Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
Formalizing nonmonotonic reasoning systems
Artificial Intelligence
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Since hardly any of people's everyday decisions are made with certainty, it is often necessary to retract earlier conclusions on the basis of new input. This aspect of common-sense reasoning in humans is often cited as a raison d'dtre for nonmonotonic theories. Going beyond this intuitive notion, this paper is based on well-documented psychological experiments. In these experiments it turns out that inferences are often remarkably unresponsive to new input even if the original basis for the inferences is discredited. The focus in the present paper, therefore, is on modeling this pervasive, yet counter-intuitive retraction behavior.