In this paper, we propose a novel method for personal robots to acquire autonomous behaviors based on interaction between users and robots. This method have two advantages, first is loadless for users by comparison with users' teaching, second is that robots can acquire behaviors as users wish by comparison with autonomous learning. In this method, robots store sensory information and results of interaction, and represent the relationship between sensor and behavior as stochastic behavior decision models. The robot advances the learning through making suggestions and questions for the user using the stochastic model. We investigate the feasibility of this method on obstacle avoidance tasks for mobile robots. Through experiments, we have confirmed that the mobile robot acquires avoidance behavior against change of environment through only several teaching. Also we have confirmed that the acquired models reflect the experience of interaction, therefore the model reflects personal preferences of teaching operation.