We report a novel approach to addressing the Raven’s Progressive Matrices (RPM) tests, one based upon purely visual representations. Our technique introduces the calculation of confidence in an answer and the automatic adjustment of level of resolution if that confidence is insufficient. We first describe the nature of the visual analogies found on the RPM. We then exhibit our algorithm and work through a detailed example. Finally, we present the performance of our algorithm on the four major variants of the RPM tests, illustrating the impact of confidence. This is the first such account of any computational model against the entirety of the Raven’s.

We present two visual algorithms, called the affine and fractal methods, which each solve a considerable portion of the Raven’s Progressive Matrices (RPM) test. The RPM is considered to be one of the premier psychometric measures of general intelligence. Current computational accounts of the RPM assume that visual test inputs are translated into propositional representations before further reasoning takes place. We propose that visual strategies can also solve RPM problems, in line with behavioral evidence showing that humans do use visual strategies to some extent on the RPM. Our two visual methods currently solve RPM problems at the level of typical 9- to 10-year-olds.

The Odd One Out test of intelligence consists of 3x3 matrix reasoning problems organized in 20 levels of difficulty. Addressing problems on this test appears to require integration of multiple cognitive abilities usually associated with creativity, including visual encoding, similarity assessment, pattern detection, and analogical transfer. We describe a novel fractal technique for addressing visual analogy problems on the Odd One Out test. In our technique, the relationship between images is encoded fractally, capturing inherent self-similarity. The technique starts at a high level of resolution, but, if that is not sufficient to resolve ambiguity, it automatically adjusts itself to the right level of resolution for addressing a given problem. Similarly, the technique automatically starts with searching for similarity between simpler relationships, but, if that is not sufficient to resolve ambiguity, it automatically searches for similarity between higher-order relationships. We present preliminary results from applying the fractal technique on a representative subset of the problems from the Odd One Out test.

Household robots are becoming commonplace. The application of social cues, such as emotion, has the potential to make such robots easier to use and understand. However, it remains unclear how household robots can or should display emotion, and what considerations should be given to emotive behavior regarding the expected set of contexts in which the robot will operate. In this paper, we report the results of our systematic evaluation of context and emotion recognition of a non-anthropomorphic robot, the iRobot Roomba. Considerations, implications, and future work are discussed.