Reasoning with Defeasible Reasons
Stipe Pandžić · 2020
This thesis grew out of interests in understanding the principles of ordinary or commonsense reasoning.This type of reasoning is easily performed by most human reasoners, thus deserving the title of "ordinary" and "commonsense".Imagine that you were to see a picture of cherry blossoms from Tokyo in an October newspaper edition.Knowing that Japanese cherry normally blossoms in March or April, you reasonably conclude that the photo must be at least half-a-year old.But were you to further learn that the Tokyo temperatures this autumn are similar to those of spring's, you would be inclined to discard your original conclusion that the photo is old.This phenomenon of withdrawing conclusions upon considering additional information is known as "non-monotonicity" of inference.A good deal of what commonsense reasoning is about is connected to non-monotonic inferences.Although humans seem to easily engage in commonsense reasoning, it is notoriously difficult to systematically explain its underlying workings.This problem came to the attention of AI researchers who realized that the design of intelligent computer programs requires understanding of and ability to engineer common sense.One distinctive feature of commonsense reasoners, as opposed to ideal reasoners, is that they make errors.Reasoning errors often do not result from obtuseness or irrational behavior, but rather from a need to draw conclusions despite having only incomplete information about a relevant subject matter.If an agent has complete information about a situation and it is able to reason deductively, then its inferences are monotonic and any addition of new information will not question previous conclusions.Ordinary reasoners seldom (if ever) have complete information about any contingent fact and they are "forced" to draw conclusions that can turn 1