AlphaDDA: strategies for adjusting the playing strength of a fully trained AlphaZero system to a suitable human training partner.

PeerJ Computer Science Pub Date : 2022-10-04 eCollection Date: 2022-01-01 DOI:10.7717/peerj-cs.1123
Kazuhisa Fujita
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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. AlphaDDA can weaken itself according to the estimated value. However, AlphaDDA beats the random player because AlphaDDA is stronger than a random player even if AlphaDDA weakens itself to the limit. 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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AlphaDDA:将经过充分训练的AlphaZero系统调整为合适的人类训练伙伴的策略。
人工智能(AI)在围棋、国际象棋、奥赛罗(逆转)等棋类游戏中取得了超人的成绩。换句话说,AI系统在这类游戏中超过了强大的人类专家玩家的水平。在这种情况下,人类玩家很难享受与AI一起玩游戏的乐趣。为了让人类玩家沉浸在游戏中,AI需要动态平衡自己与人类玩家的技能。为了解决这个问题,我们提出了AlphaDDA,一个基于alphazero的动态难度调整(DDA)人工智能。AlphaDDA由深度神经网络(DNN)和蒙特卡罗树搜索组成,就像AlphaZero一样。AlphaDDA学习和下棋的方式与AlphaZero相同,但它可以改变自己的技能。AlphaDDA使用深度神经网络仅从棋盘状态估计游戏状态的值。AlphaDDA根据估计值改变控制其技能的主要参数。因此,AlphaDDA会根据游戏状态调整自己的技能。AlphaDDA可以在不事先了解对手的情况下,仅使用游戏状态来调整其技能。在本研究中,AlphaDDA与其他AI代理一起玩Connect4、奥赛罗和6x6奥赛罗。其他人工智能代理有AlphaZero、蒙特卡洛树搜索、极大极小算法和随机玩家。这项研究表明,除了随机玩家之外,AlphaDDA可以与其他AI代理平衡其技能。AlphaDDA可以根据估计值进行自我削弱。然而,AlphaDDA击败了随机玩家,因为AlphaDDA比随机玩家更强大,即使AlphaDDA将自己削弱到极限。AlphaDDA的DDA能力是基于对游戏状态值的准确估计。我们相信,如果DNN能够准确地估计游戏状态的值,并且我们知道控制AI系统技能的参数,那么用于DDA的AlphaDDA方法可以用于任何游戏AI系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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