Inference and Plausible Reasoning in a Natural Language Understanding System Based on Object-Oriented Semantics
This work addresses the challenge of enhancing logical reasoning in text-based AI systems for specific applications like criminology and business, though it appears incremental as it builds on existing object-oriented semantics approaches.
The paper tackles the problem of performing inference and plausible reasoning in natural language understanding systems for logical questions where direct object comparison fails, presenting algorithms that combine deduction and social psychology-based reasoning to address tasks like hypothesis checking, action planning, and cause determination in domains such as criminology and medicine.
Algorithms of inference in a computer system oriented to input and semantic processing of text information are presented. Such inference is necessary for logical questions when the direct comparison of objects from a question and database can not give a result. The following classes of problems are considered: a check of hypotheses for persons and non-typical actions, the determination of persons and circumstances for non-typical actions, planning actions, the determination of event cause and state of persons. To form an answer both deduction and plausible reasoning are used. As a knowledge domain under consideration is social behavior of persons, plausible reasoning is based on laws of social psychology. Proposed algorithms of inference and plausible reasoning can be realized in computer systems closely connected with text processing (criminology, operation of business, medicine, document systems).