This article, by regarding the relative number of affirmative bits on an arbitrary finite sequence of bits as the truth value, defines a new multivalued logic system such that the set of logic formulae forms a Boolean algebra in contrast to other non-Boolean multivalued logic systems, which we call a Boolean multivalued logic system. This article also, for demonstrating compactly that the Boolean properties (in contrast to other multivalued logic systems) simplify multivalued inferences in flexible styles, shows several intuitively-understandable examples tracing biotic diversity such that, on the proposed logic system, we can easily handle semantic inferences verifying values of all bits of each logic formula, inductive inferences counting the relative numbers of affirmative bits for determination of truth values, and deductive inferences syntactically narrowing ranges of truth values.

This article discusses complementary (C) fuzzy logic system that is one of continuous multiary logic systems that satisfies a complementary law differently from usual fuzzy logic systems. This article includes formulation of the C fuzzy logic system, derivation of tautologies, and mentions an example that typically shows a difference in inference computation between the C fuzzy logic system and a usual fuzzy logic system.

This paper proposes a method of, given a multilevel image called a reference image in which an arbitrary shape S lies and another multilevel image called an input image in which one shape rotated and parallel translated from S lies, computing the angle of rotation and the quantity of parallel translation. This method applies a modified version of the Hough transform proposed by O'Gorman and Clowes (1976) to the reference and input images, and computes the angle of rotation uniquely. Moreover, this method needs no extracting feature points, and is applicable to low quality images.

A class of learning control algorithms with a forgetting factor 1> alpha >0 and without differentiation of velocity signals is proposed, which updates the input by u/sub k+1/=(1- alpha ) u/sub k/+ alpha u/sub 0/+ Phi e/sub k/, where u/sub k/ and e/sub k/ stand for command input and velocity error at kth exercise, respectively. The robustness of this learning control with respect to reinitialization errors, fluctuation of dynamics, and measurement noise is studied. It is shown that the exponential passivity of displacement robot dynamics plays a crucial role in the uniform boundedness of transient behaviors and the convergence in the progress of learning. A method called selective learning, which updates u/sub 0/ in the long-term memory by selecting the best command among the past several trials, is proposed. It is claimed that this method accelerates the speed of convergence.>

A problem of task planning of a robot can result in, another problem of finding a path that connects some given initial point and final point. The article suggests prospect of a 'wave front method' that can find one of the shortest paths in a computation time being proportional to the length of the shortest paths. This wave front method is based on an analogy of the Huygens' principle known in physics, and can be extended into the time dimension.>