FML-based type-2 fuzzy ontology for computer go knowledge representation

In this paper, an Fuzzy Markup Language (FML)-based type-2 fuzzy ontology is proposed to represent the computer Go knowledge, including an FML transformation mechanism, a type-2 fuzzy set construction, and a type-2 fuzzy set inference mechanism. The FML transformation mechanism transforms the generated smart game format (SGF) files into an FML-based document to describe the computer Go ontology. The type-2 set construction is responsible for building the type-2 fuzzy sets. Based on the built FML-based document and type-2 fuzzy sets, the type-2 fuzzy set inference mechanism infers the possibility of the game's winning rate. It is hoped that the proposed idea are feasible for inferring the winning rate of the games in the future.

[1]  Tzung-Pei Hong,et al.  A novel ontology for computer go knowledge management , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[2]  Isabelle Bloch,et al.  Fuzzy spatial relation ontology for image interpretation , 2008, Fuzzy Sets Syst..

[3]  Siu Cheung Hui,et al.  Automatic fuzzy ontology generation for semantic Web , 2006, IEEE Transactions on Knowledge and Data Engineering.

[4]  Troels Andreasen,et al.  Perspectives on ontology‐based querying , 2007, Int. J. Intell. Syst..

[5]  Paul Warren,et al.  Knowledge management and the semantic Web: from scenario to technology , 2006, IEEE Intelligent Systems.

[6]  H. Hagras,et al.  Type-2 FLCs: A New Generation of Fuzzy Controllers , 2007, IEEE Computational Intelligence Magazine.

[7]  Siu Cheung Hui,et al.  Automatic fuzzy ontology generation for semantic help-desk support , 2006, IEEE Transactions on Industrial Informatics.

[8]  Chong-Ching Chang,et al.  Ontology-based multi-agents for intelligent healthcare applications , 2010, J. Ambient Intell. Humaniz. Comput..

[9]  Jerry M. Mendel,et al.  Type-2 fuzzy sets and systems: an overview , 2007, IEEE Computational Intelligence Magazine.

[10]  Giovanni Acampora,et al.  Fuzzy control interoperability and scalability for adaptive domotic framework , 2005, IEEE Transactions on Industrial Informatics.

[11]  Marek Reformat,et al.  Ontological approach to development of computing with words based systems , 2009, Int. J. Approx. Reason..

[12]  Hani Hagras,et al.  A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation , 2010, IEEE Transactions on Fuzzy Systems.

[13]  Judy E. Scott,et al.  Comparing knowledge management in health-care and technical support organizations , 2005, IEEE Transactions on Information Technology in Biomedicine.

[14]  Chang-Shing Lee,et al.  Ontology-based Intelligent Decision Support Agent for CMMI Project Monitoring and Control , 2006, NAFIPS 2006.

[15]  J. Mendel Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[16]  Chang-Shing Lee,et al.  A fuzzy ontology and its application to news summarization , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Giovanni Acampora,et al.  Using Fuzzy Technology in Ambient Intelligence Environments , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..