Towards Tractable Inference for Resource-Bounded Agents.

For a machine to act with common sense, it is not enough that information about commonsense things be written down in a formal language. What actual knowledge — i.e. conclusions available for informing actions — a formalization is meant to provide cannot be determined without some specification of what sort of reasoning is expected. The traditional view in epistemic logic says that agents see all logical consequences of the information they have, but that would give agents capabilities far beyond common sense or what is physically realizable. To work towards addressing this issue, we introduce a new epistemic logic, based on a three-valued version of neighborhood semantics, which allows for talking about the effort used in making inferences. We discuss the advantages and limitations of this approach and suggest that the ideas used in it could also find a role in autoepistemic reasoning.