Falkenhainer, B., Forbus, K., Gentner, D. (1989) The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, pp 1-63.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....entities, an obvious byproduct of metaphor is the suggestion of analogy. The process of analogy is that of applying previously gathered knowledge in new situations. Analogy is involved in learning and reasoning [21] and it has been suggested that analogy is a fundamental cognitive process [22]. For these reasons, analogy has been an area of interest in cognitive science. Gentner and Gentner [23] conducted empirical experiments that tested the hypothesis that analogies are used in generating inferences (i.e. the generative analogy hypothesis) as opposed to the hypothesis that thought ....

.... nucleus is not equivalent to the sun [41] However, the electron and the planet share a revolvesaround (x,y) relation [41] Within SMT, an analogy is a mapping between domains in which few attributes match, but many relations map (e.g. our solar system is like an atom) 41] Falkenhainer et al. [22] have described a Structure Mapping Engine (SME) that is based on the SMT of analogy. The SME works out many computational details of SMT [22] Structure mapping has three stages [22] 63 1. Access: Retrieve a similar analogue. 2. Mapping: Find the correspondences between the subject and its ....

[Article contains additional citation context not shown here]

B. Falkenhainer, K. D. Forbus and D. Gentner, "The Structure Mapping Engine: Algorithm and Examples," in Artificial Intelligence, vol. 41, pp. 1-63, 1990.

....to new contexts, as opposed to the application of general rules of inference as is done in resolution theorem proving or tableaux methods. This approach is now known as case based or analogical reasoning, which employs a variety of mechanisms for retrieving, reusing, revising, and retaining cases [21, 22]. But despite the large amount of research to date on story understanding using frames and scripts, and on case based and analogical reasoning in terms of concrete experiences, no system to date has been endowed with a large database of commonsense knowledge in the form of frames and scripts ....

Falkenhainer, B., Forbus, K.D. and Gentner, D. (1990). The structure-mapping engine: algorithm and examples. ArtificialIntelligence 41, pp. 1-63.

....a sneeze or a cough people readily answer that a sneeze is brighter [16] The contrast model assumes that only identical features can match and does not envisage a matching process that attempts to preserve higher order compatibilities. More current models of comparison and analogy (e.g. [2, 13, 11]) do establish relational correspondences when comparing objects. These models tend to prefer mappings between analogs when 1) identical features can be mapped to one another, 2) there are higher order compatibilities and structures replicate in both analogs, 3) the mapping between the two analogs ....

....the two analogs is or almost is one to one. Although these constraints are common to all successful models of human comparison, it is not always clear how these constraints are weighted and manifested in models. In other words, different models may adopt widely different matching algorithms (e.g. [2, 11]) but can be quite similar at the computational level of analysis (in the sense of Marr, 1982) It is important to know what the commonalities and differences of competing models are in order to identify the critical issues that deserve empirical investigation. The goal of this paper is to ....

FALKENHAINER, B., FORBUS, K. D., AND GENTNER,D. The structure mapping engine: Algorithm and examples. Artificial Intelligence 41 (1989), 1--63.

....on real Cyc data. The algorithm is called MDL OC, for Minimum Description Length Orthogonal Clustering. A more detailed description, including results on a diagnosis problem also used by Michalski and Chilausky [MC80] real Cyc data, and an analogical mapping also used byFalkenhainer et al. [FFG89], is given in Derthick [Der90] 2 Problem Representation For the purposes of this paper, the goal of a learning knowledge representation system is to develop a domain theory from a set of ground atomic assertions that allows it to decide the truth or falsity of other ground assertions. The ....

Brian Falkenhainer, Kenneth D. Forbus, and Dedre Gentner. The structure-mapping engine: Algorithms and examples. Artificial Intelligence, 41:1--63, 1989.

