EnHiC: An Enforced Hill Climbing Based System for General Game Playing

被引：0

作者：

Babadi, Amin ^{[1
]}

Omoomi, Behnaz ^{[2
]}

Kendall, Graham ^{[3
,4
]}

机构：

[1] Isfahan Univ Technol, Dept Elect & Comp Engn, Esfahan 8415683111, Iran

[2] Isfahan Univ Technol, Dept Math Sci, Esfahan 8415683111, Iran

[3] Univ Nottingham, Sch Comp Sci, Nottingham NG7 2RD, England

[4] Univ Nottingham, Sch Comp Sci, Semenyih, Malaysia

来源：

2015 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND GAMES (CIG) | 2015年

关键词：

General video game playing; general game playing; enforced hill climbing; heuristic functions; FF PLANNING SYSTEM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Accurate decision making in games has always been a very complex and yet interesting problem in Artificial Intelligence (AI). General video game playing (GVGP) is a new branch of AI whose target is to design agents that are able to win in every unknown game environment by choosing wise decisions. This paper proposes a new search methodology based on enforced hill climbing for using in GVGP and we evaluate its performance on the benchmarks of the general video game AI competition (GVG-AI). Also a simple and efficient heuristic function for GVGP is proposed. The results show that EnHiC outperforms several well-known and successful methods in the GVG-AI competition.

引用

页码：193 / 199

页数：7

共 16 条

[11] A Parallel General Game Player
Mehat, Jean
Cazenave, Tristan
[J]. KUNSTLICHE INTELLIGENZ, 2011, 25 (01): : 43 - 47
[12] Multiobjective Monte Carlo Tree Search for Real-Time Games
Perez, Diego
Mostaghim, Sanaz
Samothrakis, Spyridon
Lucas, Simon M.
[J]. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES, 2015, 7 (04) : 347 - 360
[13] Schiffel S., 2007, AAAI, P1191
[14] Swiechowski M., 2015, IEEE T COMPUTATIONAL
[15] Wu JH, 2011, J ARTIF INTELL RES, V42, P815
[16] Yoon S. W., 2006, PROC INT C AUTOMATED, V2, P3

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