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MCS (Workshop : Monte Carlo) (2020 :)

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Monte Carlo Search = first workshop, MCS 2020, held in conjunction with IJCAI 2020, virtual event, January 7, 2021 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Monte Carlo Search/ edited by Tristan Cazenave, Olivier Teytaud, Mark H. M. Winands.
Reminder of title:	first workshop, MCS 2020, held in conjunction with IJCAI 2020, virtual event, January 7, 2021 : proceedings /
remainder title:	MCS 2020
other author:	Cazenave, Tristan.
corporate name:	MCS (Workshop : Monte Carlo)
Published:	Cham :Springer International Publishing : : 2021.,
Description:	ix, 141 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	The Search Algorithm for the Game of Bridge -- Stabilized Nested Rollout Policy Adaptation -- zoNNscan: A Boundary-Entropy Index for Zone Inspection of Neural Models -- Ordinal Monte Carlo Tree Search -- Monte Carlo Game Solver -- Generalized Nested Rollout Policy Adaptation -- Monte Carlo Inverse Folding -- Monte Carlo Graph Coloring -- Enhancing Playout Policy Adaptation for General Game Playing.
Contained By:	Springer Nature eBook
Subject:	Monte Carlo method - Congresses. - Data processing -
Online resource:	https://doi.org/10.1007/978-3-030-89453-5
ISBN:	9783030894535

Monte Carlo Search = first workshop, MCS 2020, held in conjunction with IJCAI 2020, virtual event, January 7, 2021 : proceedings /
Monte Carlo Searchfirst workshop, MCS 2020, held in conjunction with IJCAI 2020, virtual event, January 7, 2021 : proceedings /[electronic resource] :MCS 2020edited by Tristan Cazenave, Olivier Teytaud, Mark H. M. Winands. - Cham :Springer International Publishing :2021. - ix, 141 p. :ill. (some col.), digital ;24 cm. - Communications in computer and information science,13791865-0937 ;. - Communications in computer and information science ;1379..

The Search Algorithm for the Game of Bridge -- Stabilized Nested Rollout Policy Adaptation -- zoNNscan: A Boundary-Entropy Index for Zone Inspection of Neural Models -- Ordinal Monte Carlo Tree Search -- Monte Carlo Game Solver -- Generalized Nested Rollout Policy Adaptation -- Monte Carlo Inverse Folding -- Monte Carlo Graph Coloring -- Enhancing Playout Policy Adaptation for General Game Playing.

This book constitutes the refereed proceedings of the First Workshop on Monte Carlo Search, MCS 2020, organized in conjunction with IJCAI 2020. The event was supposed to take place in Yokohama, Japan, in July 2020, but due to the Covid-19 pandemic was held virtually on January 7, 2021. The 9 full papers of the specialized project were carefully reviewed and selected from 15 submissions. The following topics are covered in the contributions: discrete mathematics in computer science, games, optimization, search algorithms, Monte Carlo methods, neural networks, reinforcement learning, machine learning.

ISBN: 9783030894535

Standard No.: 10.1007/978-3-030-89453-5doiSubjects--Topical Terms:

3521682
Monte Carlo method
--Data processing--Congresses.

LC Class. No.: QA298 / .M37 2020

Dewey Class. No.: 518.282

Monte Carlo Search = first workshop, MCS 2020, held in conjunction with IJCAI 2020, virtual event, January 7, 2021 : proceedings /
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505 0 $a The Search Algorithm for the Game of Bridge -- Stabilized Nested Rollout Policy Adaptation -- zoNNscan: A Boundary-Entropy Index for Zone Inspection of Neural Models -- Ordinal Monte Carlo Tree Search -- Monte Carlo Game Solver -- Generalized Nested Rollout Policy Adaptation -- Monte Carlo Inverse Folding -- Monte Carlo Graph Coloring -- Enhancing Playout Policy Adaptation for General Game Playing.
520 $a This book constitutes the refereed proceedings of the First Workshop on Monte Carlo Search, MCS 2020, organized in conjunction with IJCAI 2020. The event was supposed to take place in Yokohama, Japan, in July 2020, but due to the Covid-19 pandemic was held virtually on January 7, 2021. The 9 full papers of the specialized project were carefully reviewed and selected from 15 submissions. The following topics are covered in the contributions: discrete mathematics in computer science, games, optimization, search algorithms, Monte Carlo methods, neural networks, reinforcement learning, machine learning.
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