Structure-Mapping Engine Research Articles

Sketch Worksheets in STEM Classrooms: Two Deployments

Sketching can be a valuable tool for science education, but it is currently underutilized. Sketch worksheets were developed to help change this, by using AI technology to give students immediate feedback and to give instructors assistance in grading. Sketch worksheets use visual representations automatically computed by CogSketch, which are combined with conceptual information from the OpenCyc ontology. Feedback is provided to students by comparing an instructor’s sketch to a student’s sketch, using the Structure-Mapping Engine. This paper describes our experiences in deploying sketch worksheets in two types of classes: Geoscience and AI. Sketch worksheets for introductory geoscience classes were developed by geoscientists at University of Wisconsin-Madison, authored using CogSketch and used in classes at both Wisconsin and Northwestern University. Sketch worksheets were also developed and deployed for a knowledge representation and reasoning course at Northwestern. Our experience indicates that sketch worksheets can provide helpful on-the-spot feedback to students, and significantly improve grading efficiency, to the point where sketching assignments can be more practical to use broadly in STEM education.

Extending SME to Handle Large-Scale Cognitive Modeling.

Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine (SME) since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence for SME as a process model, and summarize its role in simulating similarity-based retrieval and generalization. Then we describe five techniques now incorporated into the SME that have enabled it to tackle large-scale modeling tasks: (a) Greedy merging rapidly constructs one or more best interpretations of a match in polynomial time: O(n2 log(n)); (b) Incremental operation enables mappings to be extended as new information is retrieved or derived about the base or target, to model situations where information in a task is updated over time; (c) Ubiquitous predicates model the varying degrees to which items may suggest alignment; (d) Structural evaluation of analogical inferences models aspects of plausibility judgments; (e) Match filters enable large-scale task models to communicate constraints to SME to influence the mapping process. We illustrate via examples from published studies how these enable it to capture a broader range of psychological phenomena than before.

What Difference Reveals About Similarity

Detecting that two images are different is faster for highly dissimilar images than for highly similar images. Paradoxically, we showed that the reverse occurs when people are asked to describe how two images differ--that is, to state a difference between two images. Following structure-mapping theory, we propose that this disassociation arises from the multistage nature of the comparison process. Detecting that two images are different can be done in the initial (local-matching) stage, but only for pairs with low overlap; thus, "different" responses are faster for low-similarity than for high-similarity pairs. In contrast, identifying a specific difference generally requires a full structural alignment of the two images, and this alignment process is faster for high-similarity pairs. We described four experiments that demonstrate this dissociation and show that the results can be simulated using the Structure-Mapping Engine. These results pose a significant challenge for nonstructural accounts of similarity comparison and suggest that structural alignment processes play a significant role in visual comparison.

Analogical Dialogue Acts: Supporting Learning by Reading Analogies in Instructional Texts

Analogy is heavily used in instructional texts. We introduce the concept of analogical dialogue acts (ADAs), which represent the roles utterances play in instructional analogies. We describe a catalog of such acts, based on ideas from structure-mapping theory. We focus on the operations that these acts lead to while understanding instructional texts, using the Structure-Mapping Engine (SME) and dynamic case construction in a computational model. We test this model on a small corpus of instructional analogies expressed in simplified English, which were understood via a semi-automatic natural language system using analogical dialogue acts. The model enabled a system to answer questions after understanding the analogies that it was not able to answer without them.

A two-stage framework for UML specification matching

Context Specification matching techniques are crucial for effective retrieval processes. Despite the prevalence for object-oriented methodologies, little attention has been given to Unified Modeling Language (UML) for matching. Objective This paper presents a two-stage framework for matching two UML specifications and quantifying the results based on the systematic integration of their structural and behavioral similarities in order to identify the candidate component set for reuse. Method The first stage in the framework is an evaluation of the similarities between UML class diagrams using the Structure-Mapping Engine (SME), a simulation of the analogical reasoning approach known as the structure-mapping theory. The second stage, performed on the components identified in the first stage, is based on a graph-similarity scoring algorithm in which UML class diagrams and sequence diagrams are transformed into an SME representation and a Message-Object-Order Graph (MOOG). The effectiveness of the proposed framework was evaluated using a case study. Results The experimental results showed a reduction in potential mismatches and an overall high precision and recall. Conclusion It is concluded that the two-stage framework is capable of performing more precise matching compared to those of other single-stage matching frameworks. Moreover, the two-stage framework could be utilized within a reuse process, bypassing the need for extra information for retrieval of the components described by UML.

Solving Geometric Analogy Problems Through Two‐Stage Analogical Mapping

Evans' 1968 ANALOGY system was the first computer model of analogy. This paper demonstrates that the structure mapping model of analogy, when combined with high-level visual processing and qualitative representations, can solve the same kinds of geometric analogy problems as were solved by ANALOGY. Importantly, the bulk of the computations are not particular to the model of this task but are general purpose: We use our existing sketch understanding system, CogSketch, to compute visual structure that is used by our existing analogical matcher, Structure Mapping Engine (SME). We show how SME can be used to facilitate high-level visual matching, proposing a role for structural alignment in mental rotation. We show how second-order analogies over differences computed via analogies between pictures provide a more elegant model of the geometric analogy task. We compare our model against human data on a set of problems, showing that the model aligns well with both the answers chosen by people and the reaction times required to choose the answers.

