Game Player Strategy Pattern Recognition and How UCT Algorithms Apply Pre-knowledge of Player's Strategy to Improve Opponent AI

He, Suoju; Wang, Yi; Xie, Fang; Liu, Chen; Chen, Hongtao; Luo, Sai; Liu, Zhiqing; Zhu, Qiliang

doi:10.1109/cimca.2008.82

Cited by 24 publications

(8 citation statements)

References 5 publications

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“…For example, Huang [106] used the concept of territory in Go to significantly increase the performance of the program LINGO against GNU GO 3.8. Domain knowledge related to predicting opponents' strategies was used by Suoju et al for move pruning in the game Dead End for a 51.17% improvement over plain UCT [99].…”

Section: Pruning With Domain Knowledgementioning

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: Pruning With Domain Knowledgementioning

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

“…Find the value of time-limit of simulation (21.0149ms in this case) which has a win-rate of 50% from the above regression function (because the Pac-Man Game, which has 50% win-rate, is more likely to be an even game). Reapply time-limit of simulation (21.0149ms in this case) to DTS-controlled NPCs (Pac-Man) to PK the Strategy-B [9] player. The resulted win-rate for opponent game AI is 52%, which is close to what we expected of 50%.…”

Section: Implementation Of Dda By "Time-constrained-dts" In the Pac-mmentioning

confidence: 99%

Dynamic difficulty adjustment based on an improved algorithm of UCT for the Pac-Man Game

Chen

et al. 2011

2011 International Conference on Electronics, Communications and Control (ICECC)

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one major problem of Pac-Man Game is how to generate intelligence of opponents at appropriate challenge level. This paper presents an approach for creating game opponents by DDA (Dynamic Difficulty Adjustment), which is used to dynamically adjust challenge level of opponents according to the player's skill level. The approach of DDA is based on "timeconstrained-CI". Furthermore, in order to improve the performance of Pac-Man Game AI, this paper also discusses an alternative approach, knowledge-based-CI-controlled-NPC", which creates DDA based on knowledge from DTS.

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“…Several heuristic-based pruning mechanisms is compared in this section. The idea is inspired by the work in [23], [24], [25] where the authors utilized domain knowledge for pruning. We present the descriptions of the heuristics based on Algorithm 8 in Table IV.…”

Section: B Heuristic Based Tree Pruningmentioning

confidence: 99%

Monte Carlo Tree Search in Finding Feasible Solutions for Course Timetabling Problem

Goh¹,

Kendall²,

Sabar³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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We are addressing the course timetabling problem in this work. In a university, students can select their favorite courses each semester. Thus, the general requirement is to allow them to attend lectures without clashing with other lectures. A feasible solution is a solution where this and other conditions are satisfied. Constructing reasonable solutions for course timetabling problem is a hard task. Most of the existing methods failed to generate reasonable solutions for all cases. This is since the problem is heavily constrained and an effective method is required to explore and exploit the search space. We utilize Monte Carlo Tree Search (MCTS) in finding feasible solutions for the first time. In MCTS, we build a tree incrementally in an asymmetric manner by sampling the decision space. It is traversed in the best-first manner. We propose several enhancements to MCTS like simulation and tree pruning based on a heuristic. The performance of MCTS is compared with the methods based on graph coloring heuristics and Tabu search. We test the solution methodologies on the three most studied publicly available datasets. Overall, MCTS performs better than the method based on graph coloring heuristic; however, it is inferior compared to the Tabu based method. Experimental results are discussed.

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Game Player Strategy Pattern Recognition and How UCT Algorithms Apply Pre-knowledge of Player's Strategy to Improve Opponent AI

Cited by 24 publications

References 5 publications

A Survey of Monte Carlo Tree Search Methods

A Survey of Monte Carlo Tree Search Methods

Dynamic difficulty adjustment based on an improved algorithm of UCT for the Pac-Man Game

Monte Carlo Tree Search in Finding Feasible Solutions for Course Timetabling Problem

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