A Greedy Algorithm for Detecting Mutually Exclusive Patterns in Cancer Mutation Data

Yang, Ching‐Chieh; Tian, Zheng; Zhao, Zhongmeng; He, Xinnuo; Zhang, Xuanping; Xiao, Xiao; Wang, Jiayin

doi:10.1007/978-3-030-17938-0_15

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…To create an agent with the ability to play the SOS game, the researcher proposes the greedy strategy or greedy algorithm with some modification. Nowadays, the greedy strategy is still used in many studies like sensor placement [5], task scheduling [6], and detecting a mutually exclusive pattern in cancer mutation data [7]. The greedy strategy is also effective to be used in digital or board games, such as a card game [8], an educational game [9], and a puzzle game [10].…”

Section: Introductionmentioning

confidence: 99%

Playing the SOS Game Using Feasible Greedy Strategy

Setiawan

2020

CommIT (Communication and Information Technology) Journal

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The research aims to make an intelligent agent that can compete against the human player. In this research, the feasible greedy strategy is proposed to make an intelligent agent by checking all possible solutions in the limited tree levels to find effective movement. Several matches are conducted to evaluate the performance of the feasible greedy agent. The board size for the evaluation consists of 33, 44, 55, 66, 77, and 88 squares. From the result, the feasible greedy agent never loses against the random agent and the pure greedy agent. In 3 3 squares match, the agent can compensate against the human player, so the game always ends with a draw. In 44, 55, 66, 77, and 88 squares matches, the feasible greedy agent slightly outplays the human player.

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