Human Conditional Reasoning in Answer Set Programming
This work addresses the challenge of bridging logical reasoning with human cognitive patterns for AI researchers and psychologists, though it is incremental as it adapts existing answer set programming methods to a specific reasoning domain.
The paper tackled the problem of modeling human conditional reasoning, including logically invalid inferences like affirming the consequent and denying the antecedent, by implementing them in answer set programming with eight types of completion, and characterized their formal properties and applications to cognitive psychology and commonsense reasoning.
Given a conditional sentence "P=>Q" (if P then Q) and respective facts, four different types of inferences are observed in human reasoning. Affirming the antecedent (AA) (or modus ponens) reasons Q from P; affirming the consequent (AC) reasons P from Q; denying the antecedent (DA) reasons -Q from -P; and denying the consequent (DC) (or modus tollens) reasons -P from -Q. Among them, AA and DC are logically valid, while AC and DA are logically invalid and often called logical fallacies. Nevertheless, humans often perform AC or DA as pragmatic inference in daily life. In this paper, we realize AC, DA and DC inferences in answer set programming. Eight different types of completion are introduced and their semantics are given by answer sets. We investigate formal properties and characterize human reasoning tasks in cognitive psychology. Those completions are also applied to commonsense reasoning in AI.