This dissertation conducts an investigation into nonmonotonic reasoning---forms of reasoning which allow defeasible inferences arrived at in the absence of complete information, and which, when additional information is acquired, may need to be revoked. In contrast to the mathematical notion of consequence which is based on proof---mathematical proofs, once established, are beyond reproach, no matter what additional information is acquired---nonmonotonic forms of reasoning are often employed in Artificial Intelligence, where generally only incomplete information is available, and often 'working' inferences need to be made; e.g. default inferences. The platform on which this analysis of nonmonotonic reasoning is carried out is conditional logic; a relative of modal logic. This thesis explores notions of consequence formulated in conditional logic, and explores its possible-worlds semantics, and its connection to nonmonotonic consequence relations. In particular, the notion of default consequence is explored, receiving the interpretation that something is inferred to be true by default if it holds in a `majority' of possible worlds. A number of accounts of majority-based reasoning appear in the literature. However, it is argued that some of the more well known accounts have counter-intuitive properties. An alternative definition of `majorities' is furnished, and both modal and conditional formulations of this form of inference are given and compared---favourably---with similar approaches in the literature. A second, traditional problem of reasoning in Artificial Intelligence is tackled in this thesis: reasoning about action. The treatment presented is again based on conditional logic, but also incorporates an account of dynamic logic. The semantics proposed approaches the frame problem from a different perspective; the familiar `minimal change' approach is generalised to an account based on the principle known as Occam's Razor. The conditional introduced proves to be a valuable contribution to the account given---which again is compared, and contrasted with other approaches in the literature---accommodating a causal approach to the problem of correctly determining the indirect effects of an action.