Interpretation-driven mapping: A framework for conducting search and rerepresentation in parallel for computational analogy in design

2015 ◽  
Vol 29 (2) ◽  
pp. 185-201 ◽  
Author(s):  
Kazjon Grace ◽  
John Gero ◽  
Rob Saunders
Keyword(s):  
Computational Model ◽  
Analogical Reasoning ◽  
Fundamental Problem ◽  
Reasoning Systems ◽  
Mapping Process ◽  
Representational Change ◽  
Visual Analogy

AbstractThis paper presents a framework for the interactions between the processes of mapping and rerepresentation within analogy making. Analogical reasoning systems for use in design tasks require representations that are open to being reinterpreted. The framework, interpretation-driven mapping, casts the process of constructing an analogical relationship as requiring iterative, parallel interactions between mapping and interpreting. This paper argues that this interpretation-driven approach focuses research on a fundamental problem in analogy making: how do the representations that make new mappings possible emerge during the mapping process? The framework is useful for both describing existing analogy-making models and designing future ones. The paper presents a computational model informed by the framework Idiom, which learns ways to reinterpret the representations of objects as it maps between them. The results of an implementation in the domain of visual analogy are presented to demonstrate its feasibility. Analogies constructed by the system are presented as examples. The interpretation-driven mapping framework is then used to compare representational change in Idiom to that in three previously published systems.

Galileo and the Mountains of the Moon: Analogical Reasoning, Models and Metaphors in Scientific Discovery

2004 ◽  
Vol 4 (3-4) ◽  
pp. 451-483 ◽  
Author(s):  
Marta Spranzi
Keyword(s):  
Analogical Reasoning ◽  
Scientific Discovery ◽  
The Moon ◽  
Target Domain ◽  
Source Domain ◽  
The Earth ◽  
Mapping Process ◽  
Salient Features

AbstractThis paper is about the use of analogical reasoning, models and metaphors in Galileo's discovery of the mountains of the moon, which he describes in the Starry Messenger, a short but groundbreaking treatise published in 1610. On the basis of the observations of the Moon he has made with the newly invented telescope, Galileo shows that the Moon has mountains and that therefore it shares the same solid, opaque and rugged nature of the Earth. I will first reconstruct Galileo's reasoning, and illustrate the counterintuitive and quasi-circular way in which discovery depends on analogy: in order for analogical reasoning to succeed in bridging ontological gaps and thus serve as a discovery tool, a certain similarity between what are considered as radically different domains has to be presupposed. More particularly, in order for analogical reasoning to lead to genuine discoveries, salient features have to be selected in the source domain that will be mapped onto the target domain. There is disagreement as to how this mapping is successfully carried out: the syntactical (Dedre Gentner), pragmatic (Paul Thagard) and ontological-categorical (Rom Harré) approaches, all illuminate some features of this selection in the mapping process. On the basis of an analysis of Galileo's discovery, I will argue that we need a different "bootstrapping" approach which involves the construction of an imaginary temporary model encompassing both the source and the target domains, and which is occasionally strengthened by metaphors which serve as incomplete transitional models.

Cross-domain thinking: Common representation format or generalized mapping process?

2002 ◽  
Vol 25 (6) ◽  
pp. 683-684
Author(s):  
Peter F. Dominey
Keyword(s):  
Analogical Reasoning ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Common Representation ◽  
Mapping Process ◽  
The Common ◽  
Process Oriented

In Carruthers’ formulation, cross-domain thinking requires translation of domain specific data into a common format, and linguistic LF thus plays the role of the common medium of exchange. Alternatively, I propose a process-oriented characterization, in which there is no common representation and cross-domain thinking is rather the process of establishing mappings across domains, as in the process of analogical reasoning.

Challenges to Engineering Moral Reasoners

2017 ◽  
Author(s):  
Michał Klincewicz
Keyword(s):  
Artificial Intelligence ◽  
Analogical Reasoning ◽  
Knowledge Engineering ◽  
Ethical Theories ◽  
Philosophical Literature ◽  
Engineering Software ◽  
Promising Strategy ◽  
Symbolic Reasoning ◽  
Reasoning Systems ◽  
Moral Theories

A combination of algorithms, based on philosophical moral theories and analogical reasoning from standard cases, is a promising strategy for engineering software that can engage in moral reasoning. This chapter considers how such an architecture could be built using contemporary engineering techniques, such as knowledge engineering and symbolic reasoning systems. However, consideration of the philosophical literature on ethical theories generates engineering challenges that have to be overcome to make a computer moral reasoner viable. These difficulties include the context sensitivity of the system and temporal limitations on search—problems specific to artificial intelligence—but also difficulties that are direct consequences of particular philosophical theories. Cooperation between engineers and philosophers may be the best way to deal with those difficulties.

A computational model of analogical reasoning

1997 ◽  
Vol 40 (2) ◽  
pp. 214-224
Author(s):  
Bo Li ◽  
Qinping Zhao
Keyword(s):  
Computational Model ◽  
Analogical Reasoning

scholarly journals Similarity-Based Reasoning, Raven's Matrices, and General Intelligence

2018 ◽  
Author(s):  
Can Serif Mekik ◽  
Ron Sun ◽  
David Yun Dai
Keyword(s):  
Analogical Reasoning ◽  
Human Subjects ◽  
Visual Stimuli ◽  
Human Intelligence ◽  
General Intelligence ◽  
Structure Mapping ◽  
Computational System ◽  
Reasoning Systems ◽  
Feature Based ◽  
Raven’S Matrices

This paper presents a model tackling a variant of the Raven's Matrices family of human intelligence tests along with computational experiments. Raven's Matrices are thought to challenge human subjects' ability to generalize knowledge and deal with novel situations. We investigate how a generic ability to quickly and accurately generalize knowledge can be succinctly captured by a computational system. This work is distinct from other prominent attempts to deal with the task in terms of adopting a generalized similarity-based approach. Raven's Matrices appear to primarily require similarity-based or analogical reasoning over a set of varied visual stimuli. The similarity-based approach eliminates the need for structure mapping as emphasized in many existing analogical reasoning systems. Instead, it relies on feature-based processing with both relational and non-relational features. Preliminary experimental results suggest that our approach performs comparably to existing symbolic analogy-based models.

Synaptic organization of the gustatory NST

1994 ◽  
Vol 52 ◽  
pp. 150-151
Author(s):  
M. C. Whitehead
Keyword(s):  
Central Nervous System ◽  
Dendritic Spines ◽  
Fundamental Problem ◽  
Cell Types ◽  
Brain Regions ◽  
Excitatory Synapses ◽  
Solitary Tract ◽  
Synaptic Organization ◽  
Synaptic Junctions ◽  
Afferent Terminals

A fundamental problem in taste research is to determine how gustatory signals are processed and disseminated in the mammalian central nervous system. An important first step toward understanding information processing is the identification of cell types in the nucleus of the solitary tract (NST) and their synaptic relationships with oral primary afferent terminals. Facial and glossopharyngeal (LIX) terminals in the hamster were labelled with HRP, examined with EM, and characterized as containing moderate concentrations of medium-sized round vesicles, and engaging in asymmetrical synaptic junctions. Ultrastructurally the endings resemble excitatory synapses in other brain regions.Labelled facial afferent endings in the RC subdivision synapse almost exclusively with distal dendrites and dendritic spines of NST cells. Most synaptic relationships between the facial synapses and the dendrites are simple. However, 40% of facial endings engage in complex synaptic relationships within glomeruli containing unlabelled axon endings particularly ones termed "SP" endings. SP endings are densely packed with small, pleomorphic vesicles and synapse with both the facial endings and their postsynaptic dendrites by means of nearly symmetrical junctions.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

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