People seem adept at drawing tentative conclusions when premises do not lead to a necessary conclusion. In contrast, the artificial nonmonotonic reasoning systems that have been developed are complex and do not function with ease. This apparent difference between human and artificial computational reasoning is sometimes considered puzzling and frustrating - if people can do it so easily, why can't we get computers to do it easily? The present paper explores the ways in which people attempt to solve nonmonotonic problems which contain conflict and shows that people do not in fact reason about these nonmonotonic problems so easily; they jump to conclusions easily, but they do not reason so well. However, some people do manage to sometimes reason quite well, in that their reasoning is based on ideas that are (classically) logically justifiable. This paper explores differences between these reasoners and others who cope less well. It also explores how the identification of the way in which these people reason can be used to inform artificial nonmonotonic reasoning systems.
How to Cite:
Ford, M., (2005). On using human nonmonotonic reasoning to inform artificial systems. Psychologica Belgica. 45(1), pp.57–70. DOI: http://doi.org/10.5334/pb-45-1-57