Game tree

Results: 209



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21CE318: High-level Games Development Lecture 9: Game AI: Decision Making Diego Perez  Office 3A.527

CE318: High-level Games Development Lecture 9: Game AI: Decision Making Diego Perez Office 3A.527

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Source URL: orb.essex.ac.uk

Language: English - Date: 2015-11-30 07:41:54
22Monte Carlo Tree Search with Macro-Actions and Heuristic Route Planning for the Multiobjective Physical Travelling Salesman Problem Edward J. Powley, Daniel Whitehouse, and Peter I. Cowling Department of Computer Science

Monte Carlo Tree Search with Macro-Actions and Heuristic Route Planning for the Multiobjective Physical Travelling Salesman Problem Edward J. Powley, Daniel Whitehouse, and Peter I. Cowling Department of Computer Science

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Source URL: eldar.mathstat.uoguelph.ca

Language: English - Date: 2016-07-12 12:05:04
2314 night’s safari combining the excitement of the Victoria Falls with Zambia’s best game-viewing destinations plus the tranquillity and beauty of Lake Malawi. Kaya Mawa together with Tongabezi, Sausage Tree and Norma

14 night’s safari combining the excitement of the Victoria Falls with Zambia’s best game-viewing destinations plus the tranquillity and beauty of Lake Malawi. Kaya Mawa together with Tongabezi, Sausage Tree and Norma

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Source URL: kayamawa.com

- Date: 2013-02-06 06:36:34
    24Optimal Efficiency Guarantees for Network Design Mechanisms? Tim Roughgarden??1 and Mukund Sundararajan? ? ?1 Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305.

    Optimal Efficiency Guarantees for Network Design Mechanisms? Tim Roughgarden??1 and Mukund Sundararajan? ? ?1 Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305.

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    Source URL: theory.stanford.edu

    Language: English - Date: 2007-04-09 01:40:43
    25Finding Graph Topologies for Feasible Multirobot Motion Planning Pushkar Kolhe Henrik I. Christensen  Abstract— In this paper we present a design methodology

    Finding Graph Topologies for Feasible Multirobot Motion Planning Pushkar Kolhe Henrik I. Christensen Abstract— In this paper we present a design methodology

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    Source URL: www.researchgate.net

    Language: English
    26Hyper-heuristic General Video Game Playing Andre Mendes Julian Togelius  Andy Nealen

    Hyper-heuristic General Video Game Playing Andre Mendes Julian Togelius Andy Nealen

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    Source URL: julian.togelius.com

    Language: English - Date: 2016-07-24 11:50:26
    272014 Cultural Resources Field Survey Summary

    2014 Cultural Resources Field Survey Summary

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    Source URL: asapgas.agdc.us

    Language: English - Date: 2015-10-19 05:36:37
    28Mastering the Game of Go with Deep Neural Networks and Tree Search David Silver1 *, Aja Huang1 *, Chris J. Maddison1 , Arthur Guez1 , Laurent Sifre1 , George van den Driessche1 , Julian Schrittwieser1 , Ioannis Antonoglo

    Mastering the Game of Go with Deep Neural Networks and Tree Search David Silver1 *, Aja Huang1 *, Chris J. Maddison1 , Arthur Guez1 , Laurent Sifre1 , George van den Driessche1 , Julian Schrittwieser1 , Ioannis Antonoglo

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    Source URL: gogameguru.com

    Language: English - Date: 2016-03-04 05:06:20
    292.2 Counting Outcomes Overview of the lesson plan: Is it necessary to play a game many times to figure out the likelihood of winning? If not, how can we measure this likelihood? In this lesson, students learn that they c

    2.2 Counting Outcomes Overview of the lesson plan: Is it necessary to play a game many times to figure out the likelihood of winning? If not, how can we measure this likelihood? In this lesson, students learn that they c

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    Source URL: citydigits.org

    Language: English - Date: 2016-01-13 14:16:54
    30Transpositions and Move Groups in Monte Carlo Tree Search Benjamin E. Childs, Member, IEEE, James H. Brodeur, and Levente Kocsis. Abstract— Monte Carlo search, and specifically the UCT (Upper Confidence Bounds applied

    Transpositions and Move Groups in Monte Carlo Tree Search Benjamin E. Childs, Member, IEEE, James H. Brodeur, and Levente Kocsis. Abstract— Monte Carlo search, and specifically the UCT (Upper Confidence Bounds applied

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    Source URL: www.csse.uwa.edu.au

    Language: English - Date: 2009-02-05 01:17:40