Duel-based Deep Learning system for solving IQ tests

Paulina Tomaszewska · Adam Żychowski · Jacek Mańdziuk


One of the relevant aspects of Artificial General Intelligence is the ability of machines to demonstrate abstract reasoning skills, for instance, through solving (human) IQ tests. This work presents a new approach to machine IQ tests solving formulated as Raven’s Progressive Matrices (RPMs), called Duel-IQ. The proposed solution incorporates the concept of a tournament in which the best answer is chosen based on a set of duels between candidate RPM answers. The three relevant aspects are: (1) low computational and design complexity, (2) proposition of two schemes of pairing up candidate answers for the duels and (3) evaluation of the system on a dataset of shapes other than those used for training. Depending on a particular variant, the system reaches up to 82.8% accuracy on average in RPM tasks with 5 candidate answers and is on par with human performance and superior to other literature approaches of comparable complexity when training and test sets are from the same distribution.

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