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Using Strategies and AND OR Decomposition for Back of the Envelope Reasoning Praveen K Paritosh paritosh cs northwestern edu Kenneth D Forbus 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 and 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 In this paper we present an approach that uses strategies and creates an AND OR decomposition to solve such questions We present BotESolver a general purpose problem solving framework that uses strategies represented by suggestions and keeps track of problem solving progress in an AND OR tree BotE Solver can currently solve some fairly interesting back of the envelope estimation questions from different domains We are building a library of strategies which currently contains 23 strategies 1 Introduction One goal of qualitative reasoning QR is to understand and model common sense Forbus and Gentner 1997 proposed a hybrid model of QR where analogical reasoning and qualitative reasoning are tightly interwoven In this paper we look at quantitative estimation also called rough estimation back of the envelope analysis etc Back of the envelope BotE analysis involves the estimation of rough but quantitative answers to questions where the models and the data might be incomplete In domains like engineering design or experimental science one often comes across situations where a rough answer generated quickly is more valuable than waiting for more information or



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