Xiaoti Hu xiaotihu@stanford.edu 1 Introduction Gomoku, also know as five-in-a-row, is a strategy board game which is traditionally

More information

Towards Real-Time Volunteer Distributed Computing

Towards Real-Time Volunteer Distributed Computing Towards Real-Time Volunteer Distributed Computing Sangho Yi 1, Emmanuel Jeannot 2, Derrick Kondo 1, David P. Anderson 3 1 INRIA MESCAL, 2 RUNTIME, France 3 UC Berkeley, USA Motivation Push towards large-scale,

More information

Two-Player Perfect Information Games: A Brief Survey

Two-Player Perfect Information Games: A Brief Survey Two-Player Perfect Information Games: A Brief Survey Tsan-sheng Hsu tshsu@iis.sinica.edu.tw http://www.iis.sinica.edu.tw/~tshsu 1 Abstract Domain: two-player games. Which game characters are predominant

More information

Real-Time Connect 4 Game Using Artificial Intelligence

Real-Time Connect 4 Game Using Artificial Intelligence Journal of Computer Science 5 (4): 283-289, 2009 ISSN 1549-3636 2009 Science Publications Real-Time Connect 4 Game Using Artificial Intelligence 1 Ahmad M. Sarhan, 2 Adnan Shaout and 2 Michele Shock 1

More information

Adversarial Search. CS 486/686: Introduction to Artificial Intelligence

Adversarial Search. CS 486/686: Introduction to Artificial Intelligence Adversarial Search CS 486/686: Introduction to Artificial Intelligence 1 AccessAbility Services Volunteer Notetaker Required Interested? Complete an online application using your WATIAM: https://york.accessiblelearning.com/uwaterloo/

More information

Comparison Training for Computer Chinese Chess

Comparison Training for Computer Chinese Chess Comparison Training for Computer Chinese Chess 1 Comparison Training for Computer Chinese Chess Wen-Jie Tseng 1, Jr-Chang Chen 2, I-Chen Wu 1, Senior Member, IEEE, Tinghan Wei 1 Abstract This paper describes

More information

Playing Games. Henry Z. Lo. June 23, We consider writing AI to play games with the following properties:

Playing Games. Henry Z. Lo. June 23, We consider writing AI to play games with the following properties: Playing Games Henry Z. Lo June 23, 2014 1 Games We consider writing AI to play games with the following properties: Two players. Determinism: no chance is involved; game state based purely on decisions

More information

Adversarial Search. CS 486/686: Introduction to Artificial Intelligence

Adversarial Search. CS 486/686: Introduction to Artificial Intelligence Adversarial Search CS 486/686: Introduction to Artificial Intelligence 1 Introduction So far we have only been concerned with a single agent Today, we introduce an adversary! 2 Outline Games Minimax search

More information

Two-Player Perfect Information Games: A Brief Survey

Two-Player Perfect Information Games: A Brief Survey Two-Player Perfect Information Games: A Brief Survey Tsan-sheng Hsu tshsu@iis.sinica.edu.tw http://www.iis.sinica.edu.tw/~tshsu 1 Abstract Domain: two-player games. Which game characters are predominant

More information

Game-playing AIs: Games and Adversarial Search FINAL SET (w/ pruning study examples) AIMA

Game-playing AIs: Games and Adversarial Search FINAL SET (w/ pruning study examples) AIMA Game-playing AIs: Games and Adversarial Search FINAL SET (w/ pruning study examples) AIMA 5.1-5.2 Games: Outline of Unit Part I: Games as Search Motivation Game-playing AI successes Game Trees Evaluation

More information

Playing Othello Using Monte Carlo

Playing Othello Using Monte Carlo June 22, 2007 Abstract This paper deals with the construction of an AI player to play the game Othello. A lot of techniques are already known to let AI players play the game Othello. Some of these techniques

More information

Algorithms for Data Structures: Search for Games. Phillip Smith 27/11/13

Algorithms for Data Structures: Search for Games. Phillip Smith 27/11/13 Algorithms for Data Structures: Search for Games Phillip Smith 27/11/13 Search for Games Following this lecture you should be able to: Understand the search process in games How an AI decides on the best