....if the target problem has been solved appropriately; storage is storing the target analog in memory for potential reuse. Traditional conceptual and computational theories of analogy have focused primarily on causal knowledge and inferences (see [Holyoak and Thagard, 1997] Bhatta and Goel, 1997] [Falkenhainer et al. 1990] for examples) Psychological research, however, shows that visual reasoning often occurs in analogy [Holyoak and Thagard, 1997] Pedone et al. 1999] Some recent theories [Bhatta and Goel, 1997] Griffith et al. 2000] represent structural knowledge in addition to causal knowledge. Structural ....

....pixels. VAMP.2 represented images as agents with local knowledge. Mapping is done using ACME ARCS [Holyoak and Thagard, 1997] a constraint satisfaction connectionist network. The radiation problem mapping was one of the examples to which VAMP.2 was applied. The Structure Mapping Engine, or SME [Falkenhainer et al. 1990] finds the best mapping of elements between two domains. But SME typically is applied to instances where the situations are represented as having causal and structural knowledge. SME has been applied to visual knowledge in a system called MAGI [Ferguson, 1994] which takes visual representations ....

Brian Falkenhainer, Kenneth D. Forbus, and Dedre Gentner. The Structure mapping engine: algorithm and examples. Artificial Intelligence, 41:163, 1990.

....that analogies are built on an implicit parallelism between two information structures, computational modelling of the analogy process has been the focus of much work. This work has largely focused on creating more efficient algorithms for identifying the largest structure mapping including [Falkenhainer, Forbus and Gentner, 1989; Keane, and Brayshaw 1988; Veale, O Donoghue and Keane, 1999; Salvucci and Anderson, 2001] The second activity revolves around determining the boundaries between this problem data and irrelevant background information. This requires identifying clusters of information that can be ....

Falkenhainer, B. Forbus, Gentner, D. 1989 "The Structure Mapping Engine: Algorithm and Examples", Artificial Intelligence, 41, 1-63.

....somewhat more conducive to the integration of metaphor, these typically are not designed with the use of metaphor in mind, and the implicit nature of many metaphors makes them easy for developers to ignore. This is an unfortunate situation since metaphor is central to language [17, 18] cognition [19, 20, 21, 22], and diverse aspects of computer science [23, 24, 25] The rube paradigm seeks, in part, to change this situation by conceptually and visually integrating metaphors in M P. Further, rube encourages developers to devise their own metaphors in accordance with the observation that one of the main ....

B. Falkenhainer, K. D. Forbus & D. Gentner (1990) The Structure Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41, 1-63.

....and representation language. Introduction Most theoretical and computational accounts of analogical reasoning posit transfer of relational knowledge. Causal and functional relationships in particular have been the focus of many theories (see Holyoak Thagard 1997, Bhatta Goel 1997, Falkenhainer et al. 1990, and Winston 1980 for examples) Consider, for example, a traditional account (Gick Holyoak 1996) of Duncker s radiation problem (1926) In this task, experimental participants read a story in which a problem is solved: A general with a large army wants to overthrow a dictator who lives in a ....

....on Galatea, crossbar suppressed would be a transformation of removing an element from the image, where that element was the crossbar and the image was a prototype letter f . Then the transformation could be applied to the other letters one by one. The Structure Mapping Engine, or SME (Falkenhainer et al. 1990), is an agent that takes two systems and finds the best mapping of elements between them. This system is often applied to examples where the systems are represented as having causal and schematic knowledge. SME has been specifically applied to imagistic knowledge in a system called MAGI (Ferguson ....

Falkenhainer, B., K. D. Forbus, & D. Gentner (1990). The Structure mapping engine: algorithm and examples. Artificial Intelligence (41) pp1-63.

....indentations in the figure boundary. MAGI then performs a self similarity mapping over this relational description (Figure 1 shows an example of GeoRep s representation and MAGI s mapping) MAGI s algorithm (see Ferguson, 1994, in preparation) is very similar to the Structure Mapping Engine (SME; Falkenhainer, Forbus, Gentner, 1989; Forbus, Ferguson Gentner, 1994) MAGI s self similarity mappings are created using a local to global mapping process that enforces a set of six mapping constraints. Four of these constraints are adopted from SME: 1) the tiered identicality constraint, which allows only expressions with ....

Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, 1-63.

....t k to shape elements e i with transformation arguments a k . 2. 3 Similarity in Shape Similarities in shapes are identified by attributes, physical structure (Gero and Jun, 1995) continuous transformation (March and Steadman, 1971; Steadman, 1983; Mitchell, 1990) or organising structure (Falkenhainer et al., 1989 90) Shapes are recognised and categorised in terms of their attributes, such as, colour, line type, thickness, and so on. Shapes that have the same physical structure in terms of topology and geometry are regarded as similar shapes: congruent shapes. They are transformed in various ways, for ....

Falkenhainer, B., Forbus, K. D. and Gentner, D. (1989/90). The structure-mapping engine: algorithm and examples, Artificial Intelligence, 41: 1-63.

....Finding the best mapping could be carried out element by element, but actually involves interactive activation. jon ICS Lecture 13 February 22, 2000 Restructuring 26 Holyoak and Thagard 1989 provide a parallel constrant satisfaction model, which establishes: Structural consistency (cf. Falkenhainer, Forbus and Gentner 1989): elements map into functionally equivalent roles One to one mappings eg: fortress can map to only one of tumour or rays. It follows that thinking and perception may have more qualities in common than meet the eye. jon ICS Lecture 13 February 22, 2000 The rest of the course 27 Next ....

Falkenhainer, Forbus and Gentner (1989) The structure mapping engine: Algorithm and examples. Articial Intelligence, 41, 1-63.

.... (learning) Presenting only isomorphs restricts learning to small problem classes, while too large a degree of structural dissimilarity can result in failure of transfer and thereby obstructs learning (Pirolli Anderson, 1985) 2) A plausible cognitive model of analogical problem solving (cf. Falkenhainer, Forbus, Gentner, 1989; Hummel Holyoak, 1997) should generate correct transfer only for such source target relations where human subjects perform successfully. 3) Computer systems which employ analogical or case based reasoning techniques (Carbonell, 1986; Schmid Wysotzki, 1998) should refrain from analogical ....

....are relevant for calculating the solution. We do neither claim that human problem solvers without experience with this problem domain represent the relevant problem structure completely and correctly, nor do we make assumptions whether analogical problem solving is better modelled on a symbolic (Falkenhainer et al. 1989) or a subsymbolic (Hummel Holyoak, 1997) level. We constructed this normatively complete symbolic graph representation to explore the impact of different analytically given structural source target relations on empirical observable transfer success. In the following we will analyze ....

[Article contains additional citation context not shown here]

Falkenhainer, B., Forbus, K., & Gentner, D. (1989). The structure mapping engine: Algorithm and example. Artificial Intelligence, 41, 1-63.

.... maximal sets of consistent correspondences (matches) between relevant elements of the source and elements in the target, called global mappings (or gmaps, as the are called in the Structure Mapping Engine (SME) literature) For mapping we propose to use a modified version of Falkenheiner s SME (Falkenheiner et al. 1993). SME takes as input two descriptions, one of the source and one of the target, and produces as output a set of gmaps of the source onto the target. Each gmap contains a maximal set of matches. Here maximal means that adding any match to it would violate the consistency of the gmap. SME also ....

Falkenheiner, B., Forbus K. and Gentner, D. (1993) . The structure-mapping engine: Algorithm and examples, In: Buchanan and Wilkins (eds.) Readings in Knowledge Acquisition and Learning, Morgan Kaufmann Publishers, pp. 695-726.

....using index based retrieval techniques from Case Based Reasoning (Kolodner, 1993) For more diverse templates with rich structure and different types of template elements, a more powerful mechanism would be needed. Use of the Structure Mapping Engine (SME) Falkenhainer, Forbus Gentner, 1986) (Falkenhainer, Forbus Gentner, 1989) might be appropriate. Characteristics of the task, user, objects and object relationships would be given as the problem , and, after some pre filtering of stored templates based on a limited number of primary features, SME could be used to provide an indication of the matches, and their quality, ....

