Non-Monotonic Probabilistic Reasoning
Standard Models
In most computerized aids that quantify uncertainty, inference is equated with an essentially linear process, in which a model or knowledge base is built, numerical inputs assessed (e.g., weights on rules, prior probabilities of facts), and outputs generated. This linear approach omits the thinking processes by means of which an analyst selects one consistent set of probabilistic beliefs (including weights on rules and prior probabilities) out of all those that are possible.

The Non-Monotonic Probabilist

The Non-Monotonic Probabilitist defines reasoning more realistically. Actual reasoning about uncertainty in real-world tasks is typically highly iterative. The results of one line of reasoning are compared with the results of other lines of reasoning (or with direct judgment). If there is a discrepancy, the inputs, parameters, and even the structure of the model or knowledge base may be revised. The Non-Monotonic Probabilist provides direct support for the intelligent construction and modification of inference models in the light of experience with their application. Reasoning is no longer the blind application of a fixed model, but a creative cycle in which such models are built and improved with experience.

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