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An elementary belief function logic
Besides, the case of belief functions has been studied independently, and a belief function logic has been obtained by extending the modal logic S5 to graded modalities using {\L}ukasiewicz logic, albeit using relational semantics.
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding.
On the Qualitative Comparison of Decisions Having Positive and Negative Features
However, contraryto the latter framework that presupposes genuine numerical assessments, human agents often decide on the basis of an ordinal ranking of the pros and the cons, and by focusing on the most salient arguments.
Qualitative Possibilistic Mixed-Observable MDPs
Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POMDPs used to model situations where the agent's initial belief or observation probabilities are imprecise due to lack of past experiences or insufficient data collection.
Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (1992)
This is the Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, which was held in Stanford, CA, July 17-19, 1992
Updating with Belief Functions, Ordinal Conditioning Functions and Possibility Measures
The relations between Spohn's model and possibility theory are emphasized and Spohn's updating rule is contrasted with the Jeffrey-like rule of conditioning in possibility theory.
Automated Reasoning Using Possibilistic Logic: Semantics, Belief Revision and Variable Certainty Weights
In case where only lower bounds of necessity measures are involved, a semantics is proposed, in which the completeness of the extended resolution principle is proved.
Modeling uncertain and vague knowledge in possibility and evidence theories
This paper advocates the usefulness of new theories of uncertainty for the purpose of modeling some facets of uncertain knowledge, especially vagueness, in AI.