B. Falkenhainer, K. D. Forbus & D. Gentner. The Structure-Mapping Engine: algorithm and examples. Artificial Intelligence, 1989, Vol. 41, No. 1, pp. 1-63.

....manner. MAC FAC Many Are Called but Few Are Chosen (Gentner and Forbus, 1991) uses the identicality constraint as a basis for selecting multiple alternative source domains from an extensive memory base. These domains are then assessed for structural similarity to the target, as performed by SME (Falkenhainer, Forbus, and Gentner, 1989). The best domain identified is selected as the favoured source for the given target analog. ARCS (Thagard et al., 1990) first identifies potential sources using a similarity metric being based on WordNet. Each identified source is assessed for structural similarity to the target using an ACME ....

Falkenhainer, B. Forbus, Gentner, D. "The Structure Mapping Engine : Algorithm and Examples", Artificial Intelligence, 41, 1-63, 1989.

....the ACME model. 0 200000 400000 600000 800000 T i m e (s) No. Pixels FIG 1 METAPHOR INTERPRETATION Metaphor Interpretation, and its sister discipline of Analogical Reasoning, are areas of research from the domain of Cognitive Modelling. A variety of models exist in each domain; SME (Falkenhainer et al., 1989) ACME (Holyoak and Thagard, 1989) IAM (Keane et al., 1993) For the purposes of this paper we are only interested in Gentners (1983) Structural Similarity requirement, thus metaphor and analogy are treated as synonyms and the distinctions between them are ignored as they are not directly relevant ....

Falkenhainer B, Forbus K D, Gentner D (1989), "The Structure-Mapping Engine : Algorithm and Examples", Artificial Intelligence 41, 1-63.

....Introduction The use of analogies is a powerful and ubiquitous strategy in human reasoning and problem solving. A lot of (symbolic, connectionist hybrid) computational models have been proposed with the aim of getting more precise insights in the underlying processes (Anderson Thompson, 1989; Falkenhainer, Forbus, Gentner, 1989; Hummel Holyoak, 1997) and with the aim of exploiting this strategy in AI applications (cf. case based reasoning) Most of the computational models are focusing on analogical access and mapping thereby neglecting two crucial aspects of analogical problem solving: 1) generation of problem ....

Falkenhainer, B., Forbus, K., & Gentner, D. (1989). The structure mapping engine: Algorithm and example.

....first approach is, to present the new problem by the initial solution steps and to expand the stored program schemes accordingly 1 . Similarity can then be determined by a tree metric ( Lu79 ] The second approach uses the structure mapping engine (SME) of Falkenhainer, Forbus and Gentner ( FFG89 ] Here the new problem is presented as partial program without the recursive relation. Both approaches are reported in chapter 3. An architecture for a learning program synthesis system We will continue our work in the areas described above with the aim of building an integrated learning ....

B. Falkenhainer, K.D. Forbus, and D. Gentner. The structure mapping engine: Algorithm and example. Artificial Intelligence, 41:1--63, 1989.

....are considered distinct if the structural elements (e.g. batteries, pipes) in the domains are different. 6 Related Research The IDEAL system evolves from our earlier work on the Kritik project [Goel, 1991a; 1991b; 1992] IDEAL s SBF models, for example, are directly borrowed from Kritik. Falkenhainer, Forbus Gentner [1989] describe the use of mental models for enabling analogical transfer. But they do not address the issue of analog retrieval. Our work on IDEAL suggests that designers mental models are also useful for addressing the indexing issues in analog retrieval. Like Kritik and Kritik2, the design analog ....

B. Falkenhainer, K. Forbus, and D. Gentner. The Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41:1--63, 1989.

....context of automatic programming (Schmid Wysotzki, 1998) But we believe, that it also contains useful ideas for cognitive modeling. Problems and problem schemes are represented in a common format, namely as graphs or trees. This kind of representation is suitable for modeling structure mapping (Falkenhainer, Forbus, Gentner, 1989) and also enabels us to analyze structural relations between problems in a formally sound way. In the following we will first present some measures for describing structural relations between problems. Afterwards we will present the results on some experiments about the adaptability of ....

