模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Pattern Recognition and Artificial Intelligence
2015年
9期
769-780
,共12页
概率逻辑%概率赋值%概率真度%不可靠度%近似推理
概率邏輯%概率賦值%概率真度%不可靠度%近似推理
개솔라집%개솔부치%개솔진도%불가고도%근사추리
Probability Logic%Probability Valuation%Probability Truth Degree%Uncertainty Degree%Approximate Reasoning
将命题逻辑的赋值域由二值{0,1}推广到给定的概率空间,引进命题公式的概率赋值,概率赋值是经典命题逻辑赋值及各种真度概念的推广.利用概率赋值引入命题公式的概率真度、不可靠度、基于独立事件赋值集的概率真度等概念,通过讨论概率真度的性质,表明概率真度在全体命题公式集F( S)上满足Kolmogorov公理.证明全部命题公式基于独立事件赋值集的真度之集在[0,1]中无孤立点,以及在命题逻辑形式推演中,一个有效推理结论的不可靠度不超过各前提的不可靠度与其必要度的乘积之和等结论.在概率赋值的基础上,引进命题公式集的a. e.结论、依概率结论、依概率真度结论等概念,讨论这些概念之间的联系,并提出两个不同类型的近似推理模式.
將命題邏輯的賦值域由二值{0,1}推廣到給定的概率空間,引進命題公式的概率賦值,概率賦值是經典命題邏輯賦值及各種真度概唸的推廣.利用概率賦值引入命題公式的概率真度、不可靠度、基于獨立事件賦值集的概率真度等概唸,通過討論概率真度的性質,錶明概率真度在全體命題公式集F( S)上滿足Kolmogorov公理.證明全部命題公式基于獨立事件賦值集的真度之集在[0,1]中無孤立點,以及在命題邏輯形式推縯中,一箇有效推理結論的不可靠度不超過各前提的不可靠度與其必要度的乘積之和等結論.在概率賦值的基礎上,引進命題公式集的a. e.結論、依概率結論、依概率真度結論等概唸,討論這些概唸之間的聯繫,併提齣兩箇不同類型的近似推理模式.
장명제라집적부치역유이치{0,1}추엄도급정적개솔공간,인진명제공식적개솔부치,개솔부치시경전명제라집부치급각충진도개념적추엄.이용개솔부치인입명제공식적개솔진도、불가고도、기우독립사건부치집적개솔진도등개념,통과토론개솔진도적성질,표명개솔진도재전체명제공식집F( S)상만족Kolmogorov공리.증명전부명제공식기우독립사건부치집적진도지집재[0,1]중무고립점,이급재명제라집형식추연중,일개유효추리결론적불가고도불초과각전제적불가고도여기필요도적승적지화등결론.재개솔부치적기출상,인진명제공식집적a. e.결론、의개솔결론、의개솔진도결론등개념,토론저사개념지간적련계,병제출량개불동류형적근사추리모식.
The value domain of proposition logic is extended from two values{0,1} to a probability space, and hence the concept of probability valuation of propositional formulas is introduced. Probability valuation is a generalization of classical propositional valuation and various truth degrees. Based on probability valuation, the concepts of probability truth degree, uncertainty degree, probability truth degree based on the set of all probability valuation of formulas on independent events are introduced. Grounded on the discussion of the properties of probability truth degree, probability truth degree satisfies Kolmogorov axioms on the entire set of propositional formulas. It is proved that the set of probability truth degrees of all formulas based on the set of all probability valuation on independent events has no isolated points in [0,1]. In the form of deduction in propositional logic, the uncertainty degree of conclusion is less than or equal to the sum of the product of uncertainty degree of each premise and its essentialness degree in a formal inference. Based on probability valuation, some concepts of a. e. conclusion, conclusion in <br> probability and conclusion in probability truth of a formula set are introduced, and the relations between these concepts are discussed. Moreover, two different approximate reasoning models based on probability valuation are proposed.