From AlphaGo to BetaGo — Quantitative realization of qualitative artificial intelligence based on task realizability analysis
SU Jian-bo
CHEN Ye-fei
MA Zhe
HUANG Yao
XIANG Guo-fei
CHEN Ruo-bing
Abstract：AlphaGo,using deep learning and Monte Carlo Tree searching algorithrns,has achieved great progress in the game of Go,which realized the qualitative task by quantitative analysis.Such progress has realized the quantitative research of qualitative AI,which has a significant reference value for researchers.For AI,task realizability should be taken into consideration before executing the task.The task goal of the game of Go is winning of the game.Therefore,AlphaGo is firstly analyzed in the aspect of task realizability,including feature completeness,establishment of representation space and searching method based on representation space.Secondly,during the process of task executing,AI will confront various disturbance inevitably.The essence of AlphaGo is modeling the process of playing chess of human.Hence,the drawbacks of AlphaGo is analyzed in the aspect of disturbance rejection.Thirdly,the research of AI is a simulation of human brain activity by scientific technology.The beauty evaluation difference between AlphaGo and human player reflects the difference between quantitative analysis and qualitative description.Therefore,AlphaGo is analyzed and prospected in the aspect of beauty evaluation.In this paper,the principle of AlphaGo and the significant reference value of quantitative realization of qualitative AI are annotated through the three aspects mentioned above.Though AlphGo has achieved remarkable progress,we consider that there are still plenty of problems remain to be studied in aspects of qualitative description (e.g.beauty evaluation,art) and unknown disturbance of system.The significant progress of AI should contains the ability of qualitative analysis,which is an leap from Alpha level to Beta level.At last,the analysis in this paper is supposed to make AI researchers pay more attention to dig the relationship between qualitative description and quantitative analysis and cnhance the AI to BetaGo and higher level.
Keywords：AlphaGofeature completenessrepresentation spacetask realizabilitydisturbance rejectionbeauty evaluationartificial intelligence
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 1572-1583 )