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:
- It is a brute force method with high computational cost.
- There is no use of common sense or any other cognitive approach to make sense of the numbers, therefore the improvements are limited.
We need “more intelligence” to be able to automate common sense.
Qualitative Representation and Reasoning, on the other hand, have been demonstrated to be a much better approach to model common sense information, by transforming uncertainty and incomplete data into knowledge. Highly promising results have been obtained in the area of Robotics for autonomous Map Building, Localization and Navigation [1,2,3,4].
This is the approach that Cognitive Robots has followed since 1992.
20 years of research (at the University Jaume I, Spain) and development are now included in the most advanced intelligent system that can be incorporated into physical and virtual devices to increase their intelligent, in an immense variety of applications.
Cognitive Robots is now actively looking for commercial partnerships and investment capital to bring the company into the next level. If you are interested, please contact firstname.lastname@example.org.
1. Escrig, M.T., Peris, J.C, “The use of a reasoning process to solve the almost SLAM problem at the Robocup legged league”, Catalonian Conference on Artificial Intelligence, CCIA’05, 2005.
2. Peris, J.C, Escrig, M.T., “Cognitive Maps for mobile Robot Navigation: A Hybrid Representation Using Reference Systems”, 19th International Workshop on Qualitative Reasoning, Graz, Austria, pp. 179-185, ISBN 3-9502019-0-4, Graz, Austria, 2005.
3. Cognitive Robots S.L., USA pendent patent: “SYSTEMS AND METHODS FOR ESTABLISHING AN ENVIRONMENTAL REPRESENTATION”, October 2010.
4. Z. Falomir, Ll. Museros, L. Gonzalez-Abril, M.T. Escrig, J.A. Ortega, “A model for the qualitative description of images based on visual and spatial features”, Computer Vision and Image Understanding 116, 698–714, 2012.