Tractable Form of Non-Monotonic Reasoning to Generate Belief Set

Nonmonotonic reasoning is intended to apply specifically in situation where the initial information is incomplete. The most important property of traditional system is monotonicity, i.e. addition of new facts to the database or to the theory does not result in any previous fact being retracted. There does not exist any inconsistency between the old statements and newly added statements and is assume that situation do not change. But in real world problem situation changes and so many new assumptions are generated. A monotonic reasoning system cannot work efficiently in real life environments because information available is always incomplete. These problem can be solved using nonmonotonic reasoning. Nonmonotonic reasoning tend to be introduced proof theoretically and little attention is paid to their semantic characteristics or their computational tractability. I present a different approach for construction of consistent belief set using least fix point semantics, declarative semantics and procedural semantics