Inference, Logical Reasoning Based On The Probability Of The Conditional Event Algebra And Probability Logic Derivatives

Based on probability theory and modern deductive logic, probability logic is used as a tool to construct the logic of deductive system of the formal inductive reasoning. With the development of computer technology, it is becoming an important development direction of artificial intelligence, decision analysis, knowledge discovery and reasoning applications.In expert systems of artificial intelligence, existing data or observations are used in reasoning application and provided as a scientific basis for the practical analysis, forecasting and decision-making. The probabilistic causal relationship between variables of the data can be used to describe the interdependence of various properties of data, and the uncertainty of the interdependence. Using these relations to process logic reasoning, we can get a new, more complex causal relationship. The presentation, data mining, reasoning and application of probabilistic causal relationship in data are being attention by many researchers, and become the important field of the intelligent data analysis, knowledge discovery and research of uncertainty artificial intelligence.As an effective tool for describing the probabilistic causal relationship between variables, Bayesian networks are widely used in knowledge discovery, decision support, predictive analysis and other areas. However, in the applications of the financial trend analysis, medical diagnostics forecasts, real-time traffic control, etc., classic Bayesian networks can not meet better the needs of these applications for its accurate representation and reasoning mechanism. At the same time, in many cases of applications, users often do not care too precise quantitative knowledge, but only a qualitative description of knowledge required. Normally, the representation of the observed data and reasoning results is described by point probability or interval probability, but the reasoning is a logical process. For complex reasoning, the calculation of directly using Bayesian networks usually takes a lot of transformation of the whole probability and conditional probability. Whether these calculations can ensure that the probability and logical consistency in the reasoning process, complex reasoning, how the complex reasoning problem can be transformed equivalent to the reasoning of the relatively simple issues, these are difficulties of using Bayesian networks in practical application.Based on the methods of probabilistic logical reasoning on Bayesian networks, the general conditions probability can be used to describe the normal conditional event by extending the classical probability space with the conditional event algebra. By extending normal measurable space with conditional event, we can bring logic consistent with probability in denoting conditional probability information, and then we transform a higher-order conditional event to normal events and corresponding logical combination events via Conditional Event Algebra. By this way, we can implement the higher-order complex logic reasoning.In this paper, we research the key technologies of reasoning based on conditional event algebra and Bayesian Network for the needs of actual application, and use this method to the corporate culture survey analysis. We experimentally verify the effectiveness of the proposed method in this paper, and design the corresponding prototype system.The main work and innovations in this paper are summarized as following:●We research the method of probabilistic logic reasoning by using conditional event.Based on the conditional event algebra, we extend the normal measurable space with conditional event, and bring logic consistent with probability in denoting conditional probability information, and implement the probabilistic logic reasoning method of high-order conditional events on Bayesian Networks. By extending common space with condition event, using properties of the condition event, we extend the scalability of the conditional probability for converting high-order conditions into a lower-order terms, and propose a method for the high-order inference problem transformed into ordinary events and the corresponding connection events for reasoning problems. To carry out complex reasoning, we provide an effective support technology: the present method not only has been offset to some extent of the present probabilistic logical reasoning method, but also make up for the problem that existed in complex reasoning on Bayesian networks.●We research the forward and backward probability logic method of high-order conditionals by using condition event algebra.Based on the theories of influence diagram and fuzzy set, we utility conditional event to denote the casual relationship of decision-making process, and we use the theories of fuzzy set to extend point probability to interval probability. To resolve the real application of decision-making, we implement the forward and backward probability logic reasoning of complex decision-making problems.●We research the method of entailment in probabilistic logic reasoning. Based on a general probabilistic logic representation and reasoning methods, we propose the idea of dividing the set of known premise divided into different credibility sub-sets. When these sub-sets satisfy a certain threshold value, the corresponding conclusions also satisfy a certain threshold value, then we call this conclusion is credible.In our research, we describe the basic concepts and the corresponding attributes of such entailment relations, and give the illustrative of this entailment relationship. And we combine the methods of pattern recognition with fuzzy sets to divide a set known premise into different sub-sets. According to the constraint method of entailment relationship, we obtain the credibility threshold values of the conclusions, and complete the process of logic entailment.In this paper, we bring the logic consistent with the probability in denoting rules by extending normal measurable space with conditional event, and propose the reasoning methods form condition to result and result to condition in decision problem, and the corresponding fuzzy reasoning methods. The methods of pattern recognition and the partition of premises based on entailment reasoning used in this paper and other related works have not been reported.The methods of pattern recognition and the partition of premises based on entailment reasoning used in this paper and other related works have not been reported.