Modus Ponens Generating Function in the Class of ^-valuations of Plausibility
This work addresses a foundational issue in AI for reasoning under uncertainty, but it appears incremental as it builds on existing classes of plausibility valuations.
The paper tackled the problem of constructing inference procedures that handle uncertainties measured in ordinal scales while ensuring strict monotonicity of conclusions, and introduced a modus ponens generating function within the class of ^-valuations of plausibility that fulfills this property.
We discuss the problem of construction of inference procedures which can manipulate with uncertainties measured in ordinal scales and fulfill to the property of strict monotonicity of conclusion. The class of A-valuations of plausibility is considered where operations based only on information about linear ordering of plausibility values are used. In this class the modus ponens generating function fulfiling to the property of strict monotonicity of conclusions is introduced.