More information

A Quoridor-playing Agent

A Quoridor-playing Agent A Quoridor-playing Agent P.J.C. Mertens June 21, 2006 Abstract This paper deals with the construction of a Quoridor-playing software agent. Because Quoridor is a rather new game, research about the game

More information

Opponent Models and Knowledge Symmetry in Game-Tree Search

Opponent Models and Knowledge Symmetry in Game-Tree Search Opponent Models and Knowledge Symmetry in Game-Tree Search Jeroen Donkers Institute for Knowlegde and Agent Technology Universiteit Maastricht, The Netherlands donkers@cs.unimaas.nl Abstract In this paper

More information

ARTIFICIAL INTELLIGENCE (CS 370D)

ARTIFICIAL INTELLIGENCE (CS 370D) Princess Nora University Faculty of Computer & Information Systems ARTIFICIAL INTELLIGENCE (CS 370D) (CHAPTER-5) ADVERSARIAL SEARCH ADVERSARIAL SEARCH Optimal decisions Min algorithm α-β pruning Imperfect,

More information

Adversarial Search (Game Playing)

Adversarial Search (Game Playing) Artificial Intelligence Adversarial Search (Game Playing) Chapter 5 Adapted from materials by Tim Finin, Marie desjardins, and Charles R. Dyer Outline Game playing State of the art and resources Framework

More information

A Move Generating Algorithm for Hex Solvers

A Move Generating Algorithm for Hex Solvers A Move Generating Algorithm for Hex Solvers Rune Rasmussen, Frederic Maire, and Ross Hayward Faculty of Information Technology, Queensland University of Technology, Gardens Point Campus, GPO Box 2434,

More information

Muangkasem, Apimuk; Iida, Hiroyuki; Author(s) Kristian. and Multimedia, 2(1):

Muangkasem, Apimuk; Iida, Hiroyuki; Author(s) Kristian. and Multimedia, 2(1): JAIST Reposi https://dspace.j Title Aspects of Opening Play Muangkasem, Apimuk; Iida, Hiroyuki; Author(s) Kristian Citation Asia Pacific Journal of Information and Multimedia, 2(1): 49-56 Issue Date 2013-06

More information

Hex 2017: MOHEX wins the 11x11 and 13x13 tournaments

Hex 2017: MOHEX wins the 11x11 and 13x13 tournaments 222 ICGA Journal 39 (2017) 222 227 DOI 10.3233/ICG-170030 IOS Press Hex 2017: MOHEX wins the 11x11 and 13x13 tournaments Ryan Hayward and Noah Weninger Department of Computer Science, University of Alberta,

More information

A Grid-Based Game Tree Evaluation System

A Grid-Based Game Tree Evaluation System A Grid-Based Game Tree Evaluation System Pangfeng Liu Shang-Kian Wang Jan-Jan Wu Yi-Min Zhung October 15, 200 Abstract Game tree search remains an interesting subject in artificial intelligence, and has

More information

4. Games and search. Lecture Artificial Intelligence (4ov / 8op)

4. Games and search. Lecture Artificial Intelligence (4ov / 8op) 4. Games and search 4.1 Search problems State space search find a (shortest) path from the initial state to the goal state. Constraint satisfaction find a value assignment to a set of variables so that

More information

CS885 Reinforcement Learning Lecture 13c: June 13, Adversarial Search [RusNor] Sec

CS885 Reinforcement Learning Lecture 13c: June 13, Adversarial Search [RusNor] Sec CS885 Reinforcement Learning Lecture 13c: June 13, 2018 Adversarial Search [RusNor] Sec. 5.1-5.4 CS885 Spring 2018 Pascal Poupart 1 Outline Minimax search Evaluation functions Alpha-beta pruning CS885

More information

Ponnuki, FiveStones and GoloisStrasbourg: three software to help Go teachers

Ponnuki, FiveStones and GoloisStrasbourg: three software to help Go teachers Ponnuki, FiveStones and GoloisStrasbourg: three software to help Go teachers Tristan Cazenave Labo IA, Université Paris 8, 2 rue de la Liberté, 93526, St-Denis, France cazenave@ai.univ-paris8.fr Abstract.

