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Contact Information


Visit Tarek R Besold

A309H2, College Building


Postal Address

City, University of London
Northampton Square
United Kingdom



Dr. Tarek R. Besold is a Lecturer in Data Science at City, University of London, conducting research at the intersection between artificial intelligence, computational creativity, and cognitive systems. He studied mathematics, computer science, and logic in Erlangen, Zaragoza, and Amsterdam, obtained a PhD from the Institute of Cognitive Science in Osnabrück, and worked as a postdoctoral researcher at the KRDB Research Centre of the Free University of Bozen-Bolzano and at the Digital Media Lab of the University of Bremen.

Among others, Dr. Besold was the General Chair of the HLAI 2016 Joint Multi-Conference on Human-Level Artificial Intelligence, and founder and/or organizer of several international workshop series bridging between AI and cognitive science. He is a co-editor of the books “Computational Creativity Research: Towards Creative Machines” and “Concept Invention: Foundations, Implementation, Social Aspects and Applications”, and serves in different editorial functions for the Journal of Artificial Intelligence Research (JAIR), for Frontiers in Psychology: Cognition, and for Biologically Inspired Cognitive Architectures (BICA).


  1. PhD, University of Osnabrück, Osnabrück, Germany, Mar 2011 – Dec 2014
  2. Diplom (equiv. MSc) in Mathematics, University of Erlangen-Nuremberg, Erlangen, Germany, Oct 2004 – Dec 2009

Postgraduate Training

  1. Logic Year, University of Amsterdam, Amsterdam, Amsterdam, Netherlands, Feb 2010 – Feb 2011


  1. Lecturer in Data Science, City University London, London, Sep 2017 – present
  2. Postdoctoral Researcher, University of Bremen, Bremen, Sep 2016 – Sep 2017
  3. Postdoctoral Researcher, Free University of Bozen-Bolzano, Bolzano, Oct 2015 – Aug 2016
  4. Research Associate, University of Osnabrück, Osnabrück, Oct 2013 – Sep 2015
  5. PhD Fellow, University of Osnabrück, Osnabrück, Mar 2011 – Sep 2013


English (can read, write, speak, understand spoken and peer review), French (can read, speak and understand spoken), German (can read, write, speak, understand spoken and peer review) and Spanish; Castilian (can read, speak and understand spoken).



  1. Besold, T.R. (2013). Turing revisited: A cognitively-inspired decomposition. Studies in Applied Philosophy, Epistemology and Rational Ethics (pp. 121–132).

Conference Papers and Proceedings (22)

  1. Martinez, M., Abdel-Fattah, A.M.H., Krumnack, U., Gómez-Ramírez, D., Smaill, A., Besold, T.R., Pease, A., Schmidt, M., Guhe, M. and Kühnberger, K.U. (2017). Theory blending: extended algorithmic aspects and examples. .
  2. Harder, F. and Besold, T.R. (2017). An approach to supervised learning of three valued Lukasiewicz logic in Hölldobler's core method. .
  3. Schmid, U., Zeller, C., Besold, T., Tamaddoni-Nezhad, A. and Muggleton, S. (2017). How does predicate invention affect human comprehensibility? .
  4. Garcez, A.D.A., Besold, T.R., De Raedt, L., Foldiak, P., Hitzler, P., Icard, T., Kiihnberger, K.U., Lamb, L.C., Miikkulainen, R. and Silver, D.L. (2015). Neural-symbolic learning and reasoning: Contributions and challenges. .
  5. Besold, T.R., Kühnberger, K.U., Garcez, A.D., Saffiotti, A., Fischer, M.H. and Bundy, A. (2015). Anchoring Knowledge In Interaction: Towards a harmonic subsymbolic/symbolic framework and architecture of computational cognition. .
  6. Besold, T.R. (2014). Sensorimotor analogies in learning abstract skills and knowledge: Modeling analogy-supported education in mathematics and physics. .
  7. Martinez, M., Krumnack, U., Smaill, A., Besold, T.R., Abdel-Fattah, A.M.H., Schmidt, M., Gust, H., Kühnberger, K.U., Guhe, M. and Pease, A. (2014). Algorithmic aspects of theory blending. .
  8. Krumnack, U., Schwering, A., Kühnberger, K.U., Gust, H., Abdel-Fattah, A., Besold, T., Schmidt, M. and Schneider, S. (2013). Sketch learning by analogy. .
  9. Besold, T.R. (2013). Rationality in context: An analogical perspective. .
  10. Besold, T.R. (2013). Analogy engines in classroom teaching: Modeling the string circuit analogy. .
  11. Besold, T.R. and Robere, R. (2013). When almost is not even close: Remarks on the approximability of HDTP. .
  12. Besold, T.R. and Robere, R. (2013). A note on tractability and artificial intelligence. .
  13. Besold, T.R. (2013). Human-level artificial intelligence must be a science. .
  14. Abdel-Fattah, A.M.H., Besold, T. and Kühnberger, K.U. (2012). Creativity, cognitive mechanisms, and logic. .
  15. Robere, R. and Besold, T.R. (2012). Complex analogies: Remarks on the complexity of HDTP. .
  16. Martinez, M., Besold, T., Abdel-Fattah, A., Kuehnberger, K.U., Gust, H., Schmidt, M. and Krumnack, U. (2011). Towards a domain-independent computational framework for theory blending. .
  17. Besold, T.R., Gust, H., Krumnack, U., Abdel-Fattah, A., Schmidt, M. and Kühnberger, K.U. (2011). An argument for an analogical perspective on rationality & decision-making. .
  18. Besold, T.R. and Mandl, S. (2010). Integrating logical and sub-symbolic contexts of reasoning. .
  19. Besold, T. (2010). Theory and Implementation of Multi-Context Systems Containing Logical and Sub-Symbolic Contexts of Reasoning. .
  20. Besold, T.R. and Mandl, S. (2010). Towards an implementation of a multi-context system framework. .
  21. Besold, T.R. and Schiemann, B. (2010). A multi-context system computing modalities. .
  22. Besold, T. and Mandl, S. (2010). Integrating logical and sub-symbolic contexts of reasoning. .