....discussion and further work to be done. 2 Analyzing the structural relation between problems A common way to represent problems as graphs is, to introduce nodes for objects, relations and functions and arcs for relations between these entities. The graph representation of the water flow problem (Falkenhainer et al. 1989) is given in figure 1a. If the order of the arguments of a relation or a function is relevant, the arcs can be labeled with ordering constraints. If the arguments of a relation represent specific roles these can be also used as arc labels. The solution of a problem usually is modeled as sequence ....

Falkenhainer, B., Forbus, K., & Gentner, D. (1989). The structure mapping engine: Algorithm and example. Artificial Intelligence, 41, 1-63.

.... (learning) Presenting only isomorphs restricts learning to small problem classes, while too large a degree of structural dissimilarity can result in failure of transfer and thereby obstructs learning (Pirolli Anderson, 1985) 2) A plausible cognitive model of analogical problem solving (cf. Falkenhainer, Forbus, Gentner, 1989; Hummel Holyoak, 1997) should generate correct transfer only for such source target relations where human subjects perform successfully. 3) Computer systems which employ analogical or case based reasoning techniques (Carbonell, 1986; Schmid Wysotzki, 1998) should refrain from analogical ....

....are relevant for calculating the solution. We do neither claim that human problem solvers without experience with this problem domain represent the relevant problem structure completely and correctly, nor do we make assumptions whether analogical problem solving is better modelled on a symbolic (Falkenhainer et al. 1989) or a subsymbolic (Hummel Holyoak, 1997) level. We constructed this normatively complete symbolic graph representation to explore the impact of different analytically given structural source target relations on empirical observable transfer success. In the following we will analyze ....

[Article contains additional citation context not shown here]

Falkenhainer, B., Forbus, K., & Gentner, D. (1989). The structure mapping engine: Algorithm and example. Artificial Intelligence, 41, 1-63.

....by excitatory or inhibitory connections, respectively. Using this approach, Johnson and Zhang [30] integrated the symbolic Soar model of cognition with a connectionist model of evaluating hypotheses. Another example is Goldstone s SIAM model of human similarity estimation. Falkenhainer et al. [31] developed the Structural Mapping Engine, where consistent mapping hypotheses reinforce each other like connected active nodes in a neural net. Holyoak and Thagard [32] also perform mappings for analogical reasoning by using a net of hypotheses which reinforce or inhibit each other. The mapping ....

B. Falkenhainer, K.D. Forbus, and D. Gentner. The Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41:1--63, 1989.

....strategy. The empirical results are in correspondence with a theoretical analysis of mapping vs replay effort for this problem domain. 1 Introduction Typically, analogical problem solving is modeled as transformation of the source solution based on the mapping of source and target structure (Falkenhainer, Forbus, Gentner, 1989; Hummel Holyoak, 1997) Carbonell (1986) characterizes the problem solving strategy described by these structure mapping based models as transformational analogy (TA) and contrasts it with derivational analogy (DA) He argues that, from a computational point of view, transformational analogy is ....

Falkenhainer, B., Forbus, K., & Gentner, D. (1989). The structure mapping engine: Algorithm and example. Artificial Intelligence, 41, 1-63.

.... of research in analogical reasoning are the study of analogical reasoning as a cognitive process and as one of the main ingredients of creativity [Boden 1994] Holyoak and Thagard 1995] Finke et al. 1992] types of analogical reasoning (e.g. transformational versus derivational) Gentner 1993] [Falkenheimer et al. 1993] [Carbonell 1993] and applications of analogical reasoning to learning [Carbonell 1993] planning [Veloso 1994] design [Qian Gero 1992] Bhatta Goel 1994] Bhatta et al. Goel 1997] Of special interest for our work is the research in purpose directed analogy [Kedar Cabelli 1988] 1.1 ....