More information

On Games And Fairness

On Games And Fairness On Games And Fairness Hiroyuki Iida Japan Advanced Institute of Science and Technology Ishikawa, Japan iida@jaist.ac.jp Abstract. In this paper we conjecture that the game-theoretic value of a sophisticated

More information

More on games (Ch )

More on games (Ch ) More on games (Ch. 5.4-5.6) Announcements Midterm next Tuesday: covers weeks 1-4 (Chapters 1-4) Take the full class period Open book/notes (can use ebook) ^^ No programing/code, internet searches or friends

More information

Artificial Intelligence Search III

Artificial Intelligence Search III Artificial Intelligence Search III Lecture 5 Content: Search III Quick Review on Lecture 4 Why Study Games? Game Playing as Search Special Characteristics of Game Playing Search Ingredients of 2-Person

More information

2 person perfect information

2 person perfect information Why Study Games? Games offer: Intellectual Engagement Abstraction Representability Performance Measure Not all games are suitable for AI research. We will restrict ourselves to 2 person perfect information

More information

COMP3211 Project. Artificial Intelligence for Tron game. Group 7. Chiu Ka Wa ( ) Chun Wai Wong ( ) Ku Chun Kit ( )

COMP3211 Project. Artificial Intelligence for Tron game. Group 7. Chiu Ka Wa ( ) Chun Wai Wong ( ) Ku Chun Kit ( ) COMP3211 Project Artificial Intelligence for Tron game Group 7 Chiu Ka Wa (20369737) Chun Wai Wong (20265022) Ku Chun Kit (20123470) Abstract Tron is an old and popular game based on a movie of the same

More information

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution

More information

For slightly more detailed instructions on how to play, visit:

For slightly more detailed instructions on how to play, visit: Introduction to Artificial Intelligence CS 151 Programming Assignment 2 Mancala!! The purpose of this assignment is to program some of the search algorithms and game playing strategies that we have learned

More information

More on games (Ch )

More on games (Ch ) More on games (Ch. 5.4-5.6) Alpha-beta pruning Previously on CSci 4511... We talked about how to modify the minimax algorithm to prune only bad searches (i.e. alpha-beta pruning) This rule of checking

More information

On the fairness and complexity of generalized k-in-a-row games

On the fairness and complexity of generalized k-in-a-row games Theoretical Computer Science 385 (2007) 88 100 www.elsevier.com/locate/tcs On the fairness and complexity of generalized k-in-a-row games Ming Yu Hsieh, Shi-Chun Tsai 1001 University Road, Department of

More information

NOTE 6 6 LOA IS SOLVED

NOTE 6 6 LOA IS SOLVED 234 ICGA Journal December 2008 NOTE 6 6 LOA IS SOLVED Mark H.M. Winands 1 Maastricht, The Netherlands ABSTRACT Lines of Action (LOA) is a two-person zero-sum game with perfect information; it is a chess-like

More information

Blunder Cost in Go and Hex

Blunder Cost in Go and Hex Advances in Computer Games: 13th Intl. Conf. ACG 2011; Tilburg, Netherlands, Nov 2011, H.J. van den Herik and A. Plaat (eds.), Springer-Verlag Berlin LNCS 7168, 2012, pp 220-229 Blunder Cost in Go and

More information

Adversary Search. Ref: Chapter 5

Adversary Search. Ref: Chapter 5 Adversary Search Ref: Chapter 5 1 Games & A.I. Easy to measure success Easy to represent states Small number of operators Comparison against humans is possible. Many games can be modeled very easily, although

More information

Game Playing. Why do AI researchers study game playing? 1. It s a good reasoning problem, formal and nontrivial.

Game Playing. Why do AI researchers study game playing? 1. It s a good reasoning problem, formal and nontrivial. Game Playing Why do AI researchers study game playing? 1. It s a good reasoning problem, formal and nontrivial. 2. Direct comparison with humans and other computer programs is easy. 1 What Kinds of Games?

More information

Real-time Grid Computing : Monte-Carlo Methods in Parallel Tree Searching

Real-time Grid Computing : Monte-Carlo Methods in Parallel Tree Searching 1 Real-time Grid Computing : Monte-Carlo Methods in Parallel Tree Searching Hermann Heßling 6. 2. 2012 2 Outline 1 Real-time Computing 2 GriScha: Chess in the Grid - by Throwing the Dice 3 Parallel Tree

More information

Contents. Foundations of Artificial Intelligence. Problems. Why Board Games?

