Comparing Computer Models Solving Number Series Problems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9205)

Abstract

Inductive reasoning requires to find for given instances a general rule. This makes inductive reasoning an excellent test-bed for artificial general intelligence (AGI). An example being part of many IQ-tests are number series: for a given sequence of numbers the task is to find a next “correct” successor number. Successful reasoning may require to identify regular patterns and to form a rule, an implicit underlying function that generates this number series. Number series problems can be designed along different dimensions, such as structural complexity, required mathematical background knowledge, and even insights based on a perspective switch. The aim of this paper is to give an overview of existing cognitive and computational models, their underlying algorithmic approaches and problem classes. A first empirical comparison of some of these approaches with focus on artificial neural nets and inductive programming is presented.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Cognitive Systems GroupUniversity of BambergBambergGermany
  2. 2.Foundations of AI, Technical FacultyUniversity of FreiburgFreiburg im BreisgauGermany

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