10 citations · 20 across the 11 of their papers we have counts for
12 papers
Do LLMs Trust the Accuser or the Accusation? Measuring Belief Shifts in Werewolf
Yu-Yu Yang, Ti-Rong Wu, Hung Guei +2
Social-deduction games such as Werewolf are increasingly used to evaluate LLM agents, but existing evaluations often rely on final game outcomes. We propose a belief-shift evaluati…
MAPLE: Multi-State Aggregated Policy Evaluation for AlphaZero in Imperfect-Information Games
Qian-Rong Li, Hung Guei, I-Chen Wu +1
Imperfect-information games (IIGs) are challenging, as players must make decisions without fully observing the true game state. While AlphaZero has achieved remarkable success in p…
Evaluating Game Difficulty in Tetris Block Puzzle
Chun-Jui Wang, Jian-Ting Guo, Hung Guei +3
Tetris Block Puzzle is a single player stochastic puzzle in which a player places blocks on an 8 x 8 grid to complete lines; its popular variants have amassed tens of millions of d…
A Study of Solving Life-and-Death Problems in Go Using Relevance-Zone Based Solvers
Chung-Chin Shih, Ti-Rong Wu, Ting Han Wei +3
This paper analyzes the behavior of solving Life-and-Death (L&D) problems in the game of Go using current state-of-the-art computer Go solvers with two techniques: the Relevance-Zo…
Relevance-Zone Reduction in Game Solving
Chi-Huang Lin, Ting Han Wei, Chun-Jui Wang +5
Game solving aims to find the optimal strategies for all players and determine the theoretical outcome of a game. However, due to the exponential growth of game trees, many games r…
OptionZero: Planning with Learned Options
Po-Wei Huang, Pei-Chiun Peng, Hung Guei +1
Planning with options -- a sequence of primitive actions -- has been shown effective in reinforcement learning within complex environments. Previous studies have focused on plannin…
Solving 7x7 Killall-Go with Seki Database
Yun-Jui Tsai, Ting Han Wei, Chi-Huang Lin +4
Game solving is the process of finding the theoretical outcome for a game, assuming that all player choices are optimal. This paper focuses on a technique that can reduce the heuri…
Demystifying MuZero Planning: Interpreting the Learned Model
Hung Guei, Yan-Ru Ju, Wei-Yu Chen +1
MuZero has achieved superhuman performance in various games by using a dynamics network to predict the environment dynamics for planning, without relying on simulators. However, th…
Game Solving with Online Fine-Tuning
Ti-Rong Wu, Hung Guei, Ting Han Wei +3
Game solving is a similar, yet more difficult task than mastering a game. Solving a game typically means to find the game-theoretic value (outcome given optimal play), and optional…
MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games
Ti-Rong Wu, Hung Guei, Pei-Chiun Peng +4
This paper presents MiniZero, a zero-knowledge learning framework that supports four state-of-the-art algorithms, including AlphaZero, MuZero, Gumbel AlphaZero, and Gumbel MuZero.…
On Reinforcement Learning for the Game of 2048
Hung Guei
2048 is a single-player stochastic puzzle game. This intriguing and addictive game has been popular worldwide and has attracted researchers to develop game-playing programs. Due to…
Optimistic Temporal Difference Learning for 2048
Hung Guei, Lung-Pin Chen, I-Chen Wu
Temporal difference (TD) learning and its variants, such as multistage TD (MS-TD) learning and temporal coherence (TC) learning, have been successfully applied to 2048. These metho…