Ontology-Based Planning and Execution of Fuzzy Inference Flow

• Main Authors: Chih-Chun Chiu, 邱智淳
• Other Authors: Chih-Min Fan
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/55091005826365389809

碩士 === 元智大學 === 工業工程與管理學系 === 97 === 　　“Knowledge” is the main driving force for innovation, advancement, and competitiveness in the 21st century knowledge-based economy system. The challenges of which the industry will have to face in this economy system include, integrating scattered know-how, developing a simulated AI system for decision making, applying the system to individual organizations, raising the value of the system, and limiting a waste of corporate resources. As a result, a system with an expert system as the backbone infrastructure was proposed, known as Ontology-Based Fuzzy Inference (OFI) system, which is also the first generation OFI system. 　　This thesis proposes a second generation OFI system infrastructure using the Rule Based Fuzzy Cognitive Maps (RBFCM) as the method of inference, and further expands to include multiple dynamic fuzzy inferences for the different initial conditions of incorrectly diagnosed semiconductor failures. The second generation OFI system is formulated around the foundation built by the first generation OFI system and focuses on constructing the knowledge base module for different application fields. This research also inspects the amount of support on the methods of multiple dynamic fuzzy inferences provided by the original system objects, for effective improvements on system application and flexibility aspects. 　　The development of second generation OFI system is two-fold: Integrated OFI Ontology and Configurable System Object. The purpose of Integrated OFI Ontology is for knowledge sharing and reuse, which is enabled by using Web Ontology Language (OWL) to construct and integrate the knowledge from different fields of the industry, fuzzy theories, and inference methods. Configurable System Object is for the manipulation of various inference methods from the knowledge database. Users will be able to select the most appropriate inference method for different problems, or they can fabricate their own solution. 　　This research aims at designing formal specifications of Integrated OFI Ontology and developing Configurable System Object to support multiple dynamic fuzzy inferences. Three main issues of the OFI system are first addressed: (1) System Function Object is not reusable: improper design leading to identical logical concepts that cannot be covered by a single object, resulting in the waste of system resources; (2) System Data Object is not flexible during declaration: Data Ontology and Data Object cannot be updated in sync, once data ontology has been modified, the data object will also have to be changed, thereby resulting in information disconnect error or issues with system flexibility; (3) Unable to support the dynamical planning and execution of multiple OFI instances: even though the first generation OFI system might be able to complete the RBFCM inference through configuration of the process knowledge base, changes to the element type or dependencies will require manual re-planning of the inference process knowledge, of which the entire process is time consuming. 　　Three design improvements are proposed to counteract the problems pointed out above and to improve system flexibility on the execution of the RBFCM inference based on different conditions. The three designs are (1) Flexible Function Object: functions with identical concepts correspond to a single function object, thereby reducing function objects of duplicate concepts, improving system reliability and flexibility; (2) Generic Data Object: a 2D matrix using vectors is defined as the generic form of data objects, where Data Ontology and Data Object may be updated in sync thereby improve system flexibility; (3) Multiple OFI Flow Planner: the system will be able to automatically plan and execute multiple fuzzy inferences, as well as evaluate the changes in number or dependencies of factors without the need to reconfigure the inference process flow. 　　Implementing the three design changes mentioned above to the system and applying to the following five scenarios: two factor types – single path, three factor types – single path, three factor types – multiple path, influence of quantity of factor types on time, and influence of quantity of single factor types on time. The former three scenarios may be used to verify the practicability and expandability of the system. If the correct planning and execution results may be expected after the expansion of factors or paths, then the system is proved to be capable of supporting the planning and execution of multiple dynamic fuzzy inferences, as well as proving the expandability of the system. The latter two scenarios may be used to inspect the influence of quantity of factor types and single factor types on system execution time, where system execution time will see a rise with an increase of factors. In summary, enterprises will be able to adopt this system to each of their individual operating organizations for sharing and reusing of the knowledge database. Valuable corporate resources will no longer be wasted and system development costs can also be reduced.