Representation, reasoning and learning are the basic principles of human intelligence. The emulation of human intelligence has been the aim of Artificial Intelligence since its origins in 1956.
In fact, converting raw data into information (data in the context of other data) and hence into knowledge (information in the context of other information), is critical for understanding activities, behaviors, and in general the world we try to model. Both in the Robotics and the Computer Vision areas we try to model the real world where the humans are operating.
The type of knowledge that Robotics and Computer Vision need to obtain is Common Sense Knowledge. Contra intuitively, common sense knowledge is more difficult to model than expert knowledge, which can be quite easily modeled by expert systems (a more or less closed research area since the 70s).
Both in Robotics and Computer Vision areas, Probabilistic and Bayesian models have historically been used as the way to represent, reason and learn from the world. These methods have provided very good initial results. The problem is that they have never been scalable. That is why there is no commercial intelligent robot that has the full ability to serve people yet. Although there exist many preliminary solutions including artificial vision, the percentage of false positives or negatives are still too high to consider it as completely reliable, and therefore artificial vision is still an open research area.
The problems detected in the probabilistic approaches have been twofold: Read the rest of this entry »