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Existing BDI architectures are typically composed of a set of customizable components that are composed in a reasoning cycle.
The implementation of induction in a reasoning system presents very challenging search control problems.
It is important to appreciate the difference between a logical calculus and its corresponding implementation in a reasoning program.
It consists essentially of a number of inferential rules (and/or axioms) that one is happy to accept regardless of the situation in a reasoning process.
The other element is a strategy of searching for a proof, which determines a way the premises are selected in a reasoning process.
With such knowledge, a set of expert rules based on this ontology are involved in a reasoning process to infer levels of alerts or suggestions for the users when the intensity of the activity is detected as dangerous for her health.
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The proposed algorithm consists in a heuristic reasoning algorithm based on the expert knowledge of the process.
The role of learning in a causal reasoning system is significant.
Finally, there is a relaxation process possible fromp tos which needs to be included in a statistical reasoning.
Recent work has elegantly addressed the question regarding specific roles for α and β oscillations in a probabilistic reasoning task.
The consensus framework is implemented in an evidential reasoning context.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com