社会学习模型以及智能实现的哲学解释
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摘要
人工智能研究从图灵测试开始发展迅速,目前研究的主要方法有:符号主义、连接主义、行为主义。作为模拟工具的计算机,目前的智能实质主要体现在速度上。为了研究具有真正意义的智能结构,本文第一次从存储逻辑的层次建立了社会学习模型,提出算法概况,提出人工智能是一个网络统一体的思想,并给出了逻辑证明。图灵思想:存储程序顺序执行,以及在此建立起来的冯诺依曼体系结构:输入-存储(记忆)-计算(逻辑)-输出,是电子计算机和人工智能的理论基础。本文始终以智能为中心,第一次从存储逻辑的哲学角度分析人工智能的研究内容,得到:输入-存储(记忆)-计算(逻辑)-输出的时间和空间的逻辑表达体现人类智能的重要结论,并给出了科学实例和严格的逻辑证明。
The research about artificial intelligence(AI) improves fast since Turing test. The main researching methods are symbolism, connectivism and actionism. Essential intelligence of computer as a simulation tool mainly lies in speed now.in the paper we propose a society learning model and algorithm overview algorithm overview on the level of stored logic for the first time and give the logical proof. Turing Thought: Stored-program implementation by sequence and Von Neumann Architecture:input-storage (memory)-compute (logic)-output is theoretical principle of computer and AI.In this paper,we propose that the logical expression ofinput-storage(memory)-compute(logic)-output at time and space reflects human intelligence. essentiality ofintelligence lies on philosophy model ,not lies on mathematical algorithm.
The research about artificial intelligence(AI) improves fast since Turing test. The main researching methods are symbolism, connectivism and actionism. Essential intelligence of computer as a simulation tool mainly lies in speed now.in the paper we propose a society learning model and algorithm overview algorithm overview on the level of stored logic for the first time and give the logical proof. Turing Thought: Stored-program implementation by sequence and Von Neumann Architecture:input-storage (memory)-compute (logic)-output is theoretical principle of computer and AI.In this paper,we propose that the logical expression ofinput-storage(memory)-compute(logic)-output at time and space reflects human intelligence. essentiality ofintelligence lies on philosophy model ,not lies on mathematical algorithm.
引文
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[2]吴胜,王书琴.人工智能基础与应用[M].北京:电子工业出版社,2007
[3]李德毅等.不确定性人工智能[M].北京:国防工业出版社,2005
[4]王志良.人工心理[M].北京:机械工业出版社,2007
[5]陈桦,麻风梅等.用多Agent实现基于XML的个性化学习系统[J].微计算机信息,2008,3-3:P250-254
[6]陈顒,陈凌.分形几何学[M].北京:地震出版社,2005
[7]郭红霞,吴捷,张瑞金,王春茹.多主体技术的研究进展[J].河南科学,2004
[8]张汝波,顾国昌,刘照德,王醒策.强化学习理论、算法及应用[J].控制理论与应用,2000
[9]Michael W,Nicholas RJ.Intelligent agents:theory and practice[J].Knowledge Engineering Review,1995
[10]张晓荣.试论人类学习的本质与机器学习[J].学术论坛,2009
[11]Bowling M.Convergence and no-regret in multiAgent learning[J].Advances in Naural Information Processing Systems,2004
[12]T.M.Mitchell.Machine Learning.The MaGraw-Hill Companies,1997
[13]张新良,石纯一.对称和非对称的启发式多Agent再励学习方法[J].清华大学学报,2006
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