RAMOLI: A generic knowledge-based systems shell for symbolic data

Non classical logics were introduced to allow handling imperfect concepts in intelligent systems. One of the principal non classical logic is multi-valued logic that has the particularity to support symbolic data. We introduced in a previous work an approximate reasoning in the multi-valued framework based on linguistic modifiers that checks approximate reasoning axiomatics. This paper describes the development of software model for the treatment of imperfection with our approach of approximate reasoning. It is a knowledge-based systems shell for symbolic data called RAMOLI. This shell provides simple and interactive Graphical User Interface to introduce knowledge and to infer with our approximate reasoning.

[1]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[2]  Herman Akdag,et al.  Linguistic Modifiers in a Symbolic Framework , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[3]  Daniel G. Schwartz,et al.  A resolution-based system for symbolic approximate reasoning , 1995, Int. J. Approx. Reason..

[4]  B. Pilsworth,et al.  Axiomatic approach to implication for approximate reasoning with fuzzy logic , 1980 .

[5]  Satoru Fukami,et al.  Some considerations on fuzzy conditional inference , 1980 .

[6]  Robert Orchard,et al.  Fuzzy Reasoning in JESS: The Fuzzyj Toolkit and Fuzzyjess , 2001, ICEIS.

[7]  Simon L. Kendal,et al.  An introduction to knowledge engineering , 2007 .

[8]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[9]  Carlo Tasso,et al.  Development of knowledge-based systems for engineering , 1998 .

[10]  John W. Schaffer,et al.  Knowledge-based programming for music research , 1997 .

[11]  Daniel Pacholczyk,et al.  A qualitative theory of uncertainty , 1992, Fundam. Informaticae.

[12]  Faddoul Khoukhi Approche logico-symbolique dans le traitement des connaissances incertaines et imprecises dans les sbc , 1996 .

[13]  Khaled Ghédira,et al.  Approximate Reasoning in a Symbolic Multi-valued Framework , 2008, Computer and Information Science.

[14]  Susana Muñoz-Hernández,et al.  Fuzzy Prolog: a new approach using soft constraints propagation , 2004, Fuzzy Sets Syst..

[15]  Bob Orchard,et al.  FuzzyCLIPS Version 6.10d User's Guide , 2004 .

[16]  Amel Borgi,et al.  Extended symbolic approximate reasoning based on linguistic modifiers , 2014, Knowledge and Information Systems.

[17]  Matthew L. Ginsberg,et al.  Multivalued logics: a uniform approach to reasoning in artificial intelligence , 1988, Comput. Intell..

[18]  Khaled Ghédira,et al.  Generalized Modus Ponens Based on Linguistic Modifiers in a Symbolic Multi-Valued Framework , 2008, 38th International Symposium on Multiple Valued Logic (ismvl 2008).

[19]  Daniel Pacholczyk,et al.  Contribution au traitement logico-symbolique de la connaissance , 1992 .

[20]  Avinash C. Kak,et al.  FuzzyShell: a large-scale expert system shell using fuzzy logic for uncertainty reasoning , 1998, IEEE Trans. Fuzzy Syst..

[21]  Amel Borgi,et al.  On Some Properties of Generalized Symbolic Modifiers and Their Role in Symbolic Approximate Reasoning , 2009, ICIC.