Probabilistic semantics for vagueness

semantics;pragmatics;vagueness;probability;game theory;artificial intelligence;reinforcement learning;Bayes;Bayesian;linguistics;philosophy;statistical inference;cognitive science;Grice;formal semantics;Montague;Sorites;logic;dynamic semantics;truth-conditions

Lee, Steven Fong-Yi

关键词: semantics;    pragmatics;    vagueness;    probability;    game theory;    artificial intelligence;    reinforcement learning;    Bayes;    Bayesian;    linguistics;    philosophy;    statistical inference;    cognitive science;    Grice;    formal semantics;    Montague;    Sorites;    logic;    dynamic semantics;    truth-conditions;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/106242/LEE-DISSERTATION-2019.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

In this dissertation I argue that truth-conditional semantics for vague predicates, combined with a Bayesian account of statistical inference incorporating knowledge of truth-conditions of utterances, generates false predictions regarding negations and metalinguistic inference. I thus propose a fundamentally probabilistic semantics for vagueness on which the meaning of a vague predicate is a likelihood function on the states it encodes, with these likelihoods being generated via reinforcement learning in a signaling game.