Contents. Foundations of Artificial Intelligence. Problems. Why Board Games? Contents Foundations of Artificial Intelligence 6. Board Games Search Strategies for Games, Games with Chance, State of the Art Wolfram Burgard, Bernhard Nebel, and Martin Riedmiller Albert-Ludwigs-Universität

More information

Parameter-Free Tree Style Pipeline in Asynchronous Parallel Game-Tree Search

Parameter-Free Tree Style Pipeline in Asynchronous Parallel Game-Tree Search Parameter-Free Tree Style Pipeline in Asynchronous Parallel Game-Tree Search Shu YOKOYAMA, Tomoyuki KANEKO, Tetsuro TANAKA 2015 07 03T11:15+02:00 ACG2015 Leiden Motivation Game tree search in distributed

More information

Strategic Evaluation in Complex Domains

Strategic Evaluation in Complex Domains Strategic Evaluation in Complex Domains Tristan Cazenave LIP6 Université Pierre et Marie Curie 4, Place Jussieu, 755 Paris, France Tristan.Cazenave@lip6.fr Abstract In some complex domains, like the game

More information

Artificial Intelligence Lecture 3

Artificial Intelligence Lecture 3 Artificial Intelligence Lecture 3 The problem Depth first Not optimal Uses O(n) space Optimal Uses O(B n ) space Can we combine the advantages of both approaches? 2 Iterative deepening (IDA) Let M be a

More information

Computer Science and Software Engineering University of Wisconsin - Platteville. 4. Game Play. CS 3030 Lecture Notes Yan Shi UW-Platteville

Computer Science and Software Engineering University of Wisconsin - Platteville. 4. Game Play. CS 3030 Lecture Notes Yan Shi UW-Platteville Computer Science and Software Engineering University of Wisconsin - Platteville 4. Game Play CS 3030 Lecture Notes Yan Shi UW-Platteville Read: Textbook Chapter 6 What kind of games? 2-player games Zero-sum

More information

Computer Go: from the Beginnings to AlphaGo. Martin Müller, University of Alberta

Computer Go: from the Beginnings to AlphaGo. Martin Müller, University of Alberta Computer Go: from the Beginnings to AlphaGo Martin Müller, University of Alberta 2017 Outline of the Talk Game of Go Short history - Computer Go from the beginnings to AlphaGo The science behind AlphaGo

More information

Lambda Depth-first Proof Number Search and its Application to Go

Lambda Depth-first Proof Number Search and its Application to Go Lambda Depth-first Proof Number Search and its Application to Go Kazuki Yoshizoe Dept. of Electrical, Electronic, and Communication Engineering, Chuo University, Japan yoshizoe@is.s.u-tokyo.ac.jp Akihiro

More information

Creating a Havannah Playing Agent

Creating a Havannah Playing Agent Creating a Havannah Playing Agent B. Joosten August 27, 2009 Abstract This paper delves into the complexities of Havannah, which is a 2-person zero-sum perfectinformation board game. After determining

More information

A Bandit Approach for Tree Search

A Bandit Approach for Tree Search A An Example in Computer-Go Department of Statistics, University of Michigan March 27th, 2008 A 1 Bandit Problem K-Armed Bandit UCB Algorithms for K-Armed Bandit Problem 2 Classical Tree Search UCT Algorithm

More information

Introduction to Artificial Intelligence CS 151 Programming Assignment 2 Mancala!! Due (in dropbox) Tuesday, September 23, 9:34am

Introduction to Artificial Intelligence CS 151 Programming Assignment 2 Mancala!! Due (in dropbox) Tuesday, September 23, 9:34am Introduction to Artificial Intelligence CS 151 Programming Assignment 2 Mancala!! Due (in dropbox) Tuesday, September 23, 9:34am The purpose of this assignment is to program some of the search algorithms

More information

ADVERSARIAL SEARCH. Today. Reading. Goals. AIMA Chapter Read , Skim 5.7

ADVERSARIAL SEARCH. Today. Reading. Goals. AIMA Chapter Read , Skim 5.7 ADVERSARIAL SEARCH Today Reading AIMA Chapter Read 5.1-5.5, Skim 5.7 Goals Introduce adversarial games Minimax as an optimal strategy Alpha-beta pruning 1 Adversarial Games People like games! Games are

