Challenges for artificial cognitive systems

It can be said the neural networks (specially in their sophisticated forms) account for such abstract recoding, but this is not fully satisfactory, because there is just one network in the model; a different approach is to use layers of neural networks, where the higher level takes as inputs the patterns of the lower, sensory, layers (Sun, 2006), but up to now this is done " by hand " . Still another approach, of Vygotskian inspiration, views in the use of public symbols the key to understand cognitive, abstract recoding (Gomila, 2012), but the application of this approach within artificial cognitive systems is just beginning. Flexible use of knowledge Extracting world regularities and contingencies would be useless unless such knowledge can guide future action in real - time in an uncertain environment. This may require in the end, as anticipated above, behavioral unpredictability, which is a property than runs contrary to the technical requirements of robustness and reliability for artificial systems (to guarantee safety, as the principal engineer ' s command). The critical issue for flexibility is related to how the knowledge is " stored " (see previous section), and therefore, how it is accessed. The major roadblock to carry this out - regardless of approach - is again combinatorial explosion, whether at the level of propositional representations, as in classical AI, or at the level of degrees of freedom for the control of actuators. But it is also a problem to " judge ", in a given situation, which one is the best one to categorize it, given what the system knows.