abductív

Abductív, in the sense of abductive reasoning, refers to a form of logical inference that begins with an observation or set of observations and seeks the most plausible explanation or hypothesis that would account for them. It is often described as “inference to the best explanation” and is associated with the idea of generating hypotheses rather than proving them conclusively.

Abduction is distinct from deduction, which derives conclusions that must be true if the premises are true, and from induction, which generalizes from observed cases to broader generalizations. Abductive reasoning produces hypotheses that explain the data, but these hypotheses are not guaranteed to be true and typically require further testing or evidence.

Historically, the concept is credited to the philosopher Charles S. Peirce, who introduced the term and discussed abductive inference as a fundamental mode of scientific reasoning. In contemporary contexts, abduction is studied in philosophy of science, logic, and artificial intelligence, particularly in abductive logic programming and nonmonotonic reasoning, where systems attempt to explain observations by hypothesizing causes or events.

Applications of abductiv reasoning are widespread. In medicine, clinicians generate diagnostic hypotheses to explain symptoms. In science, researchers propose hypotheses explaining anomalous data. In engineering and troubleshooting, abductive reasoning helps identify likely root causes. In AI, abductive methods support hypothesis generation, fault diagnosis, and iterated model refinement.

In Hungarian usage, abductív serves as the term for this reasoning style and related methods, expressing the idea of forming plausible explanations from observed evidence.