In case-based reasoning, a given problem is solved by adapting the solutions to similar problems encountered in the past. A major task in case-based problem solving is to generate modifications that are useful for adapting a previous solution to solve the present problem. We describe a model-based method that uses qualitative models of cjises for generating useful modifications. The qualitative model of a case expresses a problem solver's comprehension of how the solution satisfies the constraints of the problem. We illustrate the model based method in the context of case-based design of physical devices. A designer's understanding of how the structure of a previously encountered design produces its functions is expressed in the form of a function structure model. The functional differences between a given problem and a specific case are mapped into structural modifications by a family of modification generation plans. Each plan is applicable to a specific type of functional difference, and uses the function structure model to identify the specific components that need to be modified. We discuss the evaluation of this model-based method in

In experience-based (or case-based) reasoning, new problems are solved by retrieving and adapting the solutions to similar problems encountered in the past. An important issue in experience-based reasoning is to identify different types of knowledge and reasoning useful for different classes of caseadaptation tasks. In this paper, we examine a class of non-routine case-adaptation tasks that involve patterned insertions of new elements in old solutions. W e describe a modelbased method for solving this task in the context of the design of physical devices. The method uses knowledge of generic teleological mechanisms (GTMs such as cascading. Old designs are adapted to meet new functional specifications by accessing and instantiating the appropriate G T M . The Kritik2 system evaluates the computational feasibility and sufficiency of this method for design adaptation.

Metacognitive skills are critical in learning but difficult to teach. Thus the question becomes how can we facilitate metacognitive tutoring? We present an exploratory learning environment called MILA-T with embedded metacognitive tutors imitating five functional roles of teachers in classrooms. We tested MILA–T in a controlled experiment with 237 middle school students. We examine the impact of MILA–T on the models of a natural phenomenon constructed by the students. We find that students with access to MILA–T wrote better evidential justifications for their models, and thus, deliver better-justified models for the phenomenon. We also find that these improvements persisted during a transfer task. These results lend support for organizing metacognitive tutoring around the functional roles of teachers for supporting inquiry-driven modeling

Recognition of geometrical patterns seems to be an important aspect of human intelligence. Geometric pattern recognition is used in many intelligence tests, including Dehaene’s odd-one-out test of Core Geometry (CG)) based on intuitive geometrical concepts (Dehaene et al., 2006). Earlier work has developed a symmetry-based cognitive model of Dehaene’s test and demonstrated performance comparable to that of humans. In this work, we further investigate the role of symmetry in geometrical intuition and build a cognitive model for the 2-Alternative Forced Choice (2-AFC) variation of the CG test (Marupudi & Varma 2021). In contrast to Dehaene’s test, 2-AFC leaves almost no space for cognitive models based on generalization over multiple examples. Our symmetry-based model achieves an accuracy comparable to the human average on the 2-AFC test and appears to capture an essential part of intuitive geometry.

Humans appear to often solve problems in a new domain by transferring their expertise from a more familiar domain. However, making such cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions used by designers. However, one important yet unexplored issue is where these generic mechanisms come from. W e hypothesize that they are acquired incrementally from problem-solving experiences in familiar domains by generalization over patterns of regularity. Three important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. In this paper, we show that mental models in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we show how the model-based learning method integrated with similarity-based learning addresses the issues in generalization from experiences.