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Adaptive Playouts in Monte-Carlo Tree Search with Policy-Gradient Reinforcement Learning Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Transfer Learning by Inductive Logic Programming

Authors : Yuichiro Sato, Hiroyuki Iida, H. J. van den Herik

Published in: Advances in Computer Games

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a Transfer Learning method by Inductive Logic Programing for games. We generate general knowledge from a game, and specify the knowledge so that it is applicable in another game. This is called Transfer Learning. We show the working of Transfer Learning by taking knowledge from Tic-tac-toe and transfer it to Connect4 and Connect5. For Connect4 the number of Heuristic functions we developed is 30; for Connect5 it is 20.

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previous chapter Parameter-Free Tree Style Pipeline in Asynchronous Parallel Game-Tree Search
next chapter Developing Computer Hex Using Global and Local Evaluation Based on Board Network Characteristics
Footnotes
1
For brevity, we use ‘he’ and ‘his’, whenever ‘he or she’ and ‘his or her’ are meant.
 
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Metadata
Title
Transfer Learning by Inductive Logic Programming
Authors
Yuichiro Sato
Hiroyuki Iida
H. J. van den Herik
Copyright Year
2015
Book
Advances in Computer Games

Print ISBN: 978-3-319-27991-6

Electronic ISBN: 978-3-319-27992-3

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-27992-3_20

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