| But as an approach to general intelligence, classical symbolic AI has been disappointing. A major obstacle here is the symbol grounding problem [18, 19]. The symbolic elements of a representation in classical AI – the constants, functions, and predicates – are typically hand-crafted, rather than grounded in data from the real world. Philosophically speaking, this means their semantics are parasitic on meanings in the heads of their designers rather than deriving from a direct connection with the world. Pragmatically, hand-crafted representations cannot capture the rich statistics of realworld perceptual data, cannot support ongoing adaptation to an unknown environment, and are an obvious barrier to full autonomy. By contrast, none of these problems afflict machine learning. Deep neural networks in particular have proven to be remarkably effective for supervised learning from large datasets using backpropagation. [..] The hybrid neuralsymbolic reinforcement learning architecture we propose relies on a deep learning solution to the symbol grounding problem.
Source: Marta Garnelo et al: Towards Deep Symbolic Reinforcement Learning https://arxiv.org/pdf/1609.05518.pdf