You’re walking down New York’s crowded Times Square and you look at the hot dog stand and notice that the guy is wearing a white t-shirt with jeans, trying to escape the heat under a yellow and red striped umberella, fixing hot dogs for customers… What’s unique about this story?? Well, it was easy for you to notice all of these things in a second or less because of your brain’s ability at complex recognition. Such recognition process works because the brain, nerve pathways, and sensory organs are able to pick up stimuli and process them swiftly. However, this process is a real “headache” for robots. Here’s a scoop from Physorg:
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.
The scientific work being carried out under BACS makes robots with new capabilities a real prospect: robots capable of handling incomplete information, analyzing their environment, acquiring context-specific knowledge, interpreting the data and, together with humans, taking decisions. Specific implementations with market potential are already planned. A prospective implementation with market potential is a system that can assist drivers of passenger cars and trucks by employing probabilistic control functions and driving strategies. This should make driving safer for both drivers and pedestrians. Another area of interest is 3D modelling and surveillance of safety-critical applications such as monitoring structural changes in buildings or mines and safety-relevant infrastructure elements in power lines. European industry can use Bayes’ alternative calculation models to good advantage; they have applications both in major companies in the automotive industry and mobile telephony, for example, and in small and medium-size companies active in niche markets such as healthcare, inspection, monitoring or even market forecasts.
