From information to probability: An axiomatic approach—Inference is information processing

✍ Scribed by Wilhelm Rödder; Gabriele Kern-Isberner

• Publisher: John Wiley and Sons
• Year: 2003
• Tongue: English
• Weight: 149 KB
• Volume: 18
• Category: Article
• ISSN: 0884-8173
• DOI: 10.1002/int.10094

✦ Synopsis

We define the very rich language of composed conditionals on a three-valued logic and use this language as the communication tool between man and machine. Communication takes place for three reasons: knowledge acquisition, query, and response. Learning, thinking, and answering questions are of a pure information theoretical nature. The pivot of this knowledge processing concept is the amount of information (bit) we receive if a conditional becomes true. We follow an axiomatic approach to information theory rather than the classical probabilistic approach of Shannon; information comes first, and then comes probability. In the light of this philosophy, query and response experience new interpretations. Both, acquisition and response are realized by maximizing entropy and minimizing relative entropy, respectively. The iterative solution of these mathematical optimization problems gives new insights into the adaptation of prior knowledge to new information. Our expert system shell SPIRIT supports this kind of knowledge processing, which will be established by suitable examples.