1,842 research outputs found
Monte-Carlo tree search for persona based player modeling
Is it possible to conduct player modeling without any players?
In this paper we use Monte-Carlo Tree Search-controlled
procedural personas to simulate a range of decision making
styles in the puzzle game MiniDungeons 2. The purpose is
to provide a method for synthetic play testing of game levels
with synthetic players based on designer intuition and experience.
Five personas are constructed, representing five different
decision making styles archetypal for the game. The personas
vary solely in the weights of decision-making utilities
that describe their valuation of a set affordances in MiniDungeons
2. By configuring these weights using designer expert
knowledge, and passing the configurations directly to the
MCTS algorithm, we make the personas exhibit a number of
distinct decision making and play styles.The research was supported, in part, by the FP7 ICT project
C2Learn (project no: 318480), the FP7 Marie Curie CIG
project AutoGameDesign (project no: 630665), and by the
Stibo Foundation Travel Bursary Grant for Global IT Talents.peer-reviewe
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI
In fighting games, individual players of the same skill level often exhibit
distinct strategies from one another through their gameplay. Despite this, the
majority of AI agents for fighting games have only a single strategy for each
"level" of difficulty. To make AI opponents more human-like, we'd ideally like
to see multiple different strategies at each level of difficulty, a concept we
refer to as "multidimensional" difficulty. In this paper, we introduce a
diversity-based deep reinforcement learning approach for generating a set of
agents of similar difficulty that utilize diverse strategies. We find this
approach outperforms a baseline trained with specialized, human-authored reward
functions in both diversity and performance.Comment: 8 pages, 2 figures, Experimental AI in Games 202
Adaptive Background Music for a Fighting Game: A Multi-Instrument Volume Modulation Approach
This paper presents our work to enhance the background music (BGM) in
DareFightingICE by adding an adaptive BGM. The adaptive BGM consists of five
different instruments playing a classical music piece called "Air on G-String."
The BGM adapts by changing the volume of the instruments. Each instrument is
connected to a different element of the game. We then run experiments to
evaluate the adaptive BGM by using a deep reinforcement learning AI that only
uses audio as input (Blind DL AI). The results show that the performance of the
Blind DL AI improves while playing with the adaptive BGM as compared to playing
without the adaptive BGM.Comment: This paper under review is made available for participants of
DareFightingICE Competition (https://tinyurl.com/DareFightingICE) and readers
interested in relevant area
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI
This paper reviews the field of Game AI, which not only deals with creating
agents that can play a certain game, but also with areas as diverse as creating
game content automatically, game analytics, or player modelling. While Game AI
was for a long time not very well recognized by the larger scientific
community, it has established itself as a research area for developing and
testing the most advanced forms of AI algorithms and articles covering advances
in mastering video games such as StarCraft 2 and Quake III appear in the most
prestigious journals. Because of the growth of the field, a single review
cannot cover it completely. Therefore, we put a focus on important recent
developments, including that advances in Game AI are starting to be extended to
areas outside of games, such as robotics or the synthesis of chemicals. In this
article, we review the algorithms and methods that have paved the way for these
breakthroughs, report on the other important areas of Game AI research, and
also point out exciting directions for the future of Game AI
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