More information

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence CS 4700: Foundations of Artificial Intelligence selman@cs.cornell.edu Module: Adversarial Search R&N: Chapter 5 1 Outline Adversarial Search Optimal decisions Minimax α-β pruning Case study: Deep Blue

More information

Retrograde Analysis of Woodpush

Retrograde Analysis of Woodpush Retrograde Analysis of Woodpush Tristan Cazenave 1 and Richard J. Nowakowski 2 1 LAMSADE Université Paris-Dauphine Paris France cazenave@lamsade.dauphine.fr 2 Dept. of Mathematics and Statistics Dalhousie

More information

Foundations of AI. 5. Board Games. Search Strategies for Games, Games with Chance, State of the Art. Wolfram Burgard and Luc De Raedt SA-1

Foundations of AI. 5. Board Games. Search Strategies for Games, Games with Chance, State of the Art. Wolfram Burgard and Luc De Raedt SA-1 Foundations of AI 5. Board Games Search Strategies for Games, Games with Chance, State of the Art Wolfram Burgard and Luc De Raedt SA-1 Contents Board Games Minimax Search Alpha-Beta Search Games with

More information

Adversarial Search and Game- Playing C H A P T E R 6 C M P T : S P R I N G H A S S A N K H O S R A V I

Adversarial Search and Game- Playing C H A P T E R 6 C M P T : S P R I N G H A S S A N K H O S R A V I Adversarial Search and Game- Playing C H A P T E R 6 C M P T 3 1 0 : S P R I N G 2 0 1 1 H A S S A N K H O S R A V I Adversarial Search Examine the problems that arise when we try to plan ahead in a world

More information

Artificial Intelligence. Minimax and alpha-beta pruning

Artificial Intelligence. Minimax and alpha-beta pruning Artificial Intelligence Minimax and alpha-beta pruning In which we examine the problems that arise when we try to plan ahead to get the best result in a world that includes a hostile agent (other agent

More information

COMP219: Artificial Intelligence. Lecture 13: Game Playing

COMP219: Artificial Intelligence. Lecture 13: Game Playing CMP219: Artificial Intelligence Lecture 13: Game Playing 1 verview Last time Search with partial/no observations Belief states Incremental belief state search Determinism vs non-determinism Today We will

More information

COMP219: COMP219: Artificial Intelligence Artificial Intelligence Dr. Annabel Latham Lecture 12: Game Playing Overview Games and Search

COMP219: COMP219: Artificial Intelligence Artificial Intelligence Dr. Annabel Latham Lecture 12: Game Playing Overview Games and Search COMP19: Artificial Intelligence COMP19: Artificial Intelligence Dr. Annabel Latham Room.05 Ashton Building Department of Computer Science University of Liverpool Lecture 1: Game Playing 1 Overview Last

More information

Foundations of Artificial Intelligence

Foundations of Artificial Intelligence Foundations of Artificial Intelligence 6. Board Games Search Strategies for Games, Games with Chance, State of the Art Joschka Boedecker and Wolfram Burgard and Bernhard Nebel Albert-Ludwigs-Universität

More information

Plans, Patterns and Move Categories Guiding a Highly Selective Search

Plans, Patterns and Move Categories Guiding a Highly Selective Search Plans, Patterns and Move Categories Guiding a Highly Selective Search Gerhard Trippen The University of British Columbia {Gerhard.Trippen}@sauder.ubc.ca. Abstract. In this paper we present our ideas for

More information

Generation of Patterns With External Conditions for the Game of Go

Generation of Patterns With External Conditions for the Game of Go Generation of Patterns With External Conditions for the Game of Go Tristan Cazenave 1 Abstract. Patterns databases are used to improve search in games. We have generated pattern databases for the game

More information

CS 380: ARTIFICIAL INTELLIGENCE MONTE CARLO SEARCH. Santiago Ontañón

CS 380: ARTIFICIAL INTELLIGENCE MONTE CARLO SEARCH. Santiago Ontañón CS 380: ARTIFICIAL INTELLIGENCE MONTE CARLO SEARCH Santiago Ontañón so367@drexel.edu Recall: Adversarial Search Idea: When there is only one agent in the world, we can solve problems using DFS, BFS, ID,

