005Bratko.pdf - Nazaj na uvod Back to Start Nazaj Back Qualitative Modelling Ivan Bratko Faculty of Computer and Information Sc University of Ljubljana

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8 Qualitative Modelling Ivan Bratko Faculty of Computer and Information Sc., University of Ljubljana Abstract. Traditional, quantitative simulation based on quantitative models aims at producing precise numerical results as answers to user’s questions about the problem domain. Such precise numerical answers are often overly elaborate, and they contain much more information than it is actually needed. In every day life, humans use common sense to reason about problems qualitatively , without numbers. In the area of Artificial Intelligence, methods exist for qualitative modelling and simulation. In this paper, we review some ideas of qualitative reasoning and modelling. We also discuss qualitative data mining as an approach to the analysis of numerical data, and Q2 learning which combines qualitative and quantitative approaches to modelling from data. Keywords: modelling, qualitative, quantitative, simulation, data mining, Q2 learning Quantitative vs. qualitative modelling Traditional, quantitative modelling and simulation give precise numerical answers. For everyday use, such answers are often overly elaborate. For example, consider the bath tub in Figure 1. Assume the tub is initially empty, there is a constant flow from the tap, and that the drain is closed, so there is no out flow. What will happen? To answer this question, the physicist's solution would be to write down a differential equation model of this system, and run this model by numerical simulation. The numerical simulation would produce a table with, say, 1000 rows, giving the exact values of the level 9 of water at consecutive tabulated time points. The table would show, for example, that the level will reach the top of the tub at 65.5 cm in 259.3 sec. For everyday use, such an elaborate answer is overkill. A common sense answer that completely suffices for everyday purposes, and is actually much more appropriate, is instead something like this: "The water level will keep increasing and will eventually reach the top. After this, water will be overflowing and cause a flood in the bathroom." This gives just a useful summary of a possibly large amount of quantitative information. The physicist's answer was quantitative , giving precise numerical information. The common sense answer was qualitative , just giving a useful summary of the large amount of quantitative information. top Level zero Figure 1: Bath tub with some input flow and closed drain. Qualitative modelling and reasoning in AI The humans are good at common sense, qualitative reasoning. Traditionally, computer- based methods are on the other hand mostly numerical -- just the opposite to common sense. Can the common sense, qualitative approach also be computerised? The area of qualitative reasoning and modelling in Artificial Intelligence aims at this (e.g. Weld and de Kleer 1990). It is concerned with the formalisation of and algorithms for qualitative reasoning about the world, producing qualitative, non-numerical answers to questions that are typically answered numerically by "proper" physics. To emphasise the contrast  • • • 