Inclusion-Based Approximate Reasoning

Nowadays, people start to accept fuzzy rule–based systems as flexible and convenient tools to solve a myriad of ill–defined but otherwise (for humans) straightforward tasks such as controlling fluid levels in a reactor, automatical lens focussing in cameras and adjusting an aircraft’s navigation to the change of winds and so on. Contrary to the intuition often seen as the feeding ground of fuzzy rule–based systems—namely, that they realize an extension of the Modus Ponens (MP) rule of inference to an environment with more than two truth–values—most actual applications rely at the base level on common interpolation techniques or similarity assessments to simulate the process of “calculating with words” perceived at the user level. It is doubtful whether these somewhat opportunistic approaches will perform well when more challenging requirements (e.g. aspects of logical consistency; incorporation of varying facets of uncertainty) are imposed in order to implement a s...

• Type: Conference
• Year: 2001
• Where: ICCS
• Authors: Chris Cornelis, Etienne E. Kerre
• Keywords: Applied Computing, Fuzzy Rule–based Systems, ICCS 2001, Inference, Successful Artificial Reasoning