Abstract not found

This paper begins with a brief review of SME and MAC/FAC, our simulations of matching and retrieval. Next I lay out several arguments for exploring analogy in the large, including why it is now very feasible and what we can learn by such explorations. A new constraint on cognitive simulations, the Integration Constraint, is proposed: A cognitive simulation of some aspect of analogical processing should be usable as a component in larger-scale cognitive simulations. I believe that the implications of this new constraint for cognitive simulation of analogy are far-reaching. After that, two explorations of larger-scale phenomena are described. First, I describe a theoretical framework in which we model common sense reasoning as an interplay of analogical and first-principles reasoning. Second, I describe how SME and MAC/FAC have been used in a case-based coach that is accessible to engineering thermodynamics students worldwide via electronic mail. These examples show that exploring analogy in the large can provide new insights and new challenges to our simulations. Finally, the broader implications of this approach are discussed.

Abstract not found

: Learning how to create, test, and revise models is a central skill in scientific reasoning. We argue that qualitative modeling provides an appropriate level of representation for helping middle-school students learn to become modelers. We describe a system we have created that uses visual representations to provide a studentfriendly notation for creating qualitative models. This system is currently undergoing pilot testing in Chicago Public School classrooms, using curricula developed in collaboration with teachers. Contact address: Ken Forbus Qualitative Reasoning Group, Northwestern University 1890 Maple Avenue, Evanston, IL, 60201, USA email: forbus@nwu.edu Voice: 847-491-7699 Fax: 847-491-5258 2 1 Introduction Modeling is a central skill in scientific reasoning. Learning to formulate, analyze, test, and revise models is a crucial aspect of understanding science, and critical to helping students become active, lifelong learners. Supporting students in articulating models of a ...

This paper describes an analogy ontology, a formal representation of some key ideas in analogical processing, that supports the integration of analogical processing with first-principles reasoners. The ontology is based on Gentner's structure-mapping theory, a psychological account of analogy and similarity. The semantics of the ontology are enforced via procedural attachment, using cognitive simulations of structure-mapping to provide analogical processing services. Queries that include analogical operations can be formulated in the same way as standard logical inference, and analogical processing systems in turn can call on the services of first-principles reasoners for creating cases and validating their conjectures.

Understanding common sense reasoning about the physical world is one of the goals of qualitative reasoning research. This paper describes how we combine qualitative mechanics and analogy to solve problems posed as sketches. The problems are drawn from the Bennett Mechanical Comprehension Test, which is used to evaluate technician candidates. We discuss sketch annotations, which define conceptual quantities in terms of visual measurements, how modeling decisions are made by analogy, and how analogy can be used to frame comparative analysis problems. Experimental results are presented indicating that this approach has promise. 1

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 SOLVE, a general-purpose problem solving framework that uses strategies represented by suggestions, and keeps track of problem solving progress in an AND/OR tree. SOLVE 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

Back of the envelope (BotE) 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. We present an implemented system, BotE-Solver , that can solve about a dozen estimation questions like "What is the annual cost of healthcare in USA?" from different domains using a library of strategies and the Cyc knowledge base. BotE-Solver is a general-purpose problem solving framework that uses strategies represented as suggestions, and keeps track of problem solving progress in an AND/OR tree. A key contribution of this paper is a knowledge level analysis [Newell, 1982] of the strategic knowledge used in BotE reasoning. We present a core collection of seven powerful estimation strategies that provides broad coverage for such problem solving. We hypothesize that this is the complete set of back of the envelope problem solving strategies. We present twofold support for this hypothesis: 1) an empirical analysis of all problems (n=44) on Force and Pressure, Rotation and Mechanics, Heat, and Astronomy from Clifford Swartz's "Back-of-the-Envelope Physics" [Swartz, 2003], and 2) an analysis of strategies used by BotE-Solver.

We argue for heuristic reasoning as a solution to the brittleness problem. Heuristic reasoning methods exploit the information processing structure of the reasoning system and the structure of the environment to produce reasonable answers when knowledge and/or computational resources for finding the perfect correct answer might not exist. Capturing all the heuristics to generate reasonable answers might not be as colossal of a project as it might first seem: we conjecture that there are about fifteen heuristic domains, and each of them have approximately ten heuristic methods. 1

Understanding how analogy is used in problem solving is an important problem in cognitive science. This paper describes a model of using worked solutions to solve new problems, in terms of structure-mapping processes in the Companions cognitive architecture. The Educational Testing Service independently evaluated the flexibility of the system by using AP Physics problems that were systematically varied to test different types of transfer. We also show that the model provides an explanation for many of the analogy events in VanLehn’s (1998) analysis of the use of analogy by students