Analogical Reasoning in Ink Analysis Computer Management System

First, the basic idea and solution model

The heuristic search technology based on analogy reasoning is to obtain the solution process of the past problem similar to the new problem through analogy, as the heuristic information to guide the solution of the new problem, which can reduce the blindness of the search, narrow the search scope, and reduce Solve the difficulty of the problem.

In general, heuristic search based on analogical reasoning directly applies the case library as the search space. The result of this processing will lead to two problems:

1. The performance of the system will continue to decrease during the operation of the system. With the increase of the number of solutions, the continuous accumulation of solution cases, the search space will continue to expand, although this can make the system have ample opportunities to obtain the same solution problems with the past problems, but it also leads to heuristic search information. Saturation reduces system efficiency and performance.

2. The performance of the system will depend to a large extent on the compromise between search costs and case inspiration. The contents of the casebase record are incomplete, which can reduce the requirements for the degree of similarity of the case. The matching process may be easier, but the enlightening effect of the case guidance solution in the past will be weakened; the content of the case library record is too detailed, and the degree of similarity of the case is required. It is relatively high, the matching process is difficult, the search cost is increased, and the system may also rely on weak methods to solve the problem.

In real life, human beings do not store their past experience in the mind, but use a common data structure to store it. That is, there is a process of mining useful information from past case data. This is an effective way to solve problems, accumulate experience, and enhance problem solving skills. The solution method considered in this paper is to use data mining techniques to abstractly abstract the association rules with hierarchical structure from a large number of case database data. The system solves the problem by searching for matching rules from the rule base. Since data mining produces hierarchical information for solution, the system can reduce the search cost while minimizing the impact of information loss and maintain the system's search quality, thus ensuring that the system's solution efficiency increases with the increase of operating time. Will continue to improve. The solution model is shown in Figure (Solution Model).

The empirical knowledge accumulated in the past can be divided into two categories: shallow case bases and rule bases that are generated through data mining in the case base. At the beginning of system operation, there is a lack of solving cases in the case library. There are a few simple past experience rules in the rule base, so the system must use weak methods to search for solutions. After the system has accumulated certain case experiences, the solution to the new problem is first searched in the rule base. If the rules are applied successfully,

In the figure: The case base records the solution process of past cases; the rule bank stores the processing rules generated after abstracting the data in the case database; the control strategy specifies how to determine the similarity of events; and the analogy reasoning mechanism stipulates how the specific similarity Conduct business processing, and complete the addition, modification, and deletion of case libraries, rule bases, and control strategies.

The solution is successful. Otherwise, the search case base, if there is a similar problem solving, is used as a heuristic information to guide the solution of the new problem. If there is still no similar solution to the past problems, the weak method is used to solve and the new problem solving process is added to the case library. The more experience accumulated in the past, the more effective the application of the rule base; the higher the degree of similarity between the new problem and the past problem, the fewer search times in the solution process.

Second, system implementation

In the implementation process, we need to solve 1 how to generate solution cases; 2 how to define and judge the similarity of two cases; 3 how to abstract general rules from a large number of cases; 4 how to maintain the rule base and case base; 5 How to use heuristic information to guide new issues and solve five issues.

When generating a solution case, during the process of recording and saving the problem solving, search for future solutions to search for feature information that is heuristic. The feature information defining the search process includes the sequence of operations on the search path and the state value of the solution problem at the time of selecting each operation. Due to the introduction of the rule base, feature information can be described as exhaustively as possible. In the OACMS, the characteristic information of the search process includes a description of the processing operation performed on the ink, the influencing factors of performing the processing operation, and the change of the index of the ink after each treatment.

Due to the need to find the case guidance solution with the highest degree of similarity to the new problem in the case base, it is necessary to determine the degree of similarity between the two cases. OACMS determines the degree of similarity of two cases by assigning weights to each feature information. The search cost for finding the best similarity matching process will be higher when the case pool is inflated. Therefore, the system introduces rule base to reduce the cost. Search fee.

If the occurrence of a certain type of case is more frequent, using a case library to search for a specific case to deal with, will increase the search cost effectively, using a generalized example of a certain type, solve the problem according to general rules, can be reasonably reduced Search costs, without compromising guidance on solving new problems. In the OACMS, the author uses the method of dividing the attribute value of the feature information into certain segments and then mapping it into Boolean, and obtains generalized rules of certain degree of support and certain credibility. Confidence constraint generalization rules represent the frequency of occurrence of a certain type of cases, and it is only necessary to provide generalization rules for cases that reach a certain degree of frequency; the degree of support constraints generalizes the accuracy of the rules, only to a certain degree of accuracy. The rules can guide the problem solving.

Maintenance operations on the case base and rule base include additions, modifications, and deletions. There are two cases of search for the case base, search success and search failure. If the search fails, the new problem is solved by using a weak method to directly fill in the feature information in the process of solving the problem and complete the increase of the case library. The search success will produce two kinds of situations, one is that the feature information is exactly the same, and the solution path is the same; in another case, the analogical reasoning may fail because the feature information cannot cover all the factors that affect the problem solving. In the second case, the casebase needs to be modified and the solution to the new problem added to the casebase as a new case. In OACMS, reward and penalty functions are used to determine which solution path to use to guide future solutions to similar problems. Similarly, a reward and penalty function and a certain threshold are used to determine if a case needs to be deleted to reduce future invalid search costs. The reason for the maintenance of the rule base mainly comes from changes in the case base, changes in support, and credibility. Because the update of the rule base requires scanning of the entire case base, it is not appropriate to use the change of the case base to trigger the update of the rule base. For this purpose, the OACMS uses the work mode of regularly updating the rule base.

The main problem of using heuristic information to guide the solution of new problems is to solve the problem of mismatch in the solution process. If the search process cannot obtain heuristic information, then the weak method is used to solve and the case library is added; if the search process obtains exactly the same heuristic information but the solution fails, the rewards and penalties are used to extend the case as a counterexample to the original solution case; if the search The process obtains partially similar heuristic information (it is not appropriate to apply rules to partially similar situations), heuristic information and weak methods are used to solve, and new cases are added to the case base.

Xi'an University of Technology Liu Zhipeng and Ke Zhi Xu Shaohua

Source: Guangdong Packaging