This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Predicting spatial belief reasoning: comparing cognitive and AI models

Mr. Johannes Mannhardt
University Freiburg ~ Computer Science
Dr. Leandra Bucher
University Siegen
Mr. Daniel Brand
Chemnitz University of Technology ~ Predictive Analytics
Marco Ragni
TU Chemnitz ~ Behavioral and Social Sciences

Spatial relational descriptions in everyday life sometimes need to be revised in the light of new information. While there are cognitive models for reasoning about spatial descriptions there are currently no models for belief revision for the spatial domain. This paper approaches this need by (i) revisiting existing models such as verbal model (Krumnack et al., 2010) and PRISM (Ragni and Knauff,2013) and adapt them to deal with belief revision tasks, (ii) evaluate these models by testing the predictive accuracy for the individual reasoner on a previously conducted experiment by Bucher et al.(2013), (iii) provide baseline models and machine learning models, provide user-based collaborative filtering and content-based filtering methods, and provide an analysis on the individual level. Implications for predicting the individual and identifying strategies and shared similar reasoning patterns are discussed



Belief revision
spatial reasoning
cognitive models
AI models

There is nothing here yet. Be the first to create a thread.

Cite this as:

Mannhardt, J., Bucher, L., Brand, D., & Ragni, M. (2021, July). Predicting spatial belief reasoning: comparing cognitive and AI models. Paper presented at Virtual MathPsych/ICCM 2021. Via