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Fakultät Wirtschaftsinformatik und Angewandte Informatik

Lehrstuhl für Kognitive Systeme

  1.  Otto-Friedrich-Universität Bamberg
  2. Fakultäten
  3. Wirtschaftsinformatik und Angewandte Informatik
  4. Professuren und Lehrstühle
  5. Angewandte Informatik
  6. Lehrstuhl für Kognitive Systeme
  7. Studium
  8. Lehrveranstaltungen
  9. Archiv
  10. WS14/15
  11. Reading Club Kognitive Systeme

Reading Club Kognitive Systeme (WS 14/15)

General Information

  • For a general course description please read the corresponding pages from from the WIAI module guide.
  • You find administrative information  at UnivIS.
  • Participants should sign up for the course in the virtual campus.
  • This course addresses master students and doctoral students.
  • Recommended for bachelor students in the 3rd semester and master students.
  • The course is held in block form, usually in the summer term.

Topic: Cognitive Models for Number Series Induction Problems

The ability to detect regularities and generalize over them is one of the central building blocks of human intelligence. The most impressive demonstration of this ability in learning grammatical rules for ones native language from streams of language input. In many I.Q. tests, induction of number series is included as a sub-test for the domain of reasoning and it is classified as sub-test measuring the g-factor (general intelligence) which is thought to be the foundation of other more specialized areas such as verbal intelligence.

In the seminar we will study literature from psychology, cognitive modeling, and artificial intelligence addressing number series induction. We will identify specific research questions concerning number series induction and will use these questions to structure and guide the presentation of research findings. Furthermore, we will identify a small practical task (such as testing a specific cognitive model or AI approach) which will be worked on during the seminar course.

Recommended Reading / Links 

  • Jaqueline Hofmann, Emanuel Kitzelmann, Ute Schmid: Applying Inductive Program Synthesis to Induction of Number Series A Case Study with IGOR2. KI 2014: 25-36
  • Martina Milovec, Applying Inductive Programming to Solving Number Series Problems -- Comparing Performance of Igor with Humans (MA AI, Sept. 2014) [pdf]

R. F. Amthauer, B. Brocke, D. Liepmann, and A. Beauducel. Intelligenz-Struktur-Test 2000: IST 2000. Hogrefe & Huber, 1999.

S. Bringsjord. Psychometric artificial intelligence. Journal of Experimental & Theoretical Artificial Intelligence, 23(3):271–277, 2011.

J. Burghardt. E-generalization using grammars. Artificial Intelligence, 165(1):1 – 35, 2005.

D. L. Dowe and J. Hernandez-Orallo. IQ tests are not for machines, yet. Intelligence, 40(2):77–81, 2012.

J. Hernandez-Orallo, D. L. Dowe, and M. V. Hernandez-Lloreda. Universal psychometrics: Measuring cognitive abilities in the machine kingdom. Cognitive Systems Research, 27:5074, 2014

M. Ragni and A. Klein. Predicting numbers: an AI approach to solving number series. In KI 2011: Advances in Artificial Intelligence, pages 255–259. Springer, 2011.

P. Sanghi and D. L. Dowe. A computer program capable of passing IQ tests. In 4th Intl. Conf. on Cognitive Science (ICCS’03), Sydney, pages 570–575, 2003.

M. Siebers and U. Schmid. Semi-analytic natural number series induction. In KI 2012: Advances in Artificial Intelligence, pages 249–252. Springer, 2012.

C. Strannegard, M. Amirghasemi, and S. Ulfsbücker. An anthropomorphic method for number sequence problems. Cognitive Systems Research, 2223(0):27 – 34, 2013.

Claes Strannegrd, Abdul Rahim Nizamani, Anders Sjberg, and Fredrik Engstrm. Bounded Kolmogorov complexity based on cognitive models. In K. U. Kühnberger, S. Rudolph, and P. Wang, editors, Artificial General Intelligence, LNCS 7999, 130–139. Springer.

Seminar Report

Barbora Hrda & Christian Tecihmann: Cognitive Models for Number Series Induction Problems -- An Approach to Determine the Complexity of Number Series [pdf]

Previous Topics

  • SS 2014: Experimenting with a Humanoid Robot - Programming NAO to (Inter-)Act  [Archiv Page] 
  • SS 2013: An introduction into statistic data analysis with R  [Archiv Page] 
  • SS 2012: Transfer Learning  [Archiv Page] 
  • SS 2011: Emotion Mining in Images and Text  [Archiv Page] 
  • SS 2010: Aspects of Cognitive Robotics [Archiv Page] 
  • SS 2009: Reading Club Decision Support Systems [Archiv Page] 
  • WS 08/09: Algebraic Foundations of Functional Programming (together with Theoretical Computer Science) [Archiv Page]  
  • SS 2008: Similarity (together with Statistics) [Archiv Page]
  • SS 2007: Automated Theorem Proving with Isabelle (together with Theoretical Computer Science) [Archiv Page]
  • SS 2006: Support Vector Machines [Archiv Page]