Is reduction in task space a condition for accelerated learning?

Biasing, once regarded as "cheating" in the machine learning community, is now understood and accepted as a necessary part of learning. However, despite its wide acceptance and recognition, biasing has never been studied as a separate research issue, except by Hailu & Sommer (1999), who made an attempt to shed light on the relationship between the quality of the bias and learning trials. So far, the general view held in biasing a learning system is to look for a bias that maximally collapses the search space. It is well-known, however, that reckless reduction of the search space often leads to sub-optimal learning. Regardless of the final level of optimality, this paper challenges this broadily accepted biasing scheme from the point of view of accelerating the learning process itself. Is a large search space a definitive indication of slow learning? We give a non-affirmative answer to this dogma by presenting a typical robot learning scenario. Experiments clearly indicate that, in spite of its large search space, a bias that is derived from the unique characteristics of the task shows better learning behavior than a bias that reduces the search space aggressively