Artificial Intelligence
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Uncertainty, belief, and probability
Computational Intelligence
Modal logics for qualitative possibility and beliefs
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A Simple Modal Logic for Reasoning about Revealed Beliefs
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Belief functions on MV-algebras of fuzzy events based on fuzzy evidence
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Reasoning about fuzzy belief and common belief: with emphasis on incomparable beliefs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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In this paper we introduce a new logical approach to reason explicitly about Dempster-Shafer belief functions. We adopt the following view: one just starts with Boolean formulas ϕ and a belief function on them; the belief of ϕ is taken to be the truth degree of the (fuzzy) proposition Bϕ standing for "ϕ is believed". For our complete axiomatization (Hylbert-style) we use one of the possible definitions of belief, namely as probability of (modal) necessity. This enables us to define a logical system combining the modal logic S5 with an already proposed fuzzy logic approach to reason about probabilities. In particular, our fuzzy logic is the logic LII½ which puts Lukasiewicz and Product fuzzy logics together.