Non-monotonic Reasoning in Artificial Intelligence

Complexity of Non-Monotonic Logics

Over the past few decades, non-monotonic reasoning has developed to be one of the most important topics in computational logic and artificial intelligence. The non-monotonicity here refers to the fact that, while in usual (monotonic) reasoning adding more axioms leads to potentially more possible conclusions, in non-monotonic reasoning adding new facts to a knowledge base may prevent previously...

Non monotonic reasoning in artificial intelligence pdf

Non-monotonic Inference

Non-monotonic inference is inference that is defeasible: in contrast with deductive inference, the conclusions drawn may be withdrawn in the light of further information, even though all the original premises are retained. Much of our everyday reasoning is like this, and a non-monotonic approach has applications to a number of technical problems in artificial intelligence. Work on formalizing n...

Can Formal Non-monotonic Systems Properly Describe Human Reasoning?

Monotonic (logical) reasoning makes the strong claim that an inference cannot be contradicted by future information; an assumption contrary to everyday life experience. This assumption is relaxed in non-monotonic reasoning. However, there are only few formal non-monotonic theories of reasoning that have inspired psychological theory-building. Can formal systems such as cumulative logic (system ...

A Taxonomy for Argumentative Frameworks based on Labelled Deduction

Artificial Intelligence has long dealt with the issue of finding a suitable formalization for reasoning with incomplete and potentially inconsistent information. Defeasible argumentation [SL92,CML00,PraVre99] has proven to be a successful approach in many respects, since it naturally resembles many aspects of commonsense reasoning (see [CML00,PraVre99] for details). Besides, recent work [PraVre...