Strong & Weak Methods: A Logical View of Uncertainty
This work is incremental, offering theoretical extensions to uncertainty management in AI for researchers and practitioners.
The paper addresses the debate on managing uncertainty in AI by proposing three arguments to extend the probability framework, including representing multiple uncertainty types, using weak methods for poorly defined problems, and symbolic representation of calculi, without challenging classical methods.
The last few years has seen a growing debate about techniques for managing uncertainty in AI systems. Unfortunately this debate has been cast as a rivalry between AI methods and classical probability based ones. Three arguments for extending the probability framework of uncertainty are presented, none of which imply a challenge to classical methods. These are (1) explicit representation of several types of uncertainty, specifically possibility and plausibility, as well as probability, (2) the use of weak methods for uncertainty management in problems which are poorly defined, and (3) symbolic representation of different uncertainty calculi and methods for choosing between them.