The program allows the user to design arbitrary geometrical tasks like such used in the Raven or CFT3 intelligence tests. The tasks are solved by the program and their complexity for human subjects is evaluated. The complexity measure is based on an analysis of the empricial data from the CFT3.
To run the program Python2.7 and PyGTK is required.
Any mathematical pattern can be the generation principle for number series. In con-
trast to most of the application fields of artificial neural networks (ANN) a successful
solution does not only require an approximation of the underlying function but to correctly
predict the exact next number. We propose a dynamic learning approach and evaluate our
method empirically on number series from the Online Encyclopedia of Integer Sequences.
Finally, we investigate research questions about the performance of ANNs, structural prop-
erties, and the adequate architecture of the ANN to deal successfully with number series.
Solving number series poses a challenging problem for humans and Artificial Intelligence
Systems. The task is to correctly predict the next number in a given series, in accordance
with a pattern inherent to that series. We propose a novel method based on Artificial
Neural Networks with a dynamic learning approach to solve number series problems. Our
method is evaluated on an own experiment and over 50.000 number series from the Online
Encyclopedia of Integer Sequences (OEIS) database.
Human reasoners have an impressive ability to solve analogical reasoning problems and they still outperform computational systems. Analogical reasoning is relevant in dealing with intelligence tests. There are two kinds of approaches: to solve IQ-test problems in a way similar to humans (i.e., a cognitive approach) or to solve these problems optimally (i.e., the AI approach). Most systems can be associated with one of these approaches. Detailed systems solving geometrical intelligence tests, explaining cognitive operations based on working memory and producing precise predictions and results such as error rates and response times have not been developed so far. We present a system implemented in the cognitive architecture ACT-R, able to solve analogously developed problems of Raven’s Standard and Advanced Progressive Matrices. The model solves 66 of the 72 tested problems of both tests. The model’s predicted error rates correlate to human performance with r = .8 for the Advanced Progressive Matrices and r = .7 for all problems together.
Positioning. The article, published at the European Conference on Artificial Intelligence 2012, presents an ACT-R model that is able to solve Raven’s Matrix Test.
Research Question. Is it possible to explain the limitations in human reasoning in IQ- tests based on working memory limitations?
Method. Cognitive modeling; formal analysis
Results. The cognitive model in ACT-R, is able to solve analogously developed problems of Raven’s Standard and Advanced Progressive Matrices. The model solves 66 of the 72 tested problems of both tests. The model’s predicted error rates correlate to human performance with r = .8 for the Advanced Progressive Matrices and r = .7 for all problems together.
Ragni, M., & Neubert, S. (2012). Solving Raven’s IQ-tests: An AI and Cognitive Modeling Approach. In L. D. Raedt et al. (Eds.), Proceedings of the 20th European Conference on Artificial Intelligence (Vol. 242, pp. 666–671). Amsterdam: IOS Press.