Reasoning about uncertainty

By Joseph Y. Halpern. 2003

1,283 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fda132e890ebd5d5d9ae802335c11405e9735725

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Citations in the corpus

The cognitive structure of surprise: looking for basic principles (2007, methodology): “For what concerns the probabilistic fragment of our logic we inherit all axioms and inference rules given in Halpern (2003); Fagin and Halpern (1994).”
Continuity analysis of programs (2010, background): “Uncertainty in computation has long been a question of interest in computing [8].”
Possibility theory and statistical reasoning (2006, background): “Qualitative possibility theory is also studied in detail by Halpern [64] in connection with other theories of uncertainty.”
Non-monotonicity in NPI licensing (2014, background): “For a thorough discussion of these issues, see Halpern (2003), Chap.”
Reasoning about Probabilities in Unbounded First-Order Dynamical Domains (2017, background): “Reasoning about probabilities is widely studied in the logical literature − see, for example, [Gaifman, 1964; Nilsson, 1986; Halpern, 2003]; we briefly survey the major camps below.”
Logical Induction (2016, background): “67”
Aligning Superintelligence with Human Interests: A Technical Research Agenda (2015, background): “…but a study of logical uncertainty in the restricted context of assigning probabilities to logical sentences goes back at least to (cid:32)Lo´s (1955) and Gaifman (1964), and has been investigated by many, including Halpern (2003), Hutter et al. (2013), Demski (2012), Russell (2014), and others.”
Probabilistic Multi-Label Classification with Sparse Feature Learning (2013, unknown): “The Bayesian network models for multi-label learning [de Waal and van der Gaag, 2007; Bielza et al., 2011] although take steps in this direction by learning separate feature subnetwork, class subnetwork, and feature-class bridge subnetwork, they nevertheless are limited to problems with a small numb”
Neurosymbolic AI: the 3rd wave (2020, unknown): “mbolically. Reasoning, in many cases too, is preferred to be precise and not approximate, although there are cases where approximate or human-like reasoning become more eﬃcient than logical deduction [32]. (b) Commonsense and Combinatorial Reasoning: Another key distinction that is worth making expl”
Gradable epistemic modals, probability, and scale structure (2010, background): “…a probabilistic analysis , including what interpretation of probability is appropriate, exactly what types of information are relevant, whether probability statements have ordinary truth-conditions (Yalcin 2010a), and whether even probability is a rich enough representation format (Halpern 2003).”
The philosophy of Bayes’ factors and the quantification of statistical evidence (2016, background): “/ Journal of Mathematical Psychology 72 (2016) 6–18 11”