Authors:
Marco Ragni
and
Andreas Klein
Affiliation:
Center for Cognitive Science, Germany
Keyword(s):
Knowledge Representation and Reasoning, Preferential Reasoning, Artificial Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
Composition tables are used in AI for knowledge representation and to compute transitive inferences. Most of these tables are computed by hand, i.e., there is the need to generate them automatically. Furthermore, human preferred solutions and errors in reasoning can be explained as well based on these tables. First, we will report briefly psychological results about the preferences in calculi. Then we show that we can train ANNs on a simple calculus like the point algebra and the trained ANN is able to correctly solve larger calculi such as the Cardinal Direction Calculus. As human prefer specific conclusions, we are able to show that based on the ANN, which is trained on the preferred conclusions of the point algebra alone, is able to reproduce the results on the larger calculi as well. Finally, we show that humans preferred solutions can be adequately described by the networks. A brief discussion of the structure of successful ANNs conclude the paper.