Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
Mathematical methods in artificial intelligence
Mathematical methods in artificial intelligence
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Motivation and Context-Based Multi-Robot Architecture for Dynamic Task, Role and Behavior Selections
Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics
Gestalt aspects of security patterns
IWCF'10 Proceedings of the 4th international conference on Computational forensics
Nonambiguous concept mapping in medical domain
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Recurrent Confabulation Model for Annotated Image Retrieval
International Journal of Intelligent Systems
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A new model of vertebrate cognition is introduced: maximization of cogencyp(@a@b@c@d|@e). This model is shown to be a direct generalization of Aristotelian logic, and to be rigorously related to a calculable quantity. A key aspect of this model is that in Aristotelian logic information environments it functions logically. However, in non-Aristotelian environments, instead of finding the conclusion with the highest probability of being true (a popular past model of cognition); this model instead functions in the manner of the 'duck test;' by finding that conclusion which is most supportive of the truth of the assumed facts.