A normative account of defeasible and probabilistic inference
This addresses foundational issues in logic and AI for researchers in formal reasoning, but it is incremental as it builds on existing normative accounts.
The paper tackles the problem of defining logical consequence in normative terms for uncertain and defeasible inference, extending prior work to show how agents are obliged to infer conclusions in non-monotonic frameworks.
In this paper, we provide more evidence for the contention that logical consequence should be understood in normative terms. Hartry Field and John MacFarlane covered the classical case. We extend their work, examining what it means for an agent to be obliged to infer a conclusion when faced with uncertain information or reasoning within a non-monotonic, defeasible, logical framework (which allows e. g. for inference to be drawn from premises considered true unless evidence to the contrary is presented).