Using Counterfactual Regret Minimization and Monte Carlo Tree Search for Cybersecurity Threats

Cotae, Paul; Reindorf, Nii Emil Alexander

doi:10.1109/blackseacom52164.2021.9527857

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Third, the accuracy to predict opponent movements (i.e., played card sets of other three players) is particularly evaluated for AI 4.0 player. Finally, we conduct experiments to compare the win rates and remaining points of existing AI players (i.e., AI 1.0 [12], [19], AI 2.0 [32], [33], AI 3.0 [34], [35], [36], and RL-PPO [20]) and our developed AI player (i.e., AI 4.0). Furthermore, the games of AI players against human players are performed to compare their win rates and remaining points under different numbers of played games.…”

Section: Discussionmentioning

confidence: 99%

“…However, the cards in hand and the card sets played in a real game cannot be fully determined by those expected values that are only based on the number of cards in hand. To make simulated playing more close to real playing in AI 2.0, we further integrate regret minimization [32], [33] into AI 1.0 to dynamically calculate the expected values P 2.0 (instead of P 1.0 ) of card sets to be played based on historical game data. In particular, specific playing strategies can be further learned in AI 2.0 from game-playing data; for example, if there are three remaining cards including Spade 2, Diamond 10, and Club 10, Single of Spade 2 can be played first (which could make P1, P2, and P3 all pass their turns) and then Pair of Diamond 10 and Club 10 can be immediately played (as the new dominant card set) to win more points in the game (because no more cards can be played by P1, P2, and P3).…”

Section: B Ai 20 -Dynamic Weight Adjustmentmentioning

confidence: 99%

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Challenging Artificial Intelligence With Multiopponent and Multimovement Prediction for the Card Game Big2

Chen

2022

IEEE Access

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Big2 is one of the most popular card games in Chinese residential regions; however, there is lack of advanced computer players with challenging artificial intelligence. In this study, we propose the Big2 artificial intelligence (Big2AI) framework consisting of card superiority analysis, dynamic weight adjustment, game feature learning, and multi-opponent movement prediction based on Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS). According to our review of relevant research, this is the first artificial intelligence framework that can perform self-playing with various computer players, improve win rates through historical game features, and predict multiple movements of multiple opponents in the card game Big2. An Android-based prototype of four-player Big2 game is implemented to verify the feasibility and superiority of Big2AI. Experimental results show that Big2AI outperforms existing artificial intelligence and can achieve the highest win rate and the least losing points against computer and human players in Big2 games.

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Section: Discussionmentioning

confidence: 99%

Section: B Ai 20 -Dynamic Weight Adjustmentmentioning

confidence: 99%

Challenging Artificial Intelligence With Multiopponent and Multimovement Prediction for the Card Game Big2

Chen

2022

IEEE Access

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“…However, limited by computing resources and storage resources, the solution scale of CFR is not enough for large-scale games. When solving large-scale game problems, the large-scale game problems must be abstracted and expert knowledge is needed for the detailed design, which greatly limits its further application [8,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

NFSP-PLT: Solving Games with a Weighted NFSP-PER-Based Method

Li,

Qi,

Zhang

et al. 2023

Electronics

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Nash equilibrium strategy is a typical goal when solving two-player imperfect-information games (IIGs). Neural fictitious self-play (NFSP) is a popular method to find the Nash equilibrium in IIGs, which is the first end-to-end method used to compute the Nash equilibrium strategy. However, the training of NFSP requires a large number of sample data and the interactive cost of obtaining such data is often very high. Realizing the efficient training of network under limited samples is an urgent problem. In this paper, we first proposed a new NFSP-based method, NFSP with prioritized experience replay (NFSP-PER), to improve the sample training efficiency. Then, a weighted NFSP-PER with learning time (NFSP-PLT) was proposed to control the utilization degree of priority-weighted samples. Furthermore, based on the NFSP-PLT, an adaptive upper-confidence-bound applied to tree (UCT) is used to solve the optimal response strategy, which makes the solving strategy more accurate. Extensive experimental results show that the proposed NFSP-PLT effectively improves the sample learning efficiency compared with the existing works.

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Deep Counterfactual Regret Minimization Algorithm with Regret Discount in Radar Anti-Jamming Game

Xu,

Zhang,

Tian

2023

2023 IEEE 23rd International Conference on Communication Technology (ICCT)

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Using Counterfactual Regret Minimization and Monte Carlo Tree Search for Cybersecurity Threats

Cited by 3 publications

References 9 publications

Challenging Artificial Intelligence With Multiopponent and Multimovement Prediction for the Card Game Big2

Challenging Artificial Intelligence With Multiopponent and Multimovement Prediction for the Card Game Big2

NFSP-PLT: Solving Games with a Weighted NFSP-PER-Based Method

Deep Counterfactual Regret Minimization Algorithm with Regret Discount in Radar Anti-Jamming Game

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