More information

Foundations of Artificial Intelligence

Foundations of Artificial Intelligence Foundations of Artificial Intelligence 6. Board Games Search Strategies for Games, Games with Chance, State of the Art Joschka Boedecker and Wolfram Burgard and Frank Hutter and Bernhard Nebel Albert-Ludwigs-Universität

More information

A Complex Systems Introduction to Go

A Complex Systems Introduction to Go A Complex Systems Introduction to Go Eric Jankowski CSAAW 10-22-2007 Background image by Juha Nieminen Wei Chi, Go, Baduk... Oldest board game in the world (maybe) Developed by Chinese monks Spread to

More information

2048: An Autonomous Solver

2048: An Autonomous Solver 2048: An Autonomous Solver Final Project in Introduction to Artificial Intelligence ABSTRACT. Our goal in this project was to create an automatic solver for the wellknown game 2048 and to analyze how different

More information

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Adversarial Search Instructors: David Suter and Qince Li Course Delivered @ Harbin Institute of Technology [Many slides adapted from those created by Dan Klein and Pieter Abbeel

More information

Minimax Trees: Utility Evaluation, Tree Evaluation, Pruning

Minimax Trees: Utility Evaluation, Tree Evaluation, Pruning Minimax Trees: Utility Evaluation, Tree Evaluation, Pruning CSCE 315 Programming Studio Fall 2017 Project 2, Lecture 2 Adapted from slides of Yoonsuck Choe, John Keyser Two-Person Perfect Information Deterministic

More information

Game-playing AIs: Games and Adversarial Search I AIMA

Game-playing AIs: Games and Adversarial Search I AIMA Game-playing AIs: Games and Adversarial Search I AIMA 5.1-5.2 Games: Outline of Unit Part I: Games as Search Motivation Game-playing AI successes Game Trees Evaluation Functions Part II: Adversarial Search

More information

Game Playing AI Class 8 Ch , 5.4.1, 5.5

Game Playing AI Class 8 Ch , 5.4.1, 5.5 Game Playing AI Class Ch. 5.-5., 5.4., 5.5 Bookkeeping HW Due 0/, :59pm Remaining CSP questions? Cynthia Matuszek CMSC 6 Based on slides by Marie desjardin, Francisco Iacobelli Today s Class Clear criteria

More information

CS151 - Assignment 2 Mancala Due: Tuesday March 5 at the beginning of class

CS151 - Assignment 2 Mancala Due: Tuesday March 5 at the beginning of class CS151 - Assignment 2 Mancala Due: Tuesday March 5 at the beginning of class http://www.clubpenguinsaraapril.com/2009/07/mancala-game-in-club-penguin.html The purpose of this assignment is to program some

More information

CS 229 Final Project: Using Reinforcement Learning to Play Othello

CS 229 Final Project: Using Reinforcement Learning to Play Othello CS 229 Final Project: Using Reinforcement Learning to Play Othello Kevin Fry Frank Zheng Xianming Li ID: kfry ID: fzheng ID: xmli 16 December 2016 Abstract We built an AI that learned to play Othello.

More information

CMPUT 396 Tic-Tac-Toe Game

CMPUT 396 Tic-Tac-Toe Game CMPUT 396 Tic-Tac-Toe Game Recall minimax: - For a game tree, we find the root minimax from leaf values - With minimax we can always determine the score and can use a bottom-up approach Why use minimax?

