We are sitting in a soccer stadium and discover our neighbor sitting in the 10th row. We recognize him with no difficulty at all, even though he is wearing sunglasses and a cap in his club colors. Complex recognition processes like this work because the brain, sensory organs and nerve pathways are able to pick up stimuli and process them. The ability to classify things (categorization) appears to be a fundamental characteristic of human intelligence, and one that gives robots a real "headache". In situations in which a robot has no access to knowledge of a pre-defined environment, and pre-programmed control is therefore not possible, the robot will tend to fail miserably in its task. But it is precisely autonomous robots capable of acting in response to a given situation that could be of great use to humans.
This is the focus of BACS (Bayesian Approach to Cognitive Systems), an Integrated Project under the 6th Framework Program of the European Commission which has been allocated EUR 7.5 million in funding. The BACS project brings together researchers and commercial companies working on artificial perception systems potentially capable of dealing with complex tasks in everyday settings. The basis of this research is Bayes' theorem. Thomas Bayes was an English mathematician and Presbyterian monk who lived in the 18th century. The theorem named after him describes alternatives for calculating the likelihood of events occurring using conditional probability. It is a model for rational judgment when only uncertain and incomplete information is available. Bayes' theorem is applicable to all questions relating to learning from experience. In the 50 months for which the BACS collaboration will run, the ten project partners will use the theorem to model neuronal functions and cognitive processes. The aim is to gain a better understanding of perception and action in living beings, to optimize existing learning algorithms, and to realize intelligent artificial sy
Contact: Cristbal Curio