Inductive learning and defeasible inference
Kevin B. Korb · Journal of Experimental & Theoretical Artificial Intelligence · 1995
The symbolic approach to artificial intelligence research has dominated AI until recent times. It continues to dominate work in the areas of inference and reasoning in artificial systems. The author argues, however, that non-quantitative methods are inherently insufficient for supporting inductive inference. In particular there are reasons to believe that purely deductive techniques—as advocated by the naive physics community—and their nonmonotonic progeny are insufficient for supplying means for the development of the autonomous intelligence that AI has as its primary goal. The lottery paradox points to fundamental difficulties for any such non-quantitative approach to AI. Here, it is suggested that a hybrid system employing both quantitative and non-quantitative modes of reasoning is the most promising avenue for developing an intelligence that can avoid both the paralysis induced by computational complexity and the inductive paralysis to which purely symbolic approaches succumb.