More information

Game Playing State-of-the-Art CSE 473: Artificial Intelligence Fall Deterministic Games. Zero-Sum Games 10/13/17. Adversarial Search

Game Playing State-of-the-Art CSE 473: Artificial Intelligence Fall Deterministic Games. Zero-Sum Games 10/13/17. Adversarial Search CSE 473: Artificial Intelligence Fall 2017 Adversarial Search Mini, pruning, Expecti Dieter Fox Based on slides adapted Luke Zettlemoyer, Dan Klein, Pieter Abbeel, Dan Weld, Stuart Russell or Andrew Moore

More information

Game-playing: DeepBlue and AlphaGo

Game-playing: DeepBlue and AlphaGo Game-playing: DeepBlue and AlphaGo Brief history of gameplaying frontiers 1990s: Othello world champions refuse to play computers 1994: Chinook defeats Checkers world champion 1997: DeepBlue defeats world

More information

Programming Project 1: Pacman (Due )

Programming Project 1: Pacman (Due ) Programming Project 1: Pacman (Due 8.2.18) Registration to the exams 521495A: Artificial Intelligence Adversarial Search (Min-Max) Lectured by Abdenour Hadid Adjunct Professor, CMVS, University of Oulu

More information

An Intelligent Agent for Connect-6

An Intelligent Agent for Connect-6 An Intelligent Agent for Connect-6 Sagar Vare, Sherrie Wang, Andrea Zanette {svare, sherwang, zanette}@stanford.edu Institute for Computational and Mathematical Engineering Huang Building 475 Via Ortega

More information

Previous attempts at parallelizing the Proof Number Search (PNS) algorithm used randomization [16] or a specialized algorithm called at the leaves of

Previous attempts at parallelizing the Proof Number Search (PNS) algorithm used randomization [16] or a specialized algorithm called at the leaves of Solving breakthrough with Race Patterns and Job-Level Proof Number Search Abdallah Sa dine1, Nicolas Jouandeau2, and Tristan Cazenave1 1 LAMSADE, Université Paris-Dauphine 2 LIASD, Université Paris 8 Abstract.

More information

Towards A World-Champion Level Computer Chess Tutor

Towards A World-Champion Level Computer Chess Tutor Towards A World-Champion Level Computer Chess Tutor David Levy Abstract. Artificial Intelligence research has already created World- Champion level programs in Chess and various other games. Such programs

More information

Monte Carlo Tree Search

Monte Carlo Tree Search Monte Carlo Tree Search 1 By the end, you will know Why we use Monte Carlo Search Trees The pros and cons of MCTS How it is applied to Super Mario Brothers and Alpha Go 2 Outline I. Pre-MCTS Algorithms

More information

CS 440 / ECE 448 Introduction to Artificial Intelligence Spring 2010 Lecture #5

CS 440 / ECE 448 Introduction to Artificial Intelligence Spring 2010 Lecture #5 CS 440 / ECE 448 Introduction to Artificial Intelligence Spring 2010 Lecture #5 Instructor: Eyal Amir Grad TAs: Wen Pu, Yonatan Bisk Undergrad TAs: Sam Johnson, Nikhil Johri Topics Game playing Game trees

More information

Adversarial Search: Game Playing. Reading: Chapter

Adversarial Search: Game Playing. Reading: Chapter Adversarial Search: Game Playing Reading: Chapter 6.5-6.8 1 Games and AI Easy to represent, abstract, precise rules One of the first tasks undertaken by AI (since 1950) Better than humans in Othello and

More information

Proposal and Evaluation of System of Dynamic Adapting Method to Player s Skill

Proposal and Evaluation of System of Dynamic Adapting Method to Player s Skill 1,a) 1 2016 2 19, 2016 9 6 AI AI AI AI 0 AI 3 AI AI AI AI AI AI AI AI AI 5% AI AI Proposal and Evaluation of System of Dynamic Adapting Method to Player s Skill Takafumi Nakamichi 1,a) Takeshi Ito 1 Received:

More information

Artificial Intelligence 1: game playing

Artificial Intelligence 1: game playing Artificial Intelligence 1: game playing Lecturer: Tom Lenaerts Institut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle (IRIDIA) Université Libre de Bruxelles Outline

More information

Exploration and Analysis of the Evolution of Strategies for Mancala Variants

Exploration and Analysis of the Evolution of Strategies for Mancala Variants Exploration and Analysis of the Evolution of Strategies for Mancala Variants Colin Divilly, Colm O Riordan and Seamus Hill Abstract This paper describes approaches to evolving strategies for Mancala variants.

More information

Game Playing Part 1 Minimax Search

Game Playing Part 1 Minimax Search Game Playing Part 1 Minimax Search Yingyu Liang yliang@cs.wisc.edu Computer Sciences Department University of Wisconsin, Madison [based on slides from A. Moore http://www.cs.cmu.edu/~awm/tutorials, C.

