Comparing Computer Models Solving Number Series Problems

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Comparing Computer Models Solving Number Series Problems
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 IQtests 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.
Ute Schmid, Marco Ragni
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where AGI
Authors Ute Schmid, Marco Ragni
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