A first person shooter game is a game genre where fun in the game resorts to the skill of the opponents. In particular, when the opponents are bots, adjustment of their skill levels as well as implementation of their human-likeness are important research issues that many research groups have been studying recently in order to make bots more interesting to play with. This led to a contest called BotPrize 2008 held in December 2008 where bots and human players competed with each other for human-likeness. In this paper, we analyze play video clips of all bots participated in this contest, including our bot ranked 2nd in terms of human-likeness, and derive five factors related to bot’s human-likeness. In addition, we further improve our bot considering these five factors and conduct an experiment comparing our original bot, the one submitted to the contest, and the improved one. Experimental results confirm the effectiveness of our improvements, showing that the human-likeness of the improved one has risen significantly. Keywords—FPS, Unreal Tournament, Human-likeness, Believability, Bot
[1]
Marcus Gallagher,et al.
Learning to be a Bot: Reinforcement Learning in Shooter Games
,
2008,
AIIDE.
[2]
Ah-Hwee Tan,et al.
Creating Human-like Autonomous Players in Real-time First Person Shooter Computer Games
,
2009
.
[3]
Bernard Gorman.
IMITATIVE LEARNING OF COMBAT BEHAVIOURS IN FIRST-PERSON COMPUTER GAMES
,
2007
.
[4]
Philip Hingston,et al.
Bots trained to play like a human are more fun
,
2008,
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[5]
Manuela M. Veloso,et al.
GameBots: a flexible test bed for multiagent team research
,
2002,
CACM.
[6]
Rudolf Kadlec,et al.
POGAMUT 2 - A PLATFORM FOR FAST DEVELOPMENT OF VIRTUAL AGENTS' BEHAVIOR
,
2007
.
[7]
Hector Muñoz-Avila,et al.
Hierarchical Plan Representations for Encoding Strategic Game AI
,
2005,
AIIDE.