More information

Artificial Intelligence

Artificial Intelligence Artificial Intelligence CS482, CS682, MW 1 2:15, SEM 201, MS 227 Prerequisites: 302, 365 Instructor: Sushil Louis, sushil@cse.unr.edu, http://www.cse.unr.edu/~sushil Games and game trees Multi-agent systems

More information

Foundations of AI. 6. Adversarial Search. Search Strategies for Games, Games with Chance, State of the Art. Wolfram Burgard & Bernhard Nebel

Foundations of AI. 6. Adversarial Search. Search Strategies for Games, Games with Chance, State of the Art. Wolfram Burgard & Bernhard Nebel Foundations of AI 6. Adversarial Search Search Strategies for Games, Games with Chance, State of the Art Wolfram Burgard & Bernhard Nebel Contents Game Theory Board Games Minimax Search Alpha-Beta Search

More information

Adversarial Search Aka Games

Adversarial Search Aka Games Adversarial Search Aka Games Chapter 5 Some material adopted from notes by Charles R. Dyer, U of Wisconsin-Madison Overview Game playing State of the art and resources Framework Game trees Minimax Alpha-beta

More information

V. Adamchik Data Structures. Game Trees. Lecture 1. Apr. 05, Plan: 1. Introduction. 2. Game of NIM. 3. Minimax

V. Adamchik Data Structures. Game Trees. Lecture 1. Apr. 05, Plan: 1. Introduction. 2. Game of NIM. 3. Minimax Game Trees Lecture 1 Apr. 05, 2005 Plan: 1. Introduction 2. Game of NIM 3. Minimax V. Adamchik 2 ü Introduction The search problems we have studied so far assume that the situation is not going to change.

More information

Data Structures and Algorithms

Data Structures and Algorithms Data Structures and Algorithms CS245-2015S-P4 Two Player Games David Galles Department of Computer Science University of San Francisco P4-0: Overview Example games (board splitting, chess, Network) /Max

More information

CS 188: Artificial Intelligence Spring Announcements

CS 188: Artificial Intelligence Spring Announcements CS 188: Artificial Intelligence Spring 2011 Lecture 7: Minimax and Alpha-Beta Search 2/9/2011 Pieter Abbeel UC Berkeley Many slides adapted from Dan Klein 1 Announcements W1 out and due Monday 4:59pm P2

More information

Announcements. CS 188: Artificial Intelligence Spring Game Playing State-of-the-Art. Overview. Game Playing. GamesCrafters

Announcements. CS 188: Artificial Intelligence Spring Game Playing State-of-the-Art. Overview. Game Playing. GamesCrafters CS 188: Artificial Intelligence Spring 2011 Announcements W1 out and due Monday 4:59pm P2 out and due next week Friday 4:59pm Lecture 7: Mini and Alpha-Beta Search 2/9/2011 Pieter Abbeel UC Berkeley Many

More information

Small and large MCTS playouts applied to Chinese Dark Chess stochastic game

Small and large MCTS playouts applied to Chinese Dark Chess stochastic game Small and large MCTS playouts applied to Chinese Dark Chess stochastic game Nicolas Jouandeau 1 and Tristan Cazenave 2 1 LIASD, Université de Paris 8, France n@ai.univ-paris8.fr 2 LAMSADE, Université Paris-Dauphine,

More information

Testing of Chips Used for Artificial Intelligence. PH Chen, Project Management KeyStone Alan Liao, Product Marketing FormFactor

Testing of Chips Used for Artificial Intelligence. PH Chen, Project Management KeyStone Alan Liao, Product Marketing FormFactor Testing of Chips Used for Artificial Intelligence PH Chen, Project Management KeyStone Alan Liao, Product Marketing FormFactor Agenda Artificial Intelligence Evolution and Market Space Why AI Today AI

More information

CS 5522: Artificial Intelligence II

CS 5522: Artificial Intelligence II CS 5522: Artificial Intelligence II Adversarial Search Instructor: Alan Ritter Ohio State University [These slides were adapted from CS188 Intro to AI at UC Berkeley. All materials available at http://ai.berkeley.edu.]

More information