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AlphaDDA: Strategies for Adjusting the Playing Strength of a Fully Trained AlphaZero System to a Suitable Human Training Partner
Machine Learning · Submitted 11 Nov 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This study shows that AlphaDDA can balance its skill with that of the other AI agents, except for a random player.
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The abstract, sentence by sentence
Artificial intelligence (AI) has achieved superhuman performance in board games such as Go, chess, and Othello (Reversi). In other words, the AI system surpasses the level of a strong human expert player in such games. In this context, it is difficult for a human player to enjoy playing the games with the AI. To keep human players entertained and immersed in a game, the AI is required to dynamically balance its skill with that of the human player. To address this issue, we propose AlphaDDA, an AlphaZero-based AI with dynamic difficulty adjustment (DDA). AlphaDDA consists of a deep neural network (DNN) and a Monte Carlo tree search, as in AlphaZero. AlphaDDA learns and plays a game the same way as AlphaZero, but can change its skills. AlphaDDA estimates the value of the game state from only the board state using the DNN. AlphaDDA changes a parameter dominantly controlling its skills according to the estimated value. Consequently, AlphaDDA adjusts its skills according to a game state. AlphaDDA can adjust its skill using only the state of a game without any prior knowledge regarding an opponent. In this study, AlphaDDA plays Connect4, Othello, and 6x6 Othello with other AI agents. Other AI agents are AlphaZero, Monte Carlo tree search, the minimax algorithm, and a random player. This study shows that AlphaDDA can balance its skill with that of the other AI agents, except for a random player. The DDA ability of AlphaDDA is based on an accurate estimation of the value from the state of a game. We believe that the AlphaDDA approach for DDA can be used for any game AI system if the DNN can accurately estimate the value of the game state and we know a parameter controlling the skills of the AI system.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Artificial intelligence (AI) has achieved superhuman performance in board games such as Go, chess, and Othello (Reversi). | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | In other words, the AI system surpasses the level of a strong human expert player in such games. | background 0.89 | 0.05 | 0.02 | asserted 1.00 |
| s2 | In this context, it is difficult for a human player to enjoy playing the games with the AI. | background 0.91 | 0.08 | 0.02 | asserted 0.99 |
| s3 | To keep human players entertained and immersed in a game, the AI is required to dynamically balance its skill with that of the human player. | background 0.89 | 0.05 | 0.04 | asserted 0.99 |
| s4 | To address this issue, we propose AlphaDDA, an AlphaZero-based AI with dynamic difficulty adjustment (DDA). | contribution 0.99 | 0.79 | 0.08 | asserted 1.00 |
| s5 | AlphaDDA consists of a deep neural network (DNN) and a Monte Carlo tree search, as in AlphaZero. | method 0.97 | 0.37 | 0.03 | asserted 1.00 |
| s6 | AlphaDDA learns and plays a game the same way as AlphaZero, but can change its skills. | method 0.82 | 0.51 | 0.05 | asserted 1.00 |
| s7 | AlphaDDA estimates the value of the game state from only the board state using the DNN. | method 1.00 | 0.52 | 0.03 | asserted 1.00 |
| s8 | AlphaDDA changes a parameter dominantly controlling its skills according to the estimated value. | method 0.99 | 0.72 | 0.06 | asserted 1.00 |
| s9 | Consequently, AlphaDDA adjusts its skills according to a game state. | method 0.79 | 0.70 | 0.14 | asserted 1.00 |
| s10 | AlphaDDA can adjust its skill using only the state of a game without any prior knowledge regarding an opponent. | method 0.54 | 0.73 | 0.12 | asserted 0.64 |
| s11 | In this study, AlphaDDA plays Connect4, Othello, and 6x6 Othello with other AI agents. | method 0.80 | 0.93 | 0.03 | asserted 0.97 |
| s12 | Other AI agents are AlphaZero, Monte Carlo tree search, the minimax algorithm, and a random player. | method 0.95 | 0.69 | 0.02 | asserted 0.99 |
| s13 | This study shows that AlphaDDA can balance its skill with that of the other AI agents, except for a random player. | result 1.00 | 0.94 | 0.53 | asserted 1.00 |
| s14 | The DDA ability of AlphaDDA is based on an accurate estimation of the value from the state of a game. | interpretation 0.91 | 0.69 | 0.24 | asserted 1.00 |
| s15 | We believe that the AlphaDDA approach for DDA can be used for any game AI system if the DNN can accurately estimate the value of the game state and we know a parameter controlling the skills of the AI system. | interpretation 0.69 | 0.15 | 0.51 | hedged possibility 0.77 |
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The paper
Original abstract
Artificial intelligence (AI) has achieved superhuman performance in board games such as Go, chess, and Othello (Reversi). In other words, the AI system surpasses the level of a strong human expert player in such games. In this context, it is difficult for a human player to enjoy playing the games with the AI. To keep human players entertained and immersed in a game, the AI is required to dynamically balance its skill with that of the human player. To address this issue, we propose AlphaDDA, an AlphaZero-based AI with dynamic difficulty adjustment (DDA). AlphaDDA consists of a deep neural network (DNN) and a Monte Carlo tree search, as in AlphaZero. AlphaDDA learns and plays a game the same way as AlphaZero, but can change its skills. AlphaDDA estimates the value of the game state from only the board state using the DNN. AlphaDDA changes a parameter dominantly controlling its skills according to the estimated value. Consequently, AlphaDDA adjusts its skills according to a game state. AlphaDDA can adjust its skill using only the state of a game without any prior knowledge regarding an opponent. In this study, AlphaDDA plays Connect4, Othello, and 6x6 Othello with other AI agents. Other AI agents are AlphaZero, Monte Carlo tree search, the minimax algorithm, and a random player. This study shows that AlphaDDA can balance its skill with that of the other AI agents, except for a random player. The DDA ability of AlphaDDA is based on an accurate estimation of the value from the state of a game. We believe that the AlphaDDA approach for DDA can be used for any game AI system if the DNN can accurately estimate the value of the game state and we know a parameter controlling the skills of the AI system.
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