To Create Intelligent Adaptive Game Opponent by Using Monte-Carlo for Tree Search

Yang, Jiajian; Gao, Yuan; He, Suoju; Liu, Xiao; Fu, Yiwen; Yang, Changwei; Ji, Donglin

doi:10.1109/icnc.2009.710

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…Yang et al [239] and Fu et al [88] used MCTS methods to improve the performance of their joint ANN-based Dead End player. Zhang et al [240] deal with the problem of Dynamic Difficulty Adjustment (DDA) using a time-constrained UCT.…”

Section: Pocman and Battleship Silver And Venessmentioning

confidence: 99%

A Survey of Monte Carlo Tree Search Methods

Browne

Powley

Whitehouse

et al. 2012

IEEE Trans. Comput. Intell. AI Games

2,260

1,535

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Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

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Section: Pocman and Battleship Silver And Venessmentioning

confidence: 99%

A Survey of Monte Carlo Tree Search Methods

Browne

Powley

Whitehouse

et al. 2012

IEEE Trans. Comput. Intell. AI Games

2,260

1,535

View full text Add to dashboard Cite

show abstract

“…Level of difficulty can be adjusted by changing the length of simulation time. However, too long simulation time is a serious defect in the MCT-based DDA [8]. The proceeding of game will become discontinuous when a long simulation time is set.…”

Section: Results Of Experimentsmentioning

confidence: 98%

“…Therefore, determination will be more accurate. But when the simulation time is long enough, the win-rates will become stable [8]. Using MCT approaches is a possible solution to implement DDA.…”

Section: Results Of Experimentsmentioning

confidence: 99%

Dynamic difficulty adjustment of game AI for video game Dead-End

Gao

et al. 2010

The 3rd International Conference on Information Sciences and Interaction Sciences

Self Cite

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To create a satisfactory game opponent is to optimize player's experience through creation of an dynamic balanced game, which means that win-rate of players is adjusted according to their ability. The most commonly used approach for generating satisfactory game opponent is Dynamic Difficulty Adjustment (DDA), which is to dynamically adjust challenge level of the opponent according to the player's skill level. However, DDA currently used is relatively simple and implementing DDA by adjusting opponent's intelligence is still challenging. In this paper, we propose to use Artificial Neural Network(ANN) to implement DDA and unsupervised learning methodologies to improve the performance of ANN. ANNcontrolled Non-Player Characters (NPC) can make "wise" decision based on collected attributes of all the characters in the game. Different ANNs can provide different win-rates for different player strategies, which can achieve the dynamic balance we expected and enhance the user experience of games.

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“…The Pacman game was used as a test-bed for this study. Considering that UCT is a computing intelligence method, UCT performance significantly correlates with the duration of simulation [37]. Figure 8 illustrates DDA process from UCT-created data.…”

Section: Upper Confidence Bound For Trees and Artificial Neuralmentioning

confidence: 99%

Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review

Zohaib

2018

Advances in Human-Computer Interaction

146

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Dynamic difficulty adjustment (DDA) is a method of automatically modifying a game’s features, behaviors, and scenarios in real-time, depending on the player’s skill, so that the player, when the game is very simple, does not feel bored or frustrated, when it is very difficult. The intent of the DDA is to keep the player engrossed till the end and to provide him/her with a challenging experience. In traditional games, difficulty levels increase linearly or stepwise during the course of the game. The features such as frequency, starting levels, or rates can be set only at the beginning of the game by choosing a level of difficulty. This can, however, result in a negative experience for players as they try to map a predecided learning curve. DDA attempts to solve this problem by presenting a customized solution for the gamers. This paper provides a review of the current approaches to DDA.

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To Create Intelligent Adaptive Game Opponent by Using Monte-Carlo for Tree Search

Cited by 7 publications

References 6 publications

A Survey of Monte Carlo Tree Search Methods

A Survey of Monte Carlo Tree Search Methods

Dynamic difficulty adjustment of game AI for video game Dead-End

Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review

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