A bottom-up complement to the logic-based top-down approach to the story arrangement test
Nate Chapin, Boleslaw Karol Szymanski, Selmer Bringsjord, Bettina Schimanski · Journal of Experimental & Theoretical Artificial Intelligence · 2011
Psychometric AI is a type of AI distinguished by the pursuit of intelligent systems able to excel on psychometrically validated human-level tests of cognitive abilities. We seek to build a system that solves a specific sub-test within Psychometric AI: the story arrangment test. Items in this test confront the test-taker with a set of jumbed snapshots (whether diagrammatic or otherwise) which must be ordered to tell a coherent story. We propose a dual-process system that combines bottom-up non- or sub-symbolic processing (e.g. neural network-based modelling) with top-down symbolic processing (e.g. deductive reasoning over declarative information represented as formulae in a logical system) for solving these tests of cognitive ability. The top-down process provides the benefits of a traceable proof, but requires a large amount of pre-existing knowledge. The bottom-up technique sacrifices provability and certainty on some problems for speed, but always yields some level of an answer to a given problem. This demonstrates a natural marriage between the two: the bottom-up approach seems especially powerful when used as a form of pre-processing in conjunction with a logic-based approach, because the latter approach would only need to consider a small number of possible orderings of snapshots.