....the literature what we call relational similarity is usually referred to as structural similarity . We have avoided this terminology to prevent confusion with the structural level of description of a design. Our implementation of matching is based on the idea of the Structure Mapping Engine [Falkenheimer et al. 1993]. As such it tries to find the highest level relational similarity between the target design to be simplified and the source designs considered. Simple relational matching with respect the corresponding level of designs is inefficient because it would consider all the designs in the design data ....

B. Falkenheimer, K. Forbus & D. Gentner. "The structure-mapping engine: Algorithm and examples ", In: Readings in Knowledge Acquisition and Learning, (Eds.) Buchanan & Wilkins, Morgan Kaufmann Publishers, San Mateo, CA, 1993, pp.695-726.

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Falkenhainer, B., Forbus, K., Gentner, D. (1989) The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, pp 1-63.

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Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: Algorithm and examples.

....indicated by drawing arrows, labeled with the semantics of the relationship (e.g. hypothesis, intended next state, etc. Comparison is an important operation on sketches [20] States can also be compared to each other by analogy, via a drag and drop interface that invokes our analogy software [7,13] to compare them (Figure 9) These comparisons can be used to reflect on alternate choices, and work is in progress to hypothesize enemy intent based on historical precedents. USER EXPERIMENTS AND FEEDBACK nSB has benefited from substantial formative feedback from experts in three different ....

Falkenhainer, B., Forbus, K., Gentner, D. (1989) The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, pp 1-63.

....be equally important for the use of analogy in case based reasoning, since they ensure that candidate inferences are well defined and that stronger arguments are preferred [11] Two simulations based on structure mapping are relevant to this paper. The first, the Structure Mapping Engine (SME) [1,7,11], is a cognitive simulation of analogical matching. How SME works is described in [6,7,11] Two characteristics are key to the work described here: SME operates in polynomial time, using a greedy merge algorithm to provide a small number of mappings that best satisfy the constraints of ....

....that candidate inferences are well defined and that stronger arguments are preferred [11] Two simulations based on structure mapping are relevant to this paper. The first, the Structure Mapping Engine (SME) 1,7,11] is a cognitive simulation of analogical matching. How SME works is described in [6,7,11]. Two characteristics are key to the work described here: SME operates in polynomial time, using a greedy merge algorithm to provide a small number of mappings that best satisfy the constraints of structure mapping. SME s results are consistent with a large and growing body of ....

[Article contains additional citation context not shown here]

Falkenhainer, B., Forbus, K., Gentner, D. (1989) The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, pp 1-63.

....for a sequence as an illustration. ANALOGICAL COMPARISON OF SKETCHES Analogy provides a powerful means of entering and testing knowledge. Currently sKEA enables users to compare two layers, which is useful for examining similarities and differences. We use the Structure Mapping Engine (SME) [11,18] to perform the matching. SME is a general purpose analogical matcher, which operates in polynomial time. Figure 7: Example of the metalayer Figure 8: sKEA supports combined visual conceptual analogies SME has been successfully used to model a variety of psychological phenomena, and has ....

Falkenhainer, B., Forbus, K., Gentner, D. (1989) The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, pp 1-63.

.... to their plan illustrated by the system highlighting the appropriate visual elements of the sketch (Figure 3) The analogies are based on the visual and conceptual descriptions constructed during sketching, which are fed to a general purpose analogy matcher, the Structure Mapping Engine (SME) [10,14]. The cases are also created via sketching, with additional information about the relative wisdom of particular decisions stored in the case as a basis for providing advice. The nuSketch COA Creator has been developed using a joint applications development model, using a combination of Army ....

Falkenhainer, B., Forbus, K., Gentner, D. (1989) The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, pp 1-63.

....model, shows how difference detection depends on structural alignment. Based on these two models, we describe three diagram design defects that occur in repetition based diagrams. MAGI Similarity and analogical comparison can be modeled as the structural alignment of propositional descriptions. (Falkenhainer, Forbus, Gentner, 1989; Forbus, Ferguson, Gentner, 1994; Gentner, 1983; Gentner, 1989; Goldstone, 1994; Holyoak Thagard, 1989; Keane Brayshaw, 1988) MAGI (Ferguson, 1994, In preparation) is the first model linking regularity detection with similarity. MAGI is based on the idea that symmetry and repetition (both ....

