Multilayer feedforward networks are universal approximators
Neural Networks
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
On the Treatment of Incomplete Knowledge in Formal Concept Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Modal Logic for Evaluating Formulas in Incomplete Contexts
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Perspectives of Neural-Symbolic Integration
Perspectives of Neural-Symbolic Integration
A connectionist cognitive model for temporal synchronisation and learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
A fully connectionist model generator for covered first-order logic programs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Formal concept analysis in information science
Annual Review of Information Science and Technology
Artificial General Intelligence
Artificial General Intelligence
Treating incomplete knowledge in formal concept analysis
Formal Concept Analysis
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Modern cognitive science [1] indicates that concepts stem from individual experience, which more concretely means that an agent's concept system is generated by interactions between an agent's body and the environment it lives in. In this study we present an approach that will enable Artificial Brains to generate embodied conceptual systems, including a sophisticated introspection mechanism that will allow them to transcend their initial conceptual limitations. Our approach is based on extensions to formal concept analysis. We use incomplete formal contexts to represent the sensorimotor information of the ''body'' of an Artificial Brain, and then use uncertain formal concept analysis as a mathematical tool to settle various problems related to embodied concept formation. After proving some theorems, we show that 3-valued Lukasiewicz logic is the right instrument for our purpose, overcoming the shortcomings of the existing methods. We also describe how to use neural-symbolic integration to allow this sort of approach to provide not only advanced AI functionality but also approximate simulation of aspects of human brain function.