This study introduces a machine hearing system for robot audition, which enables a robotic agent to pro-actively minimize the uncertainty of sound source location estimates through motion. The proposed system is based on an active exploration approach, providing a means to model and predict effects of the agent's future motions on localization uncertainty in a probabilistic manner. Particle filtering is used to estimate the posterior probability density function of the source position from binaural measurements, enabling to jointly assess azimuth and distance of the source. The framework allows to infer and refine a policy to select appropriate actions via a Monte Carlo exploration approach. Experiments in simulated reverberant conditions are conducted, showing that active exploration and the incorporation of distance estimation significantly improve localization performance.