JosephY. Halpern

Title: JosephY. Halpern

Main Research Question: How can we reason about expectation in different representations of uncertainty?

Methodology: The authors introduce a propositional logic for reasoning about expectation, where the semantics depends on the underlying representation of uncertainty. They provide sound and complete axiomatizations for the logic in various cases, including probability, sets of probability measures, belief functions, and possibility measures.

Results: They show that this logic is more expressive than the corresponding logic for reasoning about likelihood in the case of sets of probability measures, but equi-expressive in the case of probability, belief, and possibility. They also demonstrate that satisfiability for these logics is NP-complete.

Implications: The work provides a logical framework for reasoning about expectation in different representations of uncertainty, which can be useful in various fields such as artificial intelligence, economics, and decision theory. The results also have implications for the complexity of reasoning in these logics.

Link to Article: https://arxiv.org/abs/0312037v1 Authors: arXiv ID: 0312037v1