Journal Articles (12)

  1. Besold, T.R., Kühnberger, K.U. and Plaza, E. (2017). Towards a computational- and algorithmic-level account of concept blending using analogies and amalgams. Connection Science, 29(4), pp. 387–413. doi:10.1080/09540091.2017.1326463.
  2. Besold, T.R., Garcez, A.D., Stenning, K., van der Torre, L. and van Lambalgen, M. (2017). Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples. Minds and Machines, 27(1), pp. 37–77. doi:10.1007/s11023-017-9428-3.
  3. Besold, T.R., Hedblom, M.M. and Kutz, O. (2017). A narrative in three acts: Using combinations of image schemas to model events. Biologically Inspired Cognitive Architectures, 19, pp. 10–20. doi:10.1016/j.bica.2016.11.001.
  4. Besold, T., Hernández-Orallo, J. and Schmid, U. (2015). Can Machine Intelligence be Measured in the Same Way as Human intelligence? KI - Künstliche Intelligenz, 29(3), pp. 291–297. doi:10.1007/s13218-015-0361-4.
  5. Besold, T.R. and Kühnberger, K.U. (2015). Towards integrated neural-symbolic systems for human-level AI: Two research programs helping to bridge the gaps. Biologically Inspired Cognitive Architectures, 14, pp. 97–110. doi:10.1016/j.bica.2015.09.003.
  6. Besold, T.R., Garcez, A.D.A., Kühnberger, K.U. and Stewart, T.C. (2014). Neural-symbolic networks for cognitive capacities. Biologically Inspired Cognitive Architectures, 9, pp. iii–iv. doi:10.1016/S2212-683X(14)00061-9.
  7. Besold, T.R., Garcez, A.D., Kuehnberger, K.-.U. and Stewart, T.C. (2014). Neural-symbolic networks for cognitive capacities. BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES, 9, pp. III–IV. doi:10.1016/52212-683X(14)00061-9.
  8. Besold, T.R. (2014). A note on chances and limitations of psychometric AI. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8736, pp. 49–54.
  9. Besold, T.R. and Kühnberger, K.U. (2014). Applying AI for modeling and understanding analogy-based classroom teaching tools and techniques. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8736, pp. 55–61.
  10. Doran, D., Schulz, S. and Besold, T.R. What Does Explainable AI Really Mean? A New Conceptualization of
  11. Recknagel, A. and Besold, T.R. Efficient Dodgson-Score Calculation Using Heuristics and Parallel
  12. Besold, T.R., Garcez, A.D., Bader, S., Bowman, H., Domingos, P., Hitzler, P., Kuehnberger, K.-.U., Lamb, L.C., Lowd, D., Lima, P.M.V., Penning, L.D., Pinkas, G., Poon, H. and Zaverucha, G. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. .