Using analogical mapping to simulate time-course phenomena in perceptual similarity

We present a computational model of visual similarity. The model is based upon the idea that perceptual comparisons may utilize the same mapping processes as are used in analogy. We use the Structure Mapping Engine (SME), a model of Gentner’s structure-mapping theory of analogy, to perform comparison on representations that are automatically generated from visual input. By encoding visual scenes incrementally and sampling the output of SME at multiple stages in its processing, we are able to model not only the output of similarity judgments, but the time course of the comparison process. We demonstrate the model’s effectiveness by replicating the results from three psychological studies that bear on the time course of comparison.

The Latent Relation Mapping Engine: Algorithm and Experiments

Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.

Analogical Reasoning and Conceptual Change: A Case Study of Johannes Kepler

The work of Johannes Kepler offers clear examples of conceptual change. In this article, using Kepler's work as a case study, we argue that analogical reasoning facilitates change of knowledge in four ways: (a) highlighting, (b) projection, (c) rerepresentation, and (d) restructuring. We present these four mechanisms within the context of structure-mapping theory and its computational implementation, the structure-mapping engine. We exemplify these mechanisms using the extended analogies Kepler used in developing a causal theory of planetary motion.

MAC/FAC: A model of similarity-based retrieval

We present a model of similarity-based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two-stage process. The first stage uses a computationally cheap, non-structural matcher to filter candidate long-term memory items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure-mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity-based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity.

MAC/FAC: A Model of Similarity‐Based Retrieval

We present a model of similarity‐based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two‐stage process. The first stage uses a computationally cheap, non‐structural matcher to filter candidate long‐term memory items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure‐mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity‐based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity.

Constraints on analogical mapping: A comparison of three models

Three theories of analogy have been proposed that are supported by computational models and data from experiments on human analogical abilities. In this article we show how these theories can be unified within a common metatheoretical framework that distinguishes among levels of informational, behavioral, and hardware constraints. This framework clarifies the distinctions among three computational models in the literature: the Analogical Constraint Mapping Engine (ACME), the Structure-Mapping Engine (SME), and the Incremental Analogy Machine (IAM). We then go on to develop a methodology for the comparative testing of these models. In two different manipulations of an analogical mapping task we compare the results of computational experiments with these models against the results of psychological experiments. In the first experiment we show that increasing the number of similar elements in two analogical domains decreases the response time taken to reach the correct mapping for an analogy problem. In the second psychological experiment we find that the order in which the elements of the two domains are presented has significant facilitative effects on the ease of analogical mapping. Of the three models, only IAM embodies behavioral constraints and predicts both of these results. Finally, the immediate implications of these results for analogy research are discussed, along with the wider implications the research has for cognitive science methodology.

Constraints on Analogical Mapping: A Comparison of Three Models

Three theories of analogy have been proposed that are supported by computational models and data from experiments on human analogical abilities. In this article we show how these theories can be unified within a common metatheoretical framework that distinguishes among levels of informational, behavioral, and hardware constraints. This framework clarifies the distinctions among three computational models in the literature: the Analogical Constraint Mapping Engine (ACME), the Structure‐Mapping Engine (SME), and the Incremental Analogy Machine (IAM). We then go on to develop a methodology for the comparative testing of these models. In two different manipulations of an analogical mapping task we compare the results of computational experiments with these models against the results of psychological experiments. In the first experiment we show that increasing the number of similar elements in two analogical domains decreases the response time taken to reach the correct mapping for an analogy problem. In the second psychological experiment we find that the order in which the elements of the two domains are presented has significant facilitative effects on the ease of analogical mapping. Of the three models, only IAM embodies behavioral constraints and predicts both of these results. Finally, the immediate implications of these results for analogy research are discussed, along with the wider implications the research has for cognitive science methodology.

High-level perception, representation, and analogy: A critique of artificial intelligence methodology

Abstract High-level perception—the process of making sense of complex data at an abstract, conceptual level—is fundamental to human cognition. Through high-level perception, chaotic environmental stimuli are organized into mental representations that are used throughout cognitive processing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dismissal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models—notably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought—-and argue that these are flawed precisely because they downplay the role of high-level perception. Further, we argue that perceptual processes cannot be separated from other cognitive processes even in principle,and therefore that traditional artificial-intelligence models cannot be defended by supp...

The Structure-Mapping Engine: Algorithm and Examples (Book)

(1992). The Structure-Mapping Engine: Algorithm and Examples (Book) Metaphor and Symbolic Activity: Vol. 7, No. 2, pp. 109-113.

The structure-mapping engine: Algorithm and examples

This paper describes the structure-mapping engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's structure-mapping theory of analogy, and provides a “tool kit” for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O(N 2). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work.