Rational Nonmonotonic Reasoning
This work addresses foundational issues in AI and logic for agents needing to reason with incomplete evidence, though it appears incremental as it builds on existing probability logic frameworks.
The paper tackles the problem of interpreting nonmonotonic reasoning by proposing a possible-worlds model based on decision theory and probability logic, resulting in a rational system that treats tentative conclusions as bets rather than factual assertions.
Nonmonotonic reasoning is a pattern of reasoning that allows an agent to make and retract (tentative) conclusions from inconclusive evidence. This paper gives a possible-worlds interpretation of the nonmonotonic reasoning problem based on standard decision theory and the emerging probability logic. The system's central principle is that a tentative conclusion is a decision to make a bet, not an assertion of fact. The system is rational, and as sound as the proof theory of its underlying probability log.