A computational analysis of general intelligence tests for evaluating cognitive development

[EN] The progression in several cognitive tests for the same subjects at different ages provides valuable information about their cognitive development. One question that has caught recent interest is whether the same approach can be used to assess the cognitive development of artificial systems. In particular, can we assess whether the fluid or crystallised intelligence of an artificial cognitive system is changing during its cognitive development as a result of acquiring more concepts? In this paper, we address several IQ tests problems (odd-one-out problems, Raven s Progressive Matrices and Thurstone s letter series) with a general learning system that is not particularly designed on purpose to solve intelligence tests. The goal is to better understand the role of the basic cognitive perational constructs (such as identity, difference, order, counting, logic, etc.) that are needed to solve these intelligence test problems and serve as a proof-of-concept for evaluation in other developmental problems. From here, we gain some insights into the characteristics and usefulness of these tests and how careful we need to be when applying human test problems to assess the abilities and cognitive development of robots and other artificial cognitive systems.This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN 2015-69175-C4-1-R and TIN 2013-45732-C4-1-P, and by Generalitat Valenciana under grant PROMETEOII/2015/013.Martínez-Plumed, F.; Ferri Ramírez, C.; Hernández-Orallo, J.; Ramírez Quintana, MJ. (2017). A computational analysis of general intelligence tests for evaluating cognitive development. Cognitive Systems Research. 43:100-118. https://doi.org/10.1016/j.cogsys.2017.01.006S1001184

Selected Ph.D. Thesis Abstracts: Incremental and developmental perspectives for general-purpose learning systems

[EN] Here we present a summary of the Ph.D. thesis entitled ¿Incremental and developmental perspectives for generalpurpose learning systems¿ which has been recently defended by the author.Martínez-Plumed, F. (2017). Selected Ph.D. Thesis Abstracts: Incremental and developmental perspectives for general-purpose learning systems. IEEE Intelligent Informatics Bulletin. 18(1):26-27. doi:10.1145/3098888.3098898S2627181Hernandez-Orallo, J., Martinez-Plumed, F., Schmid, U., Siebers, M., & Dowe, D. L. (2016). Computer models solving intelligence test problems: Progress and implications. Artificial Intelligence, 230, 74-107.Martinez-Plumed, F., Ferri, C., Hernandez- Orallo, J., & Ramirez-Quintana, M. J. (2013). Learning with configurable operators and rl-based heuristics. In A. Appice (Ed.), New frontiers in mining complex patterns (Vol. 7765, p. 1-16). Springer.Martinez-Plumed, F., Ferri, C., Hernandez- Orallo, J., & Ramirez-Quintana, M. J. (2015). Knowledge acquisition with forgetting: an incremental and developmental setting. Adaptive Behavior, 23(5), 283-299.Martinez-Plumed, F., Ferri, C., Hernandez- Orallo, J., & Ramirez-Quintana, M. J. (2017). A computational analysis of general intelligence tests for evaluating cognitive development. submitted (second revision)