Reasoning with Imperfect Information and Knowledge

Nowadays, smart and intelligent computer systems are part of our everyday lives. However, computational intelligence is rather different from human intelligence, in particular, the problem of reasoning with uncertain, imprecise, incomplete, or inconsistent (in short, imperfect) information often renders computational methods relying on strict or deductive logics obsolete or fallacious whereas humans cope with this problem with relative ease. Reasoning with imperfect information plays a central role in practical deliberation and rational decision making. Indeed, models of human context-dependent reasoning that synthesise logical, philosophical and psychological aspects would provide novel insights into rational human reasoning. Several of such formal approaches have been developed in philosophy and artificial intelligence (AI) over the past decades, and an increasing interest in these new formal methods for rational human reasoning under uncertainty have emerged in psychology. Likewise, philosophers and computer scientists have shown an increased attention to the experimental methods of psychology recently. In particular for computer scientists and AI researchers, it is becoming more and