DeepAI AI Chat
Log In Sign Up

AlphaDDA: game artificial intelligence with dynamic difficulty adjustment using AlphaZero

11/11/2021
by   Kazuhisa Fujita, et al.
0

An artificial intelligence (AI) player has obtained superhuman skill for games like Go, Chess, and Othello (Reversi). In other words, the AI player becomes too strong as an opponent of human players. Then, we will not enjoy playing board games with the AI player. In order to entertain human players, the AI player is required to balance its skill with the human player's one automatically. To address this issue, I propose AlphaDDA, an AI player with dynamic difficulty adjustment based on AlphaZero. AlphaDDA consists of a deep neural network (DNN) and Monte Carlo tree search like AlphaZero. AlphaDDA estimates the value of the game state form only the board state using the DNN and changes its skill according to the value. AlphaDDA can adjust AlphaDDA's skill using only the state of a game without prior knowledge about an opponent. In this study, AlphaDDA plays Connect4, 6x6 Othello, which is Othello using a 6x6 size board, and Othello with the other AI agents. The other AI agents are AlphaZero, Monte Carlo tree search, Minimax algorithm, and a random player. This study shows that AlphaDDA achieves to balance its skill with the other AI agents except for a random player. AlphaDDA's DDA ability is derived from the accurate estimation of the value from the state of a game. We will be able to use the approach of AlphaDDA for any games in that the DNN can estimate the value from the state.

READ FULL TEXT
11/29/2020

Monte Carlo Tree Search for a single target search game on a 2-D lattice

Monte Carlo Tree Search (MCTS) is a branch of stochastic modeling that u...
11/01/2021

Gomoku: analysis of the game and of the player Wine

Gomoku, also known as five in a row, is a classical board game, ideally ...
08/04/2019

Monte-Carlo Tree Search for Simulation-based Strategy Analysis

Games are often designed to shape player behavior in a desired way; howe...
03/30/2016

Phoenix: A Self-Optimizing Chess Engine

Since the advent of computers, many tasks which required humans to spend...
10/31/2010

A Distributed AI Aided 3D Domino Game

In the article a turn-based game played on four computers connected via ...
03/21/2019

Biasing MCTS with Features for General Games

This paper proposes using a linear function approximator, rather than a ...
12/11/2019

UCT-ADP Progressive Bias Algorithm for Solving Gomoku

We combine Adaptive Dynamic Programming (ADP), a reinforcement learning ...