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Decompositional Modeling through Caricatural Reasoning
 In Proceedings of AAAI94
, 1994
"... Many physical phenomena are sufficiently complex that the corresponding equations afford little insight, or no analytical method provides an exact solution. Decompositional modeling (DM) captures a modeler's tacit skill at solving nonlinear algebraic systems. DM divides statespace into a patch ..."
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Many physical phenomena are sufficiently complex that the corresponding equations afford little insight, or no analytical method provides an exact solution. Decompositional modeling (DM) captures a modeler's tacit skill at solving nonlinear algebraic systems. DM divides statespace into a patchwork of simpler subregimes, called caricatures, each of which preserves only the dominant characteristics of that regime. It then solves the simpler nonlinear system and identifies its domain of validity. The varying patchwork reflects how variations in the parameters change the dominant characteristics. The patchwork is built by extracting equational features consisting of the relative strength of terms, and then exagerating and merging these features in different combinations, resulting in the different caricatural regimes. DM operates by providing strategic guidance to a pair of symbolic manipulation systems for qualitative sign and order of magnitude algebra. The approach is sufficient to re...
An integrated architecture for engineering problem solving
 Ph.D. dissertation, Northwestern Univ. Evanston, IL [Online]. Available: http://wwwstaff.it.uts.edu.au/˜ypisan/research/publications/ thesis/index.html. AND SALUSTRI: COMPUTATIONAL INTELLIGENCE IN PRODUCT DESIGN ENGINEERING 777
, 1998
"... An Integrated Architecture for Engineering Problem Solving Yusuf Pisan Problem solving is an essential function of human cognition. To build intelligent systems that are capable of assisting engineers and tutoring students, we need to develop an information processing model that captures the skills ..."
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An Integrated Architecture for Engineering Problem Solving Yusuf Pisan Problem solving is an essential function of human cognition. To build intelligent systems that are capable of assisting engineers and tutoring students, we need to develop an information processing model that captures the skills used in engineering problem solving. This thesis describes the Integrated Problem Solving Architecture (IPSA) that combines qualitative, quantitative and diagrammatic reasoning skills to produce annotated solutions to engineering problems. We focus on representing expert knowledge, and examine how control knowledge provides the structure for using domain knowledge. To demonstrate our architecture for engineering problem solving, we present a Thermodynamics Problem Solver (TPS) that uses the IPSA architecture. TPS solves over 150 thermodynamics problems taken from the first four chapters of a common thermodynamics textbook and produces expertlike solutions. iv ACKNOWLEDGMENTS I would l...
Intelligent ComputerAided Engineering
 AI MAGAZINE, VOL . 9 NO . 3, AMERICAN ASSOCIATION FOR ARTIFICIAL INTELLIGENCE,
, 1988
"... ..."
Intelligent Computer Aided Engineering
"... is even more important in these times of increasing economic competition. Engineering applications give AI an important opportunity to repay the investment society is making in it. Among the first to see that engineering is a valuable domain for AI was Gerald Sussman, who made these same arguments ..."
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is even more important in these times of increasing economic competition. Engineering applications give AI an important opportunity to repay the investment society is making in it. Among the first to see that engineering is a valuable domain for AI was Gerald Sussman, who made these same arguments over a decade ago. The work of his (now defunct) engineering problemsolving group at the Massachusetts Institute of Technology (MIT) illustrated the point well. Several important AI ideas grew out of that group’s attempts to automate aspects of engineering reasoning, including truthmaintenance systems (Doyle
Physical Idealization as Plausible Inference
, 1994
"... The analysis of physical systems almost always relies on idealized models of the objects involved. Any idealization, however, will be incorrect or insufficiently accurate some of the time. It seems reasonable, therefore, to view a physical idealization as a defeasible inference which can be withd ..."
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The analysis of physical systems almost always relies on idealized models of the objects involved. Any idealization, however, will be incorrect or insufficiently accurate some of the time. It seems reasonable, therefore, to view a physical idealization as a defeasible inference which can be withdrawn in the presence of contrary evidence. This talk discusses the consequences of such a view. We focus on examples where a system may or may not go into a state where idealizations are violated, such as dropping a ball near an open switch connected across a battery. We show that: 1. Nonmonotonic logics will try to enforce the idealization by supposing that the ball will miss the switch. This anomaly does not seem to be solvable by the kinds of techniques that have been applied to the Yale Shooting Problem, which it superficially resembles. We show that this problem is analogous to anomalies in nonmonotonic logic that are timeindependent. 2. A probabilistic analysis is possible,...
Order of Magnitude Comparisons of Distance
 Journal of Artificial Intelligence Research
, 1999
"... Order of magnitude reasoning  reasoning by rough comparisons of the sizes of quantities  is often called "back of the envelope calculation", with the implication that the calculations are quick though approximate. This paper exhibits an interesting class of constraint sets in which ..."
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Order of magnitude reasoning  reasoning by rough comparisons of the sizes of quantities  is often called "back of the envelope calculation", with the implication that the calculations are quick though approximate. This paper exhibits an interesting class of constraint sets in which order of magnitude reasoning is demonstrably much faster than ordinary quantitative reasoning. Specifically, we present a polynomialtime algorithm that can solve a set of constraints of the form "Points a and b are much closer together than points c and d." We prove that this algorithm can be applied if "much closer together" is interpreted either as referring to an infinite difference in scale or as referring to a finite difference in scale, as long as the difference in scale is greater than the number of variables in the constraint set. We also prove the firstorder theory over such constraints is decidable. 1 Introduction Order of magnitude reasoning  reasoning by rough comparisons of...
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"... formal theory of qualitative size and distance relations between regions ..."
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"... formal theory of qualitative size and distance relations between regions ..."
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