A Symbolic Approach to Reasoning with Linguistic Quantifiers
This addresses the problem of commonsense probabilistic reasoning for AI systems, but it is incremental as it builds on existing probabilistic logic frameworks.
The paper tackles automated reasoning in probabilistic logic using linguistic quantifiers, showing that a qualitative counterpart of the quantified syllogism is viable and relatively stable with intuitive thresholds, though it has less inference power than full probabilistic methods.
This paper investigates the possibility of performing automated reasoning in probabilistic logic when probabilities are expressed by means of linguistic quantifiers. Each linguistic term is expressed as a prescribed interval of proportions. Then instead of propagating numbers, qualitative terms are propagated in accordance with the numerical interpretation of these terms. The quantified syllogism, modelling the chaining of probabilistic rules, is studied in this context. It is shown that a qualitative counterpart of this syllogism makes sense, and is relatively independent of the threshold defining the linguistically meaningful intervals, provided that these threshold values remain in accordance with the intuition. The inference power is less than that of a full-fledged probabilistic con-quaint propagation device but better corresponds to what could be thought of as commonsense probabilistic reasoning.