....In preparation) is the first model linking regularity detection with similarity. MAGI is based on the idea that symmetry and repetition (both visual and conceptual) can be viewed as a similarity mapping between a description and itself. Using an extension of the Structure Mapping Engine (SME; Falkenhainer et al. 1989; Forbus et al. 1994) MAGI uses structural alignment to detect regularity within a single description. Like SME, MAGI s mapping process is computationally tractable because it operates in a local to global fashion. Individual alignments are constructed in parallel and then aggregated into global ....

Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, 1-63.

....design, the Guru uses a cognitive simulation of similarity based retrieval, MAC FAC (Forbus, Gentner, Law, 1995) to retrieve relevant cases. Concrete advice as to how to apply the idea of the case to the student s design is generated by a cognitive simulation of analogical matching, SME (Falkenhainer, Forbus, Gentner, 1989; Forbus, Ferguson Gentner, 1994) The use of cognitively motivated analogical processing software has two advantages over the typical state of the art in case based reasoning (CBR) systems. First, most CBR systems require hand indexing of new cases by experts familiar with both the domain and ....

Falkenhainer, B., Forbus, K., Gentner, D. The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, 1989, pp 1-63.

....be equally important for the use of analogy in case based reasoning, since they ensure that candidate inferences are well defined and that stronger arguments are preferred [12] Two simulations based on structure mapping are particularly relevant to this paper. The Structure Mapping Engine (SME) [1,5,7] is a cognitive simulation of analogical matching. Given base and target descriptions, SME finds globally consistent interpretations via a localto global match process. SME begins by proposing correspondences, called match hypotheses, in parallel between statements in the base and target. Then, ....

Falkenhainer, B., Forbus, K., & Gentner, D. (1989) The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, pp 1-63.

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Falkenhainer, B., Forbus, K., Gentner, D. (1989). The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, 1-63.

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Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41, 1-63.

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Falkenhainer, B., Forbus, K. and Gentner, D. (1989). Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41.

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Falkenhainer, B., Forbus, K. and Gentner, D. (1989). Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41, pages 1-63.

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Falkenhainer, B.; Forbus, K. D.; and Gentner, D. 1989. The structure--mapping engine: algorithm and examples. Artificial Intelligence 41:1--63.

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B. Falkenhainer, K. Forbus, and D. Gentner. The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41:1--63, 1989.

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Falkenhainer, B.; Forbus, K.; and Gentner, D.: Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41, pages 1-63 (1989)

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Falkenhainer, B.; Forbus, K.; and Gentner, D.: Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41, pages 1-63 (1989)

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B. Falkenhainer, K. D. Forbus, & D. Gentner. (1989). Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, 1-63.

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Falkenhainer, B., Forbus, K. and Gentner, D. Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41, pages 1-63. 1989.

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Brian Falkenhainer, Kenneth Forbus, Dedre Gentner. (1989). Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41, pages 163, 1989.

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B. Falkenhainer, K. Forbus, and D. Gentner. The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41(1):1--63, 1989.

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Forbus, K.D., Falkenhainer, B., Gentner, D. 1989. The structure mapping engine: Algorithm and examples. Artificial Intelligence 41(1):1-63.

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Falkenhainer, B., Forbus, K.D. and Gentner, D. (1990). The structure-mapping engine: algorithm and examples. Artificial Intelligence 41, pp. 1-63.

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Brian Falkenhainer, Kenneth D. Forbus, and Dedre Gentner. The Structure mapping engine: algorithm and examples. Artificial Intelligence, 41:163, 1990.

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Falkenhainer, B., Forbus, K., Gentner, D. (1989) The Structure-Mapping Engine: Algorithm and examples. Artificial Intelligence, 41, pp 1-63.

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Falkenhainer, B., K. D. Forbus, & D. Gentner (1990). The Structure mapping engine: algorithm and examples. Artificial Intelligence (41) 1--63.

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