2004 年 22 巻 2 号 p. 215-222
Multivalued logic is important as a fundamental technique in designing machine intelligence. Particularly Boolean multivalued logic such that the whole set of logic formulas forms a Boolean algebra inherits those theorems, laws, etc. which are obtained in traditional Boolean binary logic. In this article, only logic such that logic formulas take more-than-two truth values is called multivalued logic, and any logic such that weights or costs are added to logic formulas of Boolean binary logic is not classified into multivalued logic. This article defines Boolean multivalued logic by coding binary fractions being greater than or equal to 0 and less than 1 directly as truth values. To handle readily Bayesian theory rationalizing collection of knowledges via observation, this article also introduces an arithmetic operation called “conditioning” in addition to usual logic operations. A key to advancing machine intelligence built in a certain kind of robots required an ability of thinking is extracting causality between objects by introducing such a robust logic that can process inferences consistently. This article shows with some instances the way of optimizing truth values of atoms when what truth values some logic formulas should take are given as knowledges, and the way of calculating the truth values of unknown logic formulas as inferences. It also mentions possibility of introducing natural language for realization of phonic conversation between users and machine intelligence.