Comparing Computer Models Solving Number Series Problems

  • Ute SchmidEmail author
  • Marco Ragni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9205)


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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© 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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