McDermott, D.


Non-monotonic logic I

Classics

'Non-monotonic' logical systems are logics in which the introduction of new axioms can invalidate old theorems. Such logics are very important in modeling the benefits of active processes which, acting in the presence of incomplete information, must make and subsequently revise assumptions in light of new observations. We present the motivation and history of such logics. We develop model and proof theories, a proof procedure, and applications for one non-monotonic logic. In particular, we prove the completeness of the non-monotoic predicate calculus and the decidability of the non-monotonic sentential calculus. We also discuss characteristic properties of this logic and its relationship to stronger logics, logics of incomplete information, and truth maintenance systems.Artificial Intelligence 13:41-72


Artificial intelligence meets natural stupidity

Classics

As a field, artificial intelligence has always been on the border of respectability, and therefore on the border of crackpottery. Many critics , have urged that we are over the border. We have been very defensive toward this charge, drawing ourselves up with dignity when it is made and folding the cloak of Science about us. On the other hand, in private, we have been justifiably proud of our willingness to explore weird ideas, because pursuing them is the only way to make progress.[Scroll to page 4 of Source document.]See also: ACM Digital Library citation. SIGART Newsletter 57:4-9.


Assimilation of new information by a natural language understanding system

Classics

It accepts sentences in a modified predicate calculus symbolism and uses plausible reasoning to visualize scenes, resolve ambiguous pronoun and noun phrase references, explain events, and make conditional predictions. Because it does plausible deduction, with tentative conclusions, it must contain a formalism for describing its reasons for its conclusions and what the alternatives are. When an inconsistency is detected in its world model, it uses its recorded information to resolve it, one way or another. It uses simulation techniques to make deductions about other creatures' motivation and behavior, assuming they are goal-oriented beings like itself.