Derivable Belief and Hyperintensional Algorithmic Semantics



Year of publication 2017
Type Conference abstract
MU Faculty or unit

Faculty of Arts

Description Familiar arguments involving belief sentences show that possible world semantics, employed by standard epistemic logic, is untenable, since it misrepresents intuitively (in)valid inference. We confess hyperintensional, neo-fregean semantics according to which meaning is an algorithm determining the expression's denotation. Analysis of belief sentences then yields an explicit model of belief. Such models are known to be too restrictive; we thus supplement it by a specific novel version of rule-based implicit approach. Derivable belief consists of beliefs an agent is capable to achieve using derivation systems she masters. The notion of derivation system enables an apt modelling of agent's inference resources.
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