ProSocrates

Problem-solving (Newell & Simon, 1972),prosocrates human creative cognition (Finke, Ward, & Smith, 1992) (Boden, 2003), spatial cognition (Freksa, 2015), Qualitative modelling (Forbus, 2010) and computational creativity (Colton & Wiggins, 2012) are topics often treated separately, despite their major potential for synergies.

Problem-solving has been approached in different ways by AI and the study of human cognition. The ability to flexibly solve novel problems with little training is a fundamental component in human intelligence. For example, rescheduling a trip due to unexpected circumstances, playing Tangram/origami or finding a solution to an ill-structured problem (Newell, 1969) are well known tasks in human problem-solving. Common-sense reasoning, model building and the ability to creatively solve novel problems have an important role in the challenge of approaching general artificial intelligence.

Computational creativity focuses on building creative artificial systems capable of creative feats similar to those achieved by humans (Colton, 2012). Through its applied nature, computational creativity is a great way of studying the type of algorithms and system implementations that can be used to approach artificially creative results, however the processes and representations in the field are rarely compared to those used by humans. Human creative cognition investigates the way humans solve a multiplicity of creative tasks (Mednick & Mednick, 1971), from the simple (coming up with an alternative use for an object) to the complex (solving insight problems)(Batchelder & Alexander, 2012), asking questions about process. However, no cognitive modeling universal set of tools exists for the pursuit of implementing computational approaches to test hypotheses in a unified manner.

Spatial cognition and reasoning (Freksa, 2015) is also known to contribute to the development of abstract thought, and to have a role in problem solving. Spatial cognition studies have shown that there is a strong link between success in Science, Technology, Engineering and Math (STEM) disciplines and spatial abilities (Newcombe, 2010). Qualitative modeling (Forbus, 2011) concerns the representations and reasoning that people use to understand continuous aspects of the world. Qualitative representations are also thought to be closer to the cognitive domain, as showed by models for object sketch recognition (Forbus et al., 2011), for solving Raven’s Progressive Matrices intelligence test (Lovett & Forbus, 2017), for solving oddity tasks (Lovett & Forbus, 2011), for 3D perspective descriptions matching (Falomir, 2015) and for paper folding reasoning (Falomir, 2016). In the context of creativity, spatial descriptors and qualitative shape and colour descriptors and their similarity formulations were tools for object replacement and object composition in the theoretical approach presented by Olteteanu & Falomir, (2016) to solve Alternative Uses Test.

Cognitive Systems is a great intersection point for all these topics, bringing together the perspectives of human cognition, cognitive modeling and cognitive artificial intelligence. The interactions between (i) problem-solving and creativity in the context of cognitive systems (Oltețeanu & Falomir, 2015, 2016; Oltețeanu et al. 2015, 2016; Oltețeanu 2014, 2016a-d) and between (ii) qualitative modelling and spatial cognition (Falomir, 2015, 2016) were studied by the proposed guest editors in the last few years.

The focus of ProSocrates is to bring the previous described disciplines together, by bringing in dialogue specialists from each of the fields, and authors of experimental, theoretical and computational work which combine perspectives from at least two of these 4 topics (problem-solving, spatial cognition/reasoning, cognitive systems and creativity). The interactions between problem-solving and creativity (Olteteanu & Falomir, 2015, 2016; Olteteanu, 2014) and between qualitative modelling and spatial cognition (Falomir, 2015, 2016) were studied by the editors of SI-ProSocrates in the last few years. The larger aim is to produce theoretical tools, approaches and methodologies to approach cognitive systems for creative and spatial problem solving in a manner that would benefit from such interdisciplinary bootstrapping.

References

  • Batchelder, W. H., & Alexander, G. E. (2012). Insight Problem Solving: A Critical Examination of the Possibility of Formal Theory. The Journal of Problem Solving , 5 (1), 56-100.
  • Boden, M. (2003). The Creative Mind: Myths and Mechanisms. Routledge.
  • Colton, S. (2012). The painting fool: Stories from building an automated painter. Computers and creativity , 3-38.
  • Colton, S., & Wiggins, G. A. (2012). Computational Creativity: The Final Frontier? ECAI Proceedings, (pp. 21-26).
  • Finke, R. A., Ward, T. B., & Smith, S. M. (1992). Creative cognition: Theory, research, and applications. MIT press Cambridge, MA.
  • Forbus, K. D. (2011). Qualitative modeling. Wiley Interdisciplinary Reviews: Cognitive Science, 2(4):374–391.
  • Falomir, Z. (2015). A qualitative model for reasoning about 3d objects using depth and different perspectives. In Lechowski, T., Waga, P., and Zawidzki, M., editors, LQMR 2015 Workshop, volume 7 of Annals of Computer Science and Information Systems, pages 3–11. PTI.
  • Falomir, Z. (2016). Towards a qualitative descriptor for paper folding reasoning. In Proc. of the 29th International Workshop on Qualitative Reasoning. Co-located with IJCAI’2016 in New York, USA.
  • Forbus, K., Usher, J., Lovett, A., Lockwood, K., and Wetzel, J. (2011).  CogSketch: Sketch understanding for Cognitive Science Research and for Education.  Topics in Cognitive Science, 3(4), pp 648-666.
  • Freksa, C. (2015). Strong spatial cognition. In S. Fabrikant, M. Raubal, M. Bertolotto, C. Davies, S. Freundschuh, & S. Bell (Ed.), Spatial Information Theory, COSIT 2015 Proceedings, LNCS 9368, pp. 65-86.
  • Lovett, A. and Forbus, K. (2011). Cultural commonalities and differences in spatial problem-solving: A computational analysis. Cognition, 121(2):281 – 287.
Lovett, A. and Forbus, K. (2017). Modeling visual problem solving as analogical reasoning. Psy-chological Review, 124(1):60 – 90.
  • Mednick, S. A., & Mednick, M. (1971). Remote associates test: Examiner’s manual. Houghton Mifflin.
  • Newell, A., & Simon, A. (1972). Human problem solving. Englewood Cliffs, NJ:Prentice Hall.
  • Newcombe, N. (2010). Picture this: Increasing math and science learning by improving spatialthinking. American Educator, 34(2):29–35.
  • Olteteanu, A.-M., & Falomir, Z. (2015). comRAT-C: A computational compound Remote Associates Test solver based on language data and its comparison to human performance. Pattern Recognition Letters, 67, 81-90.
  • Olteteanu, A.-M., & Falomir, Z. (2016). Object replacement and object composition in a creative cognitive system. towards a computational solver of the Alternative Uses Test. Cognitive Systems Research, 39:15-32.
  • Oltețeanu, A.-M. (2014) Two general classes in creative problem-solving? An account based on the cognitive processes involved in the problem structure – representation structure relationship. In Proceedings of the Workshop “Computational Creativity, Concept Invention, and General Intelligence”, Publications of the Institute of Cognitive Science, Osnabrück.