In this paper, we propose a novel approach for learning context free grammars (CFGs) from positive and negative samples by solving a Boolean satisfiability problem (SAT). We encode the set of samples, together with limits on the sizes of rule sets to be synthesized as a Boolean expression. An assignment satisfying the Boolean expression contains a minimal set of rules that derives all positive samples and no negative samples. A feature of this approach is that we can synthesize the minimal set of rules in Chomsky normal form. The other feature is that our learning method reflects any improvements of SAT solvers. We present experimental results on learning CFGs for fundamental context free languages, including a set of strings composed of the equal numbers of a's and b's and the set of strings over {a, b}* not of the form ww.

A human can lift up an object with almost minimal grasping force regardless of the friction coefficient between the fingers and the object, but the sensing mechanism for this remarkable task has not been well explained yet. We propose a tactile sensor to detect a friction coefficient at the moment of touch. With this sensor output we evaluate the largest lifting-force just before a slip happens, without any preliminary motions. The sensor has a sensing cell in the elastic body, which measures strain/stress components parallel and vertical to the surface. Those plural stress/strain parameters at a point gives the friction coefficient. A prototype sensor is fabricated with acoustic resonant tensor cell, and we examine its principles in experiments.