Praveen K. Paritosh and Kenneth D. Forbus
paritosh@cs.northwestern.edu, forbus@northwestern.edu
Qualitative Reasoning Group Department of Computer Science, Northwestern University, 1890 Maple Ave, Evanston IL 60201 USA
Abstract: Back of the envelope reasoning involves generating quantitative answers in situations where exact data and models are unavailable, where available data is often incomplete and/or inconsistent. A rough estimate generated quickly is more valuable and useful than a detailed analysis, which might be unnecessary, impractical, or impossible because the situation does not provide enough time, information, or other resources to perform one. Such reasoning is a key component of commonsense reasoning about everyday physical situations. This paper presents a similarity-based approach to such reasoning. In a new scenario or problem, retrieving a similar example from experience, so to say, sets the stage for solving the new problem by borrowing relevant modeling assumptions and reasonable values for parameters. This provides us with a very useful class of problems, which involve tightly interwoven qualitative and analogical reasoning. Incorporating effects of quantitative dimensions in similarity judgments and generalizations, hitherto unexplored